diff --git a/Train_ALIGNNFF_Mlearn.ipynb b/Train_ALIGNNFF_Mlearn.ipynb deleted file mode 100644 index 08b701b..0000000 --- a/Train_ALIGNNFF_Mlearn.ipynb +++ /dev/null @@ -1,857 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4", - "authorship_tag": "ABX9TyNqFkrt3WAqBQtNohKwfaAv", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "m-7Dw7PbnND4", - "outputId": "95bfdfa4-e681-4df1-c1be-fedbd1f53561" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in links: https://data.dgl.ai/wheels/cu118/repo.html\n", - 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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl>=0.6.0->alignn) (3.4)\n", - "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8->alignn) (2.1.3)\n", - "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8->alignn) (1.3.0)\n", - "Installing collected packages: xmltodict, spglib, pyparsing, pyflakes, pydocstyle, pydantic, mccabe, flake8, jarvis-tools, ase, pytorch-ignite, alignn\n", - " Attempting uninstall: pyparsing\n", - " Found existing installation: pyparsing 3.1.0\n", - " Uninstalling pyparsing-3.1.0:\n", - " Successfully uninstalled pyparsing-3.1.0\n", - " Attempting uninstall: pydantic\n", - " Found existing installation: pydantic 1.10.9\n", - " Uninstalling pydantic-1.10.9:\n", - " Successfully uninstalled pydantic-1.10.9\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "confection 0.0.4 requires pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4, but you have pydantic 1.8.1 which is incompatible.\n", - "dglgo 0.0.2 requires pydantic>=1.9.0, but you have pydantic 1.8.1 which is incompatible.\n", - "inflect 6.0.4 requires pydantic>=1.9.1, but you have pydantic 1.8.1 which is incompatible.\n", - "spacy 3.5.3 requires pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4, but you have pydantic 1.8.1 which is incompatible.\n", - "thinc 8.1.10 requires pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4, but you have pydantic 1.8.1 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed alignn-2023.6.20 ase-3.22.1 flake8-6.0.0 jarvis-tools-2023.5.26 mccabe-0.7.0 pydantic-1.8.1 pydocstyle-6.3.0 pyflakes-3.0.1 pyparsing-2.4.7 pytorch-ignite-0.5.0.dev20230703 spglib-2.0.2 xmltodict-0.13.0\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.colab-display-data+json": { - "pip_warning": { - "packages": [ - "pyparsing" - ] - } - } - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "CPU times: user 569 ms, sys: 75.2 ms, total: 644 ms\n", - "Wall time: 1min 8s\n" - ] - } - ], - "source": [ - "%%time\n", - "!pip install dgl -f https://data.dgl.ai/wheels/cu118/repo.html\n", - "!pip install dglgo -f https://data.dgl.ai/wheels-test/repo.html\n", - "!pip install alignn" - ] - }, - { - "cell_type": "code", - "source": [ - "import os\n", - "!pwd\n", - "os.chdir('/content')\n", - "# Clone ALIGNN repo to get example folder\n", - "if not os.path.exists('alignn'):\n", - " !git clone https://github.com/usnistgov/alignn.git\n", - "\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "hFZn-PrinTX0", - "outputId": "62396fcd-c367-4c37-b57e-a1a53666e94c" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content\n", - "Cloning into 'alignn'...\n", - "remote: Enumerating objects: 3776, done.\u001b[K\n", - "remote: Counting objects: 100% (1370/1370), done.\u001b[K\n", - "remote: Compressing objects: 100% (440/440), done.\u001b[K\n", - "remote: Total 3776 (delta 1009), reused 1116 (delta 885), pack-reused 2406\u001b[K\n", - "Receiving objects: 100% (3776/3776), 75.52 MiB | 20.50 MiB/s, done.\n", - "Resolving deltas: 100% (2185/2185), done.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "#Just to test basic installation on gpu went right\n", - "import time\n", - "t1=time.time()\n", - "!train_folder.py --root_dir \"alignn/alignn/examples/sample_data\" --epochs 3 --batch_size 2 --config \"alignn/alignn/examples/sample_data/config_example.json\" --output_dir=temp\n", - "t2=time.time()\n", - "print ('Time in s',t2-t1)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IvJIYDhOnh2v", - "outputId": "18213557-b3b4-41e7-ebeb-7874b41d7117" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "DGL backend not selected or invalid. Assuming PyTorch for now.\n", - "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable. Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n", - "fatal: not a git repository (or any of the parent directories): .git\n", - "MAX val: 6.149\n", - "MIN val: 0.0\n", - "MAD: 1.0520696\n", - "Baseline MAE: 2.3805500000000004\n", - "data range 4.907 0.0\n", - "100% 40/40 [00:02<00:00, 16.94it/s]\n", - "df atoms ... target\n", - "0 {'lattice_mat': [[3.566933224304235, 0.0, -0.0... ... 0.000\n", - "1 {'lattice_mat': [[4.089078911208881, 0.0, 0.0]... ... 0.000\n", - "2 {'lattice_mat': [[-1.833590720595598, 1.833590... ... 0.000\n", - "3 {'lattice_mat': [[7.2963518353359165, 0.0, 0.0... ... 0.472\n", - "4 {'lattice_mat': [[1.6777483798834445, -2.90594... ... 0.000\n", - "5 {'lattice_mat': [[4.157436115454804, -0.0, 0.0... ... 0.000\n", - "6 {'lattice_mat': [[0.0, 5.1858714074842, 5.1858... ... 0.000\n", - "7 {'lattice_mat': [[3.790914410660539, -0.0, 0.0... ... 0.000\n", - "8 {'lattice_mat': [[4.284492173131309, 1.636192e... ... 0.000\n", - "9 {'lattice_mat': [[3.2250494729190726, 2.216578... ... 0.689\n", - "10 {'lattice_mat': [[5.587070827330502, -0.006443... ... 1.517\n", - "11 {'lattice_mat': [[4.927781968323723, -0.0, 0.0... ... 0.000\n", - "12 {'lattice_mat': [[10.725911963093319, 1.159968... ... 0.000\n", - "13 {'lattice_mat': [[5.140164879556414, 0.3718366... ... 0.000\n", - "14 {'lattice_mat': [[5.194393535053021, 0.0345773... ... 0.000\n", - "15 {'lattice_mat': [[4.927229198330356, -0.0, -0.... ... 2.122\n", - "16 {'lattice_mat': [[4.839493559425439, 9.7116505... ... 0.000\n", - "17 {'lattice_mat': [[-2.2512310528422197, 1.49649... ... 0.000\n", - "18 {'lattice_mat': [[7.709535704177289, 2.46207e-... ... 0.000\n", - "19 {'lattice_mat': [[3.292134155794691, 0.0, 0.0]... ... 0.502\n", - "20 {'lattice_mat': [[9.067075684180468, -0.0, 0.0... ... 1.197\n", - "21 {'lattice_mat': [[6.850665464204784, -0.0, 0.0... ... 0.560\n", - "22 {'lattice_mat': [[3.3542337275744103, 0.0, 0.0... ... 0.051\n", - "23 {'lattice_mat': [[4.509029640475962, 0.0564034... ... 4.907\n", - "24 {'lattice_mat': [[3.5058938597621094, -3.08124... ... 1.681\n", - "25 {'lattice_mat': [[4.191262576674699, 0.0, -0.0... ... 0.016\n", - "26 {'lattice_mat': [[0.0, -3.9587610833154616, 0.... ... 0.658\n", - "27 {'lattice_mat': [[4.084155317570781, -1.066825... ... 0.000\n", - "28 {'lattice_mat': [[9.407270982425844, 0.0171637... ... 2.472\n", - "29 {'lattice_mat': [[3.93712543178282, 0.0, 2.273... ... 3.851\n", - "30 {'lattice_mat': [[-0.0127275386492899, 4.47534... ... 0.482\n", - "31 {'lattice_mat': [[3.5666343258756448, 0.0, 0.0... ... 0.000\n", - "32 {'lattice_mat': [[7.843871888963013, 0.0, 0.0]... ... 0.924\n", - "33 {'lattice_mat': [[5.157077730332642, 0.0020004... ... 4.030\n", - "34 {'lattice_mat': [[3.8114364321417686, 0.0, 0.0... ... 0.000\n", - "35 {'lattice_mat': [[5.464512229851642, 0.0, -2.0... ... 0.239\n", - "36 {'lattice_mat': [[0.0, 4.936437902689708, 4.93... ... 0.000\n", - "37 {'lattice_mat': [[0.0, 4.893247728183244, 4.89... ... 0.000\n", - "38 {'lattice_mat': [[0.0, 4.901572410735, 4.90157... ... 0.000\n", - "39 {'lattice_mat': [[0.0, 5.104615296684174, 5.10... ... 0.000\n", - "\n", - "[40 rows x 3 columns]\n", - "warning: could not load CGCNN features for 103\n", - "Setting it to max atomic number available here, 103\n", - "warning: could not load CGCNN features for 101\n", - "Setting it to max atomic number available here, 103\n", - "warning: could not load CGCNN features for 102\n", - "Setting it to max atomic number available here, 103\n", - "building line graphs\n", - "100% 40/40 [00:00<00:00, 636.33it/s]\n", - "data range 1.569 0.0\n", - "100% 5/5 [00:00<00:00, 41.33it/s]\n", - "df atoms ... target\n", - "0 {'lattice_mat': [[-0.0, 5.040771484524319, 5.0... ... 0.000\n", - "1 {'lattice_mat': [[4.376835486482439, 0.0086562... ... 0.000\n", - "2 {'lattice_mat': [[-0.0, 5.037541505850243, 5.0... ... 0.000\n", - "3 {'lattice_mat': [[10.37325585559557, -2.271858... ... 1.569\n", - "4 {'lattice_mat': [[0.0, 5.129874508851702, 5.12... ... 0.000\n", - "\n", - "[5 rows x 3 columns]\n", - "building line graphs\n", - "100% 5/5 [00:00<00:00, 979.70it/s]\n", - "data range 6.149 0.0\n", - "100% 5/5 [00:00<00:00, 34.36it/s]\n", - "df atoms ... target\n", - "0 {'lattice_mat': [[1.6712283e-08, -2.5080296697... ... 6.149\n", - "1 {'lattice_mat': [[6.603532697435508, 0.0, -0.0... ... 4.072\n", - "2 {'lattice_mat': [[-0.0, 4.517300851474054, 4.5... ... 0.000\n", - "3 {'lattice_mat': [[-0.0, 4.326757913323647, 4.3... ... 0.000\n", - "4 {'lattice_mat': [[6.9098665629767275, 0.128626... ... 2.341\n", - "\n", - "[5 rows x 3 columns]\n", - "building line graphs\n", - "100% 5/5 [00:00<00:00, 817.00it/s]\n", - "n_train: 40\n", - "n_val: 5\n", - "n_test: 5\n", - "version='112bbedebdaecf59fb18e11c929080fb2f358246' dataset='user_data' target='target' atom_features='cgcnn' neighbor_strategy='k-nearest' id_tag='jid' random_seed=123 classification_threshold=None n_val=None n_test=None n_train=None train_ratio=0.8 val_ratio=0.1 test_ratio=0.1 target_multiplication_factor=None epochs=3 batch_size=2 weight_decay=1e-05 learning_rate=0.001 filename='sample' warmup_steps=2000 criterion='mse' optimizer='adamw' scheduler='onecycle' pin_memory=False save_dataloader=False write_checkpoint=True write_predictions=True store_outputs=True progress=True log_tensorboard=False standard_scalar_and_pca=False use_canonize=True num_workers=0 cutoff=8.0 max_neighbors=12 keep_data_order=True normalize_graph_level_loss=False distributed=False n_early_stopping=None output_dir='temp' model=ALIGNNConfig(name='alignn', alignn_layers=4, gcn_layers=4, atom_input_features=92, edge_input_features=80, triplet_input_features=40, embedding_features=64, hidden_features=256, output_features=1, link='identity', zero_inflated=False, classification=False, num_classes=2)\n", - "config:\n", - "{'atom_features': 'cgcnn',\n", - " 'batch_size': 2,\n", - " 'classification_threshold': None,\n", - " 'criterion': 'mse',\n", - " 'cutoff': 8.0,\n", - " 'dataset': 'user_data',\n", - " 'distributed': False,\n", - " 'epochs': 3,\n", - " 'filename': 'sample',\n", - " 'id_tag': 'jid',\n", - " 'keep_data_order': True,\n", - " 'learning_rate': 0.001,\n", - " 'log_tensorboard': False,\n", - " 'max_neighbors': 12,\n", - " 'model': {'alignn_layers': 4,\n", - " 'atom_input_features': 92,\n", - " 'classification': False,\n", - " 'edge_input_features': 80,\n", - " 'embedding_features': 64,\n", - " 'gcn_layers': 4,\n", - " 'hidden_features': 256,\n", - " 'link': 'identity',\n", - " 'name': 'alignn',\n", - " 'num_classes': 2,\n", - " 'output_features': 1,\n", - " 'triplet_input_features': 40,\n", - " 'zero_inflated': False},\n", - " 'n_early_stopping': None,\n", - " 'n_test': None,\n", - " 'n_train': None,\n", - " 'n_val': None,\n", - " 'neighbor_strategy': 'k-nearest',\n", - " 'normalize_graph_level_loss': False,\n", - " 'num_workers': 0,\n", - " 'optimizer': 'adamw',\n", - " 'output_dir': 'temp',\n", - " 'pin_memory': False,\n", - " 'progress': True,\n", - " 'random_seed': 123,\n", - " 'save_dataloader': False,\n", - " 'scheduler': 'onecycle',\n", - " 'standard_scalar_and_pca': False,\n", - " 'store_outputs': True,\n", - " 'target': 'target',\n", - " 'target_multiplication_factor': None,\n", - " 'test_ratio': 0.1,\n", - " 'train_ratio': 0.8,\n", - " 'use_canonize': True,\n", - " 'val_ratio': 0.1,\n", - " 'version': '112bbedebdaecf59fb18e11c929080fb2f358246',\n", - " 'warmup_steps': 2000,\n", - " 'weight_decay': 1e-05,\n", - " 'write_checkpoint': True,\n", - " 'write_predictions': True}\n", - "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py:114: UserWarning: Deterministic behavior was enabled with either `torch.use_deterministic_algorithms(True)` or `at::Context::setDeterministicAlgorithms(true)`, but this operation is not deterministic because it uses CuBLAS and you have CUDA >= 10.2. To enable deterministic behavior in this case, you must set an environment variable before running your PyTorch application: CUBLAS_WORKSPACE_CONFIG=:4096:8 or CUBLAS_WORKSPACE_CONFIG=:16:8. For more information, go to https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility (Triggered internally at ../aten/src/ATen/Context.cpp:156.)\n", - " return F.linear(input, self.weight, self.bias)\n", - "/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py:200: UserWarning: Deterministic behavior was enabled with either `torch.use_deterministic_algorithms(True)` or `at::Context::setDeterministicAlgorithms(true)`, but this operation is not deterministic because it uses CuBLAS and you have CUDA >= 10.2. To enable deterministic behavior in this case, you must set an environment variable before running your PyTorch application: CUBLAS_WORKSPACE_CONFIG=:4096:8 or CUBLAS_WORKSPACE_CONFIG=:16:8. For more information, go to https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility (Triggered internally at ../aten/src/ATen/Context.cpp:156.)\n", - " Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", - "Val_MAE: 1.0282\n", - "Train_MAE: 0.8347\n", - "Val_MAE: 0.4758\n", - "Train_MAE: 1.8375\n", - "Val_MAE: 0.9286\n", - "Train_MAE: 1.6457\n", - "Test MAE: 2.507803225517273\n", - "Time taken (s): 14.817846298217773\n", - "Time in s 28.74060034751892\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!wget https://gist.githubusercontent.com/knc6/eb04b911cd5428bb2ac79b7622c0da26/raw/ffdcbbccc9488d536890a3a5ffd69313a2a458bd/config_mlearn_cu.json" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ylnu0xCjoWjR", - "outputId": "fe9bd3ec-d2d3-4fab-88eb-5d978fc7a096" - }, - "execution_count": 17, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2023-07-03 17:09:15-- https://gist.githubusercontent.com/knc6/eb04b911cd5428bb2ac79b7622c0da26/raw/ffdcbbccc9488d536890a3a5ffd69313a2a458bd/config_mlearn_cu.json\n", - "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.111.133, ...\n", - "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|185.199.109.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 2034 (2.0K) [text/plain]\n", - "Saving to: ‘config_mlearn_cu.json’\n", - "\n", - "\rconfig_mlearn_cu.js 0%[ ] 0 --.-KB/s \rconfig_mlearn_cu.js 100%[===================>] 1.99K --.-KB/s in 0s \n", - "\n", - "2023-07-03 17:09:16 (36.1 MB/s) - ‘config_mlearn_cu.json’ saved [2034/2034]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!ls alignn/alignn/examples/sample_data_ff/mlearn_data/all/" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "YWcGU66vn4J2", - "outputId": "b9884322-6fe2-4413-bfd2-96bc102529a5" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "config_example.json id_prop.json prepare_mlearn.py\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from jarvis.db.jsonutils import loadjson,dumpjson\n", - "#lower batch size, samples, alignn and gcn layers etc. to fit in colab\n", - "d=loadjson('config_mlearn_cu.json')\n", - "d['batch_size']=2\n", - "# d['n_train']=100\n", - "# d['n_val']=5\n", - "# d['n_test']=5\n", - "dumpjson(data=d,filename='config_mlearn_cu_less.json')" - ], - "metadata": { - "id": "1yQ5rv5rsvdC" - }, - "execution_count": 34, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - " !train_folder_ff.py --root_dir \"alignn/alignn/examples/sample_data_ff/mlearn_data/Cu/\" --config \"config_mlearn_cu_less.json\" --output_dir=\"OutCu\"" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "o8tG1cAusthS", - "outputId": "3600074c-fb41-4664-d241-187ea60f70f0" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "fatal: not a git repository (or any of the parent directories): .git\n", - "len dataset 324\n", - "MAX val: -3.5154572437962965\n", - "MIN val: -4.101246713888889\n", - "MAD: 0.11948171020654628\n", - "Baseline MAE: 0.13718126977887754\n", - "data range -3.5154572437962965 -4.101246713888889\n", - "100% 262/262 [00:43<00:00, 6.06it/s]\n", - "df target ... jid\n", - "0 -3.981879 ... 14\n", - "1 -3.982149 ... 10\n", - "2 -4.088199 ... 257\n", - "3 -3.977330 ... 13\n", - "4 -4.080317 ... 317\n", - ".. ... ... ...\n", - "257 -4.072867 ... 234\n", - "258 -3.896843 ... 38\n", - "259 -4.000258 ... 201\n", - "260 -3.555814 ... 309\n", - "261 -4.073291 ... 172\n", - "\n", - "[262 rows x 4 columns]\n", - "warning: could not load CGCNN features for 103\n", - "Setting it to max atomic number available here, 103\n", - "warning: could not load CGCNN features for 101\n", - "Setting it to max atomic number available here, 103\n", - "warning: could not load CGCNN features for 102\n", - "Setting it to max atomic number available here, 103\n", - "building line graphs\n", - "100% 262/262 [00:02<00:00, 112.28it/s]\n", - "data range -3.5660914372222225 -4.100467069907408\n", - "100% 31/31 [00:04<00:00, 7.20it/s]\n", - "df target ... jid\n", - "0 -4.036756 ... 88\n", - "1 -4.070120 ... 135\n", - "2 -4.034540 ... 250\n", - "3 -3.895673 ... 43\n", - "4 -4.063742 ... 93\n", - "5 -3.986996 ... 17\n", - "6 -4.100467 ... 262\n", - "7 -4.072812 ... 192\n", - "8 -4.094845 ... 215\n", - "9 -3.950117 ... 267\n", - "10 -4.018524 ... 249\n", - "11 -4.100040 ... 223\n", - "12 -3.984551 ... 263\n", - "13 -4.067251 ... 204\n", - "14 -3.979483 ... 7\n", - "15 -3.950117 ... 298\n", - "16 -3.566091 ... 270\n", - "17 -3.961314 ... 106\n", - "18 -4.099057 ... 248\n", - "19 -3.938866 ... 268\n", - "20 -4.018524 ... 176\n", - "21 -4.083955 ... 157\n", - "22 -3.979699 ... 19\n", - "23 -4.080512 ... 241\n", - "24 -3.956893 ... 136\n", - "25 -4.092080 ... 245\n", - "26 -4.099172 ... 221\n", - "27 -3.953565 ... 150\n", - "28 -3.980227 ... 11\n", - "29 -3.964034 ... 65\n", - "30 -3.962107 ... 73\n", - "\n", - "[31 rows x 4 columns]\n", - "building line graphs\n", - "100% 31/31 [00:00<00:00, 71.82it/s]\n", - "data range -3.565395898333333 -4.100757043611111\n", - "100% 31/31 [00:05<00:00, 5.68it/s]\n", - "df target ... jid\n", - "0 -3.952025 ... 48\n", - "1 -3.979364 ... 23\n", - "2 -4.054687 ... 53\n", - "3 -4.073379 ... 230\n", - "4 -4.097994 ... 162\n", - "5 -3.971151 ... 4\n", - "6 -3.989234 ... 36\n", - "7 -4.100757 ... 194\n", - "8 -4.072867 ... 322\n", - "9 -4.067251 ... 224\n", - "10 -3.984111 ... 1\n", - "11 -3.958536 ... 84\n", - "12 -3.616537 ... 126\n", - "13 -4.081772 ... 314\n", - "14 -4.097994 ... 315\n", - "15 -4.096307 ... 173\n", - "16 -3.599006 ... 70\n", - "17 -3.571802 ... 82\n", - "18 -4.067251 ... 324\n", - "19 -4.099057 ... 175\n", - "20 -4.065376 ... 171\n", - "21 -4.056356 ... 288\n", - "22 -4.056721 ... 275\n", - "23 -4.099171 ... 195\n", - "24 -3.976079 ... 20\n", - "25 -3.570559 ... 56\n", - "26 -3.574628 ... 137\n", - "27 -4.073380 ... 209\n", - "28 -3.923674 ... 45\n", - "29 -3.565396 ... 138\n", - "30 -3.985642 ... 27\n", - "\n", - "[31 rows x 4 columns]\n", - "building line graphs\n", - "100% 31/31 [00:00<00:00, 115.73it/s]\n", - "n_train: 262\n", - "n_val: 31\n", - "n_test: 31\n", - "version='112bbedebdaecf59fb18e11c929080fb2f358246' dataset='user_data' target='target' atom_features='cgcnn' neighbor_strategy='k-nearest' id_tag='jid' random_seed=123 classification_threshold=None n_val=31 n_test=31 n_train=262 train_ratio=0.9 val_ratio=0.05 test_ratio=0.05 target_multiplication_factor=None epochs=100 batch_size=2 weight_decay=1e-05 learning_rate=0.001 filename='sample' warmup_steps=2000 criterion='l1' optimizer='adamw' scheduler='onecycle' pin_memory=False save_dataloader=False write_checkpoint=True write_predictions=True store_outputs=False progress=True log_tensorboard=False standard_scalar_and_pca=False use_canonize=False num_workers=0 cutoff=8.0 max_neighbors=12 keep_data_order=False normalize_graph_level_loss=False distributed=False n_early_stopping=None output_dir='OutCu' model=ALIGNNAtomWiseConfig(name='alignn_atomwise', alignn_layers=2, gcn_layers=4, atom_input_features=92, edge_input_features=80, triplet_input_features=40, embedding_features=64, hidden_features=300, output_features=1, grad_multiplier=-1, calculate_gradient=True, atomwise_output_features=0, graphwise_weight=0.8, gradwise_weight=0.2, stresswise_weight=0.0, atomwise_weight=0.0, link='identity', zero_inflated=False, classification=False, force_mult_natoms=True, energy_mult_natoms=False, include_pos_deriv=False, use_cutoff_function=False, inner_cutoff=6.0, stress_multiplier=1.0, add_reverse_forces=False)\n", - "config:\n", - "{'atom_features': 'cgcnn',\n", - " 'batch_size': 2,\n", - " 'classification_threshold': None,\n", - " 'criterion': 'l1',\n", - " 'cutoff': 8.0,\n", - " 'dataset': 'user_data',\n", - " 'distributed': False,\n", - " 'epochs': 100,\n", - " 'filename': 'sample',\n", - " 'id_tag': 'jid',\n", - " 'keep_data_order': False,\n", - " 'learning_rate': 0.001,\n", - " 'log_tensorboard': False,\n", - " 'max_neighbors': 12,\n", - " 'model': {'add_reverse_forces': False,\n", - " 'alignn_layers': 2,\n", - " 'atom_input_features': 92,\n", - " 'atomwise_output_features': 0,\n", - " 'atomwise_weight': 0.0,\n", - " 'calculate_gradient': True,\n", - " 'classification': False,\n", - " 'edge_input_features': 80,\n", - " 'embedding_features': 64,\n", - " 'energy_mult_natoms': False,\n", - " 'force_mult_natoms': True,\n", - " 'gcn_layers': 4,\n", - " 'grad_multiplier': -1,\n", - " 'gradwise_weight': 0.2,\n", - " 'graphwise_weight': 0.8,\n", - " 'hidden_features': 300,\n", - " 'include_pos_deriv': False,\n", - " 'inner_cutoff': 6.0,\n", - " 'link': 'identity',\n", - " 'name': 'alignn_atomwise',\n", - " 'output_features': 1,\n", - " 'stress_multiplier': 1.0,\n", - " 'stresswise_weight': 0.0,\n", - " 'triplet_input_features': 40,\n", - " 'use_cutoff_function': False,\n", - " 'zero_inflated': False},\n", - " 'n_early_stopping': None,\n", - " 'n_test': 31,\n", - " 'n_train': 262,\n", - " 'n_val': 31,\n", - " 'neighbor_strategy': 'k-nearest',\n", - " 'normalize_graph_level_loss': False,\n", - " 'num_workers': 0,\n", - " 'optimizer': 'adamw',\n", - " 'output_dir': 'OutCu',\n", - " 'pin_memory': False,\n", - " 'progress': True,\n", - " 'random_seed': 123,\n", - " 'save_dataloader': False,\n", - " 'scheduler': 'onecycle',\n", - " 'standard_scalar_and_pca': False,\n", - " 'store_outputs': False,\n", - " 'target': 'target',\n", - " 'target_multiplication_factor': None,\n", - " 'test_ratio': 0.05,\n", - " 'train_ratio': 0.9,\n", - " 'use_canonize': False,\n", - " 'val_ratio': 0.05,\n", - " 'version': '112bbedebdaecf59fb18e11c929080fb2f358246',\n", - " 'warmup_steps': 2000,\n", - " 'weight_decay': 1e-05,\n", - " 'write_checkpoint': True,\n", - " 'write_predictions': True}\n", - "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", - " warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n", - "TrainLoss Epoch 0 total 65.7026293380186 out 0.5222341728801946 atom nan grad 0.42393880712419096 stress nan\n", - "Saving data for epoch: 0\n", - "ValLoss Epoch 0 total 3.5256495475769043 out 0.22399423122406006 atom nan grad 0.2953340650623654 stress nan\n", - "TrainLoss Epoch 1 total 23.655563490465283 out 0.15444544708455793 atom nan grad 0.2852870073799714 stress nan\n", - "ValLoss Epoch 1 total 3.596076086163521 out 0.2506690740585327 atom nan grad 0.2044297159521289 stress nan\n", - "TrainLoss Epoch 2 total 19.99852642789483 out 0.139688408101788 atom nan grad 0.20480433278511542 stress nan\n", - "ValLoss Epoch 2 total 4.836315810680389 out 0.37697776953379314 atom nan grad 0.10681174003440622 stress nan\n", - "TrainLoss Epoch 3 total 19.925315987318754 out 0.14120348992238518 atom nan grad 0.1902330254611111 stress nan\n", - "Saving data for epoch: 3\n", - "ValLoss Epoch 3 total 3.193852737545967 out 0.2483779509862264 atom nan grad 0.07050999214735121 stress nan\n", - "TrainLoss Epoch 4 total 19.4559537852183 out 0.14641990006424999 atom nan grad 0.1517692724090012 stress nan\n", - "Saving data for epoch: 4\n", - "ValLoss Epoch 4 total 0.9328981172293425 out 0.051348876953125 atom nan grad 0.1070026844730713 stress nan\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "\n", - "# !train_folder_ff.py --root_dir \"alignn/alignn/examples/sample_data_ff/mlearn_data/all/\" --config \"config.json\" --output_dir=\"OutAll\"" - ], - "metadata": { - "id": "Owq57SLTniQr" - }, - "execution_count": 12, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "!nvcc --version" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bH2B3DFyn7w3", - "outputId": "9211c522-31a2-4353-facc-eb2c02535493" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "nvcc: NVIDIA (R) Cuda compiler driver\n", - "Copyright (c) 2005-2022 NVIDIA Corporation\n", - "Built on Wed_Sep_21_10:33:58_PDT_2022\n", - "Cuda compilation tools, release 11.8, V11.8.89\n", - "Build cuda_11.8.r11.8/compiler.31833905_0\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip freeze" - ], - "metadata": { - "id": "7aPp-8e4pQ5E" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "bnsmfvOepjVl" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "iJS2zJK8p1Um" - }, - "execution_count": 9, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/jarvis-tools-notebooks/Matgl_mlearn.ipynb b/jarvis-tools-notebooks/Matgl_mlearn.ipynb new file mode 100644 index 0000000..0f3ad48 --- /dev/null +++ b/jarvis-tools-notebooks/Matgl_mlearn.ipynb @@ -0,0 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"colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OHWiKysET91G", + "outputId": "630eba38-8436-4422-e2cb-414fae49de40" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m188.6/188.6 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.5/6.5 MB\u001b[0m \u001b[31m34.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m47.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires kaleido, which is not installed.\n", + "lida 0.0.10 requires python-multipart, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "if not os.path.exists('jarvis_leaderboard'):\n", + " !git clone https://github.com/usnistgov/jarvis_leaderboard.git\n", + "os.chdir('jarvis_leaderboard')\n", + "!pip install -e ." + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "j9bR4GzBVGBK", + "outputId": "db61f83c-aa20-45aa-d7f2-ffae881f421c" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'jarvis_leaderboard'...\n", + "remote: Enumerating objects: 57856, done.\u001b[K\n", + "remote: Counting 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requests>=2.23.0->jarvis-tools>=2021.07.19->jarvis-leaderboard==2023.12.16) (2023.11.17)\n", + "Building wheels for collected packages: paginate\n", + " Building wheel for paginate (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for paginate: filename=paginate-0.5.6-py3-none-any.whl size=12666 sha256=84c00cf577f54330b94b3afe8d729bc124f4afaef8147a5814b6e313039fd9e9\n", + " Stored in directory: /root/.cache/pip/wheels/47/d3/18/0b5bebc873f29bea61fedece1e92cbcbef416839dfe5bd0eef\n", + "Successfully built paginate\n", + "Installing collected packages: paginate, xmltodict, watchdog, typing-extensions, rouge, pyyaml-env-tag, pymdown-extensions, pathspec, mkdocs-material-extensions, mergedeep, colorama, annotated-types, pydantic-core, ghp-import, pydantic, mkdocs, mkdocs-material, jarvis-tools, jarvis-leaderboard\n", + " Attempting uninstall: typing-extensions\n", + " Found existing installation: typing_extensions 4.5.0\n", + " Uninstalling typing_extensions-4.5.0:\n", + " Successfully uninstalled typing_extensions-4.5.0\n", + " Attempting uninstall: pydantic\n", + " Found existing installation: pydantic 1.10.13\n", + " Uninstalling pydantic-1.10.13:\n", + " Successfully uninstalled pydantic-1.10.13\n", + " Running setup.py develop for jarvis-leaderboard\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires kaleido, which is not installed.\n", + "lida 0.0.10 requires python-multipart, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\n", + "llmx 0.0.15a0 requires cohere, which is not installed.\n", + "llmx 0.0.15a0 requires openai, which is not installed.\n", + "llmx 0.0.15a0 requires tiktoken, which is not installed.\n", + "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed annotated-types-0.6.0 colorama-0.4.6 ghp-import-2.1.0 jarvis-leaderboard-2023.12.16 jarvis-tools-2023.12.12 mergedeep-1.3.4 mkdocs-1.5.3 mkdocs-material-9.5.3 mkdocs-material-extensions-1.3.1 paginate-0.5.6 pathspec-0.12.1 pydantic-2.5.3 pydantic-core-2.14.6 pymdown-extensions-10.7 pyyaml-env-tag-0.1 rouge-1.0.1 typing-extensions-4.9.0 watchdog-3.0.0 xmltodict-0.13.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install numpy==1.23.5\n", + "# then restart session" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "P1ao42dQYC2T", + "outputId": "507d6e74-a30a-47f4-a18c-5a8153e1d66f" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting numpy==1.23.5\n", + " Downloading numpy-1.23.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.1/17.1 MB\u001b[0m \u001b[31m56.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: numpy\n", + " Attempting uninstall: numpy\n", + " Found existing installation: numpy 1.26.3\n", + " Uninstalling numpy-1.26.3:\n", + " Successfully uninstalled numpy-1.26.3\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires kaleido, which is not installed.\n", + "lida 0.0.10 requires python-multipart, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\n", + "pymatgen 2023.12.18 requires numpy>=1.25.0, but you have numpy 1.23.5 which is incompatible.\n", + "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed numpy-1.23.5\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "numpy" + ] + } + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "!wget https://figshare.com/ndownloader/files/40357663 -O mlearn.json.zip" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jNlPraDSWvQ6", + "outputId": "4185c160-05fe-4bab-e4da-6d6ded125a19" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-01-08 08:30:09-- https://figshare.com/ndownloader/files/40357663\n", + "Resolving figshare.com (figshare.com)... 54.155.30.132, 34.241.157.25, 2a05:d018:1f4:d003:b62a:af42:316b:d9dd, ...\n", + "Connecting to figshare.com (figshare.com)|54.155.30.132|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240108/eu-west-1/s3/aws4_request&X-Amz-Date=20240108T083009Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=fc018c2981df1812989d7fb35059e81c20c1e6b312ba3fd861b236cedac47ce7 [following]\n", + "--2024-01-08 08:30:09-- https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240108/eu-west-1/s3/aws4_request&X-Amz-Date=20240108T083009Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=fc018c2981df1812989d7fb35059e81c20c1e6b312ba3fd861b236cedac47ce7\n", + "Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.101.107, 52.218.89.155, 52.218.24.10, ...\n", + "Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.101.107|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 2542319 (2.4M) [application/zip]\n", + "Saving to: ‘mlearn.json.zip’\n", + "\n", + "mlearn.json.zip 100%[===================>] 2.42M --.-KB/s in 0.1s \n", + "\n", + "2024-01-08 08:30:10 (18.4 MB/s) - ‘mlearn.json.zip’ saved [2542319/2542319]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import json,zipfile\n", + "mlearn = json.loads(\n", + " zipfile.ZipFile(\"mlearn.json.zip\").read(\n", + " \"mlearn.json\"\n", + " )\n", + " )" + ], + "metadata": { + "id": "GMV9UlXzWFbo" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "import os\n", + "from jarvis.core.atoms import Atoms\n", + "import os\n", + "import shutil\n", + "import warnings\n", + "\n", + "import numpy as np\n", + "import pytorch_lightning as pl\n", + "from dgl.data.utils import split_dataset\n", + "#from mp_api.client import MPRester\n", + "from pytorch_lightning.loggers import CSVLogger\n", + "import numpy as np\n", + "import torch\n", + "from matgl.apps.pes import Potential\n", + "import zipfile,json, glob\n", + "import pandas as pd\n", + "import matgl\n", + "from matgl.ext.pymatgen import Structure2Graph, get_element_list\n", + "from matgl.graph.data import M3GNetDataset, MGLDataLoader, collate_fn_efs\n", + "from matgl.models import M3GNet\n", + "from matgl.utils.training import PotentialLightningModule\n", + "\n", + "# To suppress warnings for clearer output\n", + "warnings.simplefilter(\"ignore\")\n", + "\n", + "os.chdir('/content')\n", + "elements = ['Si']\n", + "max_epochs=100\n", + "models={}\n", + "for element in elements:\n", + "\n", + " benchmark_energies = (\n", + " \"jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/mlearn_\"\n", + " + element\n", + " + \"_energy.json.zip\"\n", + " )\n", + " temp_energies = benchmark_energies.split(\"/\")[-1].split(\".zip\")[0]\n", + " energies = json.loads(\n", + " zipfile.ZipFile(benchmark_energies).read(temp_energies)\n", + " )\n", + " train_ids = list(energies[\"train\"].keys())\n", + " test_ids = list(energies[\"test\"].keys())\n", + "\n", + " train_energies=[]\n", + " train_forces=[]\n", + " train_stresses=[]\n", + " train_structures=[]\n", + " for i in mlearn:\n", + " if i[\"jid\"] in train_ids:\n", + " # print(i)\n", + " train_energies.append(i[\"energy\"])\n", + " train_forces.append(i[\"forces\"])\n", + " train_stresses.append(i['stresses'])\n", + " atoms = Atoms.from_dict(i[\"atoms\"])\n", + " train_structures.append(atoms.pymatgen_converter())\n", + "\n", + "\n", + " labels = {\n", + " \"energies\": train_energies,\n", + " \"forces\": train_forces,\n", + " \"stresses\": train_stresses,\n", + " }\n", + "\n", + "\n", + "\n", + " element_types = get_element_list(train_structures)\n", + " converter = Structure2Graph(element_types=element_types, cutoff=5.0)\n", + " train_data = M3GNetDataset(\n", + " threebody_cutoff=4.0,\n", + " structures=train_structures,\n", + " converter=converter,\n", + " labels=labels,\n", + " )\n", + "\n", + "\n", + " test_energies=[]\n", + " test_forces=[]\n", + " test_stresses=[]\n", + " test_structures=[]\n", + " tids = []\n", + " for i in mlearn:\n", + " if i[\"jid\"] in test_ids:\n", + " # print(i)\n", + " test_energies.append(i[\"energy\"])\n", + " test_forces.append(i[\"forces\"])\n", + " test_stresses.append(i['stresses'])\n", + " atoms = Atoms.from_dict(i[\"atoms\"])\n", + " test_structures.append(atoms.pymatgen_converter())\n", + " tids.append(i['jid'])\n", + "\n", + " labels = {\n", + " \"energies\": test_energies,\n", + " \"forces\": test_forces,\n", + " \"stresses\": test_stresses,\n", + " }\n", + "\n", + "\n", + "\n", + " element_types = get_element_list(test_structures)\n", + " converter = Structure2Graph(element_types=element_types, cutoff=5.0)\n", + " test_data = M3GNetDataset(\n", + " threebody_cutoff=4.0,\n", + " structures=test_structures,\n", + " converter=converter,\n", + " labels=labels,\n", + " )\n", + "\n", + "\n", + " train_loader, val_loader, test_loader = MGLDataLoader(\n", + " train_data=train_data,\n", + " val_data=test_data,\n", + " test_data=test_data,\n", + " collate_fn=collate_fn_efs,\n", + " batch_size=2,\n", + " num_workers=1,\n", + " )\n", + " model = M3GNet(\n", + " element_types=element_types,\n", + " is_intensive=False,\n", + " )\n", + " lit_module = PotentialLightningModule(model=model)\n", + "\n", + "\n", + "\n", + " logger = CSVLogger(\"logs\", name=\"M3GNet_training\")\n", + " # Inference mode = False is required for calculating forces, stress in test mode and prediction mode\n", + " trainer = pl.Trainer(max_epochs=max_epochs, accelerator=\"cpu\", logger=logger, inference_mode=False)\n", + " trainer.fit(model=lit_module, train_dataloaders=train_loader, val_dataloaders=val_loader)\n", + " models[element]=trainer\n", + " trainer.test(dataloaders=test_loader)\n", + "\n", + " ######\n", + "\n", + " def get_matgl_pred(atoms=None,model=None):\n", + " element_types = get_element_list([atoms.pymatgen_converter()])\n", + " p2g = Structure2Graph(element_types=element_types, cutoff=5.0)\n", + " graph, lat, state = p2g.get_graph(atoms.pymatgen_converter())\n", + " ff = Potential(model=model, calc_hessian=False)\n", + " e, f, s, h = ff(graph, lat, torch.tensor(state))\n", + " return e.detach().numpy(),f.detach().numpy(),s.detach().numpy()\n", + "\n", + " df = pd.DataFrame(\n", + " json.loads(\n", + " zipfile.ZipFile(\"mlearn.json.zip\").read(\n", + " \"mlearn.json\"\n", + " )\n", + " )\n", + " )\n", + " print(df)\n", + " i_model = trainer.model.model.model\n", + " for i in glob.glob(\"/content/jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/*energy*.zip\"):\n", + " if \"mlearn\" in i and element in i:\n", + " fname_e = (\n", + " \"AI-MLFF-energy-\"\n", + " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " + \"-test-mae.csv\"\n", + " )\n", + " fname_f = (\n", + " \"AI-MLFF-forces-\"\n", + " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " + \"-test-multimae.csv\"\n", + " )\n", + " fname_s = (\n", + " \"AI-MLFF-stresses-\"\n", + " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " + \"-test-multimae.csv\"\n", + " )\n", + " f_e = open(fname_e, \"w\")\n", + " f_f = open(fname_f, \"w\")\n", + " f_s = open(fname_s, \"w\")\n", + "\n", + " f_e.write(\"id,prediction\\n\")\n", + " f_f.write(\"id,prediction\\n\")\n", + " f_s.write(\"id,prediction\\n\")\n", + "\n", + " print(i)\n", + " dat = json.loads(\n", + " zipfile.ZipFile(i).read(i.split(\"/\")[-1].split(\".zip\")[0])\n", + " )\n", + " print(dat[\"test\"])\n", + " for key, val in dat[\"test\"].items():\n", + " entry = df[df[\"jid\"] == key]\n", + " atoms = Atoms.from_dict(entry.atoms.values[0])\n", + " # print(key,val,df[df['jid']==key],atoms)\n", + " # energy,forces=get_alignn_forces(atoms)\n", + " energy, forces, stress = get_matgl_pred(atoms=atoms,model=i_model)\n", + " print(key, val, energy, atoms.num_atoms)\n", + " line = key + \",\" + str(energy) + \"\\n\"\n", + " f_e.write(line)\n", + " line = (\n", + " key\n", + " + \",\"\n", + " + str(\";\".join(map(str, np.array(forces).flatten())))\n", + " + \"\\n\"\n", + " )\n", + " f_f.write(line)\n", + " line = (\n", + " key\n", + " + \",\"\n", + " + str(\";\".join(map(str, np.array(stress).flatten())))\n", + " + \"\\n\"\n", + " )\n", + " f_s.write(line)\n", + " f_e.close()\n", + " f_f.close()\n", + " f_s.close()\n", + " zname = fname_e + \".zip\"\n", + " with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " myzip.write(fname_e)\n", + "\n", + " zname = fname_f + \".zip\"\n", + " with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " myzip.write(fname_f)\n", + "\n", + " zname = fname_s + \".zip\"\n", + " with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " myzip.write(fname_s)\n", + "\n", + "\n", + "# x=[]\n", + "# y=[]\n", + "# i_model = trainer.model.model.model\n", + "# for i,j,k in zip(test_structures,test_energies,tids):\n", + "\n", + "# e, f, s, h = get_matgl_pred(structure=i,model=i_model)\n", + "# #pen = trainer.model.model.model.predict_structure(i)\n", + "# nat = len(i)\n", + "# print(j,e)\n", + "# x.append(j/nat)\n", + "# y.append(e/nat)\n", + "# x = np.array(x)\n", + "# y = np.array(y)\n", + "# from sklearn.metrics import mean_absolute_error\n", + "# mean_absolute_error(x,y)\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 558, + "referenced_widgets": [ + "fd589a80de1049e2a3b4bb43e6b8dbde", + "20cec4b4a2cb49b0900113221cf036ec", + "39aebc5c03ed488e98c40364e8b73eab", + "d6415f9ba18e4814ae2203cdc29ec86e", + "d10606a6a4f14fd8a2c89d239aae26b2", + "9697e8dc29134ab4857712cb97058cec", + "a62beeccdeb84f6da9ed01c712700165", + "4f415ddcdf434988b103b35e63c17149", + "b6f112db53034dc8b65d5e8d2435ed02", + "8343cfa3dc104570902818f3bce449ff", + "ac985ab639bb49cc91541ae734827592", + "b8aa389e188b4a4b82282fdd4ede20a1", + "5f12a4c32d734e45b1a55b2c53a53c2a", + "2e4d2c78609a40c59328e0375a3e3d16", + "e56df54852744f62bd61f83a5ecf6a66", + "5e29206fd10f4cc4bc62224a2be05217", + "4af34f3b4f9e4acc9021503e492f70c9", + "6b43a4bc3ca646ceb56b9dc03cb9d8cb", + "787d4f41186849b590c56fd60ff5b19f", + "875593e5a14f472dad544236b170d67b", + "b1c2a68ed0284b869df724cdec2b8b4e", + "aeab5e13b0054c4b8af5e7fd3e414b0a", + "c47dfda1d97b403eb83b309516fd7f66", + 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Assuming PyTorch for now.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable. Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 214/214 [00:02<00:00, 83.15it/s]\n", + "INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n", + "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n", + "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n", + "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n", + "WARNING:lightning_fabric.loggers.csv_logs:Missing logger folder: logs/M3GNet_training\n", + "INFO:pytorch_lightning.callbacks.model_summary:\n", + " | Name | Type | Params\n", + "--------------------------------------------\n", + "0 | mae | MeanAbsoluteError | 0 \n", + "1 | rmse | MeanSquaredError | 0 \n", + "2 | model | Potential | 282 K \n", + "--------------------------------------------\n", + "282 K Trainable params\n", + "0 Non-trainable params\n", + "282 K Total params\n", + "1.130 Total estimated model params size (MB)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Sanity Checking: | | 0/? 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"Successfully installed colorama-0.4.6 ghp-import-2.1.0 jarvis-tools-2023.9.20 mergedeep-1.3.4 mkdocs-1.5.3 mkdocs-material-9.4.5 mkdocs-material-extensions-1.2 paginate-0.5.6 pathspec-0.11.2 pymdown-extensions-10.3 pyyaml-env-tag-0.1 spglib-2.1.0 watchdog-3.0.0 xmltodict-0.13.0\n" + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m974.6/974.6 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m802.1/802.1 kB\u001b[0m \u001b[31m45.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m57.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.7/3.7 MB\u001b[0m \u001b[31m40.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m241.0/241.0 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.1/82.1 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for paginate (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ - "!pip install jarvis-tools" + "!pip install -q jarvis-tools" ] }, - { - "cell_type": "markdown", - "source": [ - "# JARVIS-Tools +Quantum Espresso workflow used in: https://www.nature.com/articles/s41524-022-00933-1 and https://pubs.acs.org/doi/abs/10.1021/acs.nanolett.2c04420" - ], - "metadata": { - "id": "YYF-iR3aUOY3" - } - }, { "cell_type": "code", "source": [ "%%time\n", "import os\n", + "os.chdir('/content')\n", "# Clone the QE git\n", "if not os.path.exists('q-e'):\n", " os.system('git clone https://github.com/QEF/q-e.git')\n", "# Install the dependencies\n", - "!apt-get update\n", "!apt-get install -y libfftw3-3 libfftw3-dev libfftw3-doc\n", - "%cd q-e\n", + "os.chdir('q-e')\n", "# run the configure file\n", - "!DFLAGS='-D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK' FFT_LIBS='-lfftw3' ./configure --enable-openmp\n", + "#!DFLAGS='-D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK' FFT_LIBS='-lfftw3' ./configure --enable-openmp\n", + "\n", + "!!DFLAGS='-D__FFTW3 ' FFT_LIBS='-lfftw3' ./configure\n", "! make pw" ], "metadata": { @@ -159,39 +89,23 @@ "base_uri": "https://localhost:8080/" }, "id": "wnTJPQJyxE0r", - "outputId": "f342c3eb-e268-4d7f-93ec-eee512b5a862" + "outputId": "c799c17e-e1f5-4711-d305-1a5a10b20b50" }, - "execution_count": 1, + "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "\r0% [Working]\r \rGet:1 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease [3,626 B]\n", - "\r0% [Connecting to archive.ubuntu.com] [Waiting for headers] [1 InRelease 0 B/3,\r0% [Connecting to archive.ubuntu.com] [Waiting for headers] [Connecting to ppa.\r \rGet:2 http://security.ubuntu.com/ubuntu jammy-security InRelease [110 kB]\n", - "Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", - "Hit:4 http://archive.ubuntu.com/ubuntu jammy InRelease\n", - "Get:5 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [119 kB]\n", - "Get:6 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [109 kB]\n", - "Hit:7 https://ppa.launchpadcontent.net/c2d4u.team/c2d4u4.0+/ubuntu jammy InRelease\n", - "Hit:8 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n", - "Get:9 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1,004 kB]\n", - "Get:10 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [1,347 kB]\n", - "Get:11 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [1,081 kB]\n", - "Hit:12 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n", - "Get:13 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,269 kB]\n", - "Hit:14 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n", - "Fetched 5,043 kB in 2s (2,503 kB/s)\n", - "Reading package lists... Done\n", "Reading package lists... Done\n", "Building dependency tree... Done\n", "Reading state information... Done\n", "The following additional packages will be installed:\n", " libfftw3-bin libfftw3-double3 libfftw3-long3 libfftw3-quad3 libfftw3-single3\n", "The following NEW packages will be installed:\n", - " libfftw3-3 libfftw3-bin libfftw3-dev libfftw3-doc libfftw3-double3\n", - " libfftw3-long3 libfftw3-quad3 libfftw3-single3\n", - "0 upgraded, 8 newly installed, 0 to remove and 25 not upgraded.\n", + " libfftw3-3 libfftw3-bin libfftw3-dev libfftw3-doc libfftw3-double3 libfftw3-long3 libfftw3-quad3\n", + " libfftw3-single3\n", + "0 upgraded, 8 newly installed, 0 to remove and 18 not upgraded.\n", "Need to get 4,918 kB of archives.\n", "After this operation, 26.2 MB of additional disk space will be used.\n", "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-double3 amd64 3.3.8-2ubuntu8 [770 kB]\n", @@ -202,7 +116,7 @@ "Get:6 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-bin amd64 3.3.8-2ubuntu8 [35.5 kB]\n", "Get:7 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-dev amd64 3.3.8-2ubuntu8 [2,101 kB]\n", "Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-doc all 3.3.8-2ubuntu8 [262 kB]\n", - "Fetched 4,918 kB in 0s (11.7 MB/s)\n", + "Fetched 4,918 kB in 1s (3,447 kB/s)\n", "Selecting previously unselected package libfftw3-double3:amd64.\n", "(Reading database ... 120875 files and directories currently installed.)\n", "Preparing to unpack .../0-libfftw3-double3_3.3.8-2ubuntu8_amd64.deb ...\n", @@ -238,384 +152,70 @@ "Setting up libfftw3-dev:amd64 (3.3.8-2ubuntu8) ...\n", "Processing triggers for man-db (2.10.2-1) ...\n", "Processing triggers for libc-bin (2.35-0ubuntu3.1) ...\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", - "\n", "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", "\n", - "/content/q-e\n", - "directory QEHeat/src : ok\n", - "directory ACFDT/src : not present in /content/q-e/install\n", - "directory KCW/PP : ok\n", - "all dependencies updated successfully\n", - "checking build system type... x86_64-pc-linux-gnu\n", - "checking ARCH... x86_64\n", - "checking setting AR... ... ar\n", - "checking setting ARFLAGS... ... ruv\n", - "checking for gfortran... gfortran\n", - "checking whether the Fortran compiler works... yes\n", - "checking for Fortran compiler default output file name... a.out\n", - "checking for suffix of executables... \n", - "checking whether we are cross compiling... no\n", - "checking for suffix of object files... o\n", - "checking whether the compiler supports GNU Fortran... yes\n", - "checking whether gfortran accepts -g... yes\n", - "checking for Fortran flag to compile .f90 files... none\n", - "checking for mpiifort... no\n", - "checking for mpif90... mpif90\n", - "checking whether the compiler supports GNU Fortran... yes\n", - "checking whether mpif90 accepts -g... yes\n", - "checking version of mpif90... gfortran 11.4\n", - "setting F90... gfortran\n", - "setting MPIF90... mpif90\n", - "checking for cc... cc\n", - "checking whether the compiler supports GNU C... yes\n", - "checking whether cc accepts -g... yes\n", - "checking for cc option to enable C11 features... none needed\n", - "setting CC... cc\n", - "setting CFLAGS... -O3\n", - "using F90... gfortran\n", - "setting FFLAGS... -O3 -g -fallow-argument-mismatch -fopenmp\n", - "setting F90FLAGS... $(FFLAGS) -cpp -fopenmp\n", - "setting FFLAGS_NOOPT... -O0 -g\n", - "setting CPP... cpp\n", - "setting CPPFLAGS... -P -traditional -Uvector\n", - "setting LD... mpif90\n", - "setting LDFLAGS... -g -pthread -fopenmp\n", - "checking whether make sets $(MAKE)... yes\n", - "checking whether Fortran files must be preprocessed... no\n", - "checking for library containing dgemm... -lmkl_gf_lp64\n", - "setting BLAS_LIBS... -lmkl_gf_lp64 -lmkl_gnu_thread -lmkl_core\n", - "checking FFT... using FFT_LIBS with no testing ... \n", - "BEWARE: FFT not explicitly selected, verify your DFLAGS= line\n", - "-lfftw3\n", - "checking MASS... \n", - "checking for library containing mpi_init... none required\n", - "checking for library containing pdgemr2d... no\n", - "checking for library containing pdgemr2d... no\n", - "checking for library containing pdgemr2d... no\n", - "checking for library containing pdgemr2d... no\n", - "checking for library containing pdgemr2d... no\n", - "checking for library containing pdgemr2d... no\n", - "checking ELPA... \n", - "checking for Environ... not used\n", - "checking if legacy plugins are enabled ... no\n", - "checking for ranlib... ranlib\n", - "checking for wget... wget -O\n", - "setting WGET... wget -O\n", - "checking for git... git\n", - "Source files are cloned from a git repository.\n", - "On git branch develop\n", - "On git commit hash e3968f1e75e8661110548601b5381f9e496e6930\n", - "Submodule 'external/d3q' (https://github.com/anharmonic/d3q.git) registered for path 'external/d3q'\n", - "Submodule 'external/devxlib' (https://gitlab.com/max-centre/components/devicexlib.git) registered for path 'external/devxlib'\n", - "Submodule 'external/fox' (https://github.com/pietrodelugas/fox.git) registered for path 'external/fox'\n", - "Submodule 'external/lapack' (https://github.com/Reference-LAPACK/lapack.git) registered for path 'external/lapack'\n", - "Submodule 'external/mbd' (https://github.com/libmbd/libmbd.git) registered for path 'external/mbd'\n", - "Submodule 'external/pw2qmcpack' (https://github.com/QMCPACK/pw2qmcpack.git) registered for path 'external/pw2qmcpack'\n", - "Submodule 'external/qe-gipaw' (https://github.com/dceresoli/qe-gipaw) registered for path 'external/qe-gipaw'\n", - "Submodule 'external/wannier90' (https://github.com/wannier-developers/wannier90.git) registered for path 'external/wannier90'\n", - "setting DFLAGS... -D__MPI\n", - "setting IFLAGS... -I. -I$(TOPDIR)/include\n", - "configure: creating ./config.status\n", - "config.status: creating install/make_lapack.inc\n", - "config.status: creating include/configure.h\n", - "config.status: creating make.inc\n", - "config.status: creating configure.msg\n", - "config.status: creating install/make_wannier90.inc\n", - "config.status: creating include/qe_cdefs.h\n", - "--------------------------------------------------------------------\n", - "ESPRESSO can take advantage of several optimized numerical libraries\n", - "(essl, fftw, mkl...). This configure script attempts to find them,\n", - "but may fail if they have been installed in non-standard locations.\n", - "If a required library is not found, the local copy will be compiled.\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", "\n", - "The following libraries have been found:\n", - " BLAS_LIBS= -lmkl_gf_lp64 -lmkl_gnu_thread -lmkl_core\n", - " LAPACK_LIBS=\n", - " FFT_LIBS=-lfftw3\n", - " \n", - " \n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", "\n", - "Please check if this is what you expect.\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n", "\n", - "If any libraries are missing, you may specify a list of directories\n", - "to search and retry, as follows:\n", - " ./configure LIBDIRS=\"list of directories, separated by spaces\"\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", "\n", - "Parallel environment detected successfully.\\\n", - "Configured for compilation of parallel executables.\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", "\n", - "For more info, read the ESPRESSO User's Guide (Doc/users-guide.tex).\n", - "--------------------------------------------------------------------\n", - "configure: success\n", "test -d bin || mkdir bin\n", "( cd UtilXlib ; make TLDEPS= all || exit 1 )\n", "make[1]: Entering directory '/content/q-e/UtilXlib'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c parallel_include.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c parallel_include.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c util_param.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c util_param.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c clib_wrappers.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c clib_wrappers.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c nvtx_wrapper.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c nvtx_wrapper.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c clocks_handler.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c clocks_handler.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c cptimer.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c copy.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c c_mkdir.c\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c device_helper.f90\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c cptimer.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c copy.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c c_mkdir.c\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c device_helper.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "\u001b[01m\u001b[Kmp.f90:2783:19:\u001b[m\u001b[K\n", - "\n", - " 2783 | CALL MPI_Irecv( recvbuf, size(recvbuf), MPI_DOUBLE_PRECISION, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 2827 | CALL MPI_Irecv( recvbuf, size(recvbuf), MPI_DOUBLE_COMPLEX, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kmp.f90:2788:19:\u001b[m\u001b[K\n", - "\n", - " 2788 | CALL MPI_Isend( sendbuf, size(sendbuf), MPI_DOUBLE_PRECISION, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 2832 | CALL MPI_Isend( sendbuf, size(sendbuf), MPI_DOUBLE_COMPLEX, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kmp.f90:2579:30:\u001b[m\u001b[K\n", - "\n", - " 2579 | CALL MPI_Sendrecv_replace( buf, SIZE(buf), MPI_DOUBLE_PRECISION, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 2613 | CALL MPI_Sendrecv_replace( buf, SIZE(buf), MPI_DOUBLE_COMPLEX, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kmp.f90:2401:22:\u001b[m\u001b[K\n", - "\n", - " 2401 | CALL MPI_ALLTOALL( sndbuf, nsiz, MPI_DOUBLE_COMPLEX, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 2437 | CALL MPI_ALLTOALL( sndbuf, nsiz, MPI_INTEGER, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/INTEGER(4)).\n", - "\u001b[01m\u001b[Kmp.f90:2322:29:\u001b[m\u001b[K\n", - "\n", - " 2322 | alldata, recvcount, displs, my_element_type, gid, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 2346 | alldata, recvcount, displs, my_element_type, gid, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "\u001b[01m\u001b[Kmp.f90:2293:29:\u001b[m\u001b[K\n", - "\n", - " 2290 | CALL MPI_GATHERV( MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 2293 | CALL MPI_GATHERV( alldata(1,displs(myid+1)+1), recvcount(myid+1), my_column_type, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/INTEGER(4)).\n", - "\u001b[01m\u001b[Kmp.f90:2252:26:\u001b[m\u001b[K\n", - "\n", - " 2252 | CALL MPI_GATHERV( mydata, nrecv( myid + 1 ), MPI_INTEGER, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 2290 | CALL MPI_GATHERV( MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (scalar and rank-2)\n", - "\u001b[01m\u001b[Kmp.f90:2155:26:\u001b[m\u001b[K\n", - "\n", - " 2155 | CALL MPI_GATHERV( mydata, recvcount( myid + 1 ), MPI_INTEGER, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 2290 | CALL MPI_GATHERV( MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (scalar and rank-1)\n", - "\u001b[01m\u001b[Kmp.f90:1974:40:\u001b[m\u001b[K\n", - "\n", - " 1974 | CALL parallel_min_real( msglen, msg, gid, -1 )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 1986 | CALL parallel_min_real( msglen, msg, gid, -1 )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "\u001b[01m\u001b[Kmp.f90:1952:43:\u001b[m\u001b[K\n", - "\n", - " 1952 | CALL parallel_min_integer( msglen, msg, gid, -1 )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 1963 | CALL parallel_min_integer( msglen, msg, gid, -1 )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "\u001b[01m\u001b[Kmp.f90:1929:40:\u001b[m\u001b[K\n", - "\n", - " 1929 | CALL parallel_max_real( msglen, msg, gid, -1 )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 1941 | CALL parallel_max_real( msglen, msg, gid, -1 )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "\u001b[01m\u001b[Kmp.f90:1900:43:\u001b[m\u001b[K\n", - "\n", - " 1900 | CALL parallel_max_integer( msglen, msg, gid, -1 )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 1916 | CALL parallel_max_integer( msglen, msg, gid, -1 )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "\u001b[01m\u001b[Kmp.f90:1870:39:\u001b[m\u001b[K\n", - "\n", - " 1870 | CALL reduce_base_real( msglen, msg, gid, -1 )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 1886 | CALL reduce_base_real( 2 * msglen, msg, gid, -1 )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kmp.f90:1732:43:\u001b[m\u001b[K\n", - "\n", - " 1732 | CALL reduce_base_real( 2 * msglen, msg, gid, -1 )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 1886 | CALL reduce_base_real( 2 * msglen, msg, gid, -1 )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "\u001b[01m\u001b[Kmp.f90:1680:42:\u001b[m\u001b[K\n", - "\n", - " 1680 | CALL reduce_base_real_to( msglen, msg, res, group, root )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 1793 | CALL reduce_base_real_to( 2 * msglen, msg, res, gid, -1 )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kmp.f90:1438:42:\u001b[m\u001b[K\n", - "\n", - " 1438 | CALL reduce_base_integer( msglen, msg, gid, -1 )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 1528 | CALL reduce_base_integer( msglen, msg, gid, -1 )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "\u001b[01m\u001b[Kmp.f90:1342:28:\u001b[m\u001b[K\n", - "\n", - " 1342 | CALL MPI_SEND( msg_sour, SIZE(msg_sour), MPI_DOUBLE_PRECISION, dest, ip, group, ierr)\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 1389 | CALL MPI_SEND( msg_sour, SIZE(msg_sour), MPI_DOUBLE_COMPLEX, dest, ip, group, ierr)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kmp.f90:1346:28:\u001b[m\u001b[K\n", - "\n", - " 1346 | CALL MPI_RECV( msg_dest, SIZE(msg_dest), MPI_DOUBLE_PRECISION, sour, ip, group, istatus, IERR )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 1393 | CALL MPI_RECV( msg_dest, SIZE(msg_dest), MPI_DOUBLE_COMPLEX, sour, ip, group, istatus, IERR )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kmp.f90:726:28:\u001b[m\u001b[K\n", - "\n", - " 726 | CALL bcast_logical( msg, msglen, source, gid )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 758 | CALL bcast_logical( msg, msglen, source, gid )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "\u001b[01m\u001b[Kmp.f90:634:25:\u001b[m\u001b[K\n", - "\n", - " 634 | CALL bcast_real( msg, 2 * msglen, source, gid )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 711 | CALL bcast_real( msg, 2 * msglen, source, gid )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "\u001b[01m\u001b[Kmp.f90:620:25:\u001b[m\u001b[K\n", - "\n", - " 620 | CALL bcast_real( msg, msglen, source, gid )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 711 | CALL bcast_real( msg, 2 * msglen, source, gid )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kmp.f90:443:28:\u001b[m\u001b[K\n", - "\n", - " 443 | CALL bcast_integer( msg, msglen, source, group )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 817 | CALL bcast_integer( imsg, msglen, source, group )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "\u001b[01m\u001b[Kmp.f90:191:24:\u001b[m\u001b[K\n", - "\n", - " 191 | CALL MPI_GATHER(mydata, 1, MPI_INTEGER, alldata, 1, MPI_INTEGER, root, group, IERR)\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 215 | CALL MPI_GATHER(mydata, msglen, MPI_INTEGER, alldata, msglen, MPI_INTEGER, root, group, IERR)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c divide.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c divide.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c data_buffer.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c data_buffer.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c eval_infix.c\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c error_handler.f90\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c eval_infix.c\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c error_handler.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp_bands_util.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp_bands_util.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c export_gstart_2_solvers.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c export_gstart_2_solvers.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c find_free_unit.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c find_free_unit.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c fletcher32_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c fletcher32_mod.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c fletcher32.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c md5.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c md5_from_file.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c memstat.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c memusage.c\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mem_counter.f90\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c fletcher32.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c md5.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c md5_from_file.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c memstat.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c memusage.c\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mem_counter.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp_base.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp_base.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "\u001b[01m\u001b[Kmp_base.f90:142:20:\u001b[m\u001b[K\n", - "\n", - " 90 | CALL MPI_BCAST( array, n, MPI_DOUBLE_PRECISION, root, gid, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 142 | CALL MPI_BCAST(array, n, MPI_INTEGER, root, gid, ierr)\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (INTEGER(4)/REAL(8)).\n", - "\u001b[01m\u001b[Kmp_base.f90:601:22:\u001b[m\u001b[K\n", - "\n", - " 385 | CALL MPI_REDUCE( ps(1+(n-1)*maxb), buff, maxb, MPI_DOUBLE_PRECISION, MPI_SUM, root, comm, info )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 601 | CALL MPI_REDUCE(ps(1 + (n - 1) * maxb), buff, maxb, MPI_INTEGER, MPI_SUM, root, comm, info)\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (INTEGER(4)/REAL(8)).\n", - "\u001b[01m\u001b[Kmp_base.f90:604:25:\u001b[m\u001b[K\n", - "\n", - " 388 | CALL MPI_ALLREDUCE( ps(1+(n-1)*maxb), buff, maxb, MPI_DOUBLE_PRECISION, MPI_SUM, comm, info )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 604 | CALL MPI_ALLREDUCE(ps(1 + (n - 1) * maxb), buff, maxb, MPI_INTEGER, MPI_SUM, comm, info)\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (INTEGER(4)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp_base_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp_base_gpu.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c print_mem.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c print_mem.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c ptrace.c\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c set_mpi_comm_4_solvers.f90\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c ptrace.c\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c set_mpi_comm_4_solvers.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c thread_util.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c thread_util.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "ar ruv libutil.a clib_wrappers.o clocks_handler.o cptimer.o copy.o c_mkdir.o device_helper.o divide.o data_buffer.o eval_infix.o error_handler.o export_gstart_2_solvers.o find_free_unit.o fletcher32_mod.o fletcher32.o md5.o md5_from_file.o memstat.o memusage.o mem_counter.o mp.o mp_base.o mp_base_gpu.o mp_bands_util.o parallel_include.o print_mem.o ptrace.o set_mpi_comm_4_solvers.o util_param.o thread_util.o nvtx_wrapper.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", @@ -661,7 +261,7 @@ "remote: Counting objects: 100% (116/116), done.\u001b[K\n", "remote: Compressing objects: 100% (67/67), done.\u001b[K\n", "remote: Total 76 (delta 41), reused 26 (delta 8), pack-reused 0\u001b[K\n", - "Unpacking objects: 100% (76/76), 88.62 KiB | 1.77 MiB/s, done.\n", + "Unpacking objects: 100% (76/76), 88.62 KiB | 1.70 MiB/s, done.\n", "From https://gitlab.com/max-centre/components/devicexlib\n", " * branch a6b89ef77b1ceda48e967921f1f5488d2df9226d -> FETCH_HEAD\n", "Submodule path 'external/devxlib': checked out 'a6b89ef77b1ceda48e967921f1f5488d2df9226d'\n", @@ -671,22 +271,22 @@ "make all; \\\n", "touch /content/q-e//install/libcuda_devxlib # do not download and configure again\n", "make[2]: Entering directory '/content/q-e/external/devxlib/src'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_auxfunc_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_auxfunc_mod.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e/external/devxlib//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_fbuff.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_fbuff.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e/external/devxlib//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_fbuff_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_fbuff_mod.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e/external/devxlib//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_memcpy_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_memcpy_mod.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e/external/devxlib//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c deviceXlib_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c deviceXlib_mod.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e/external/devxlib//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_memcpy.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_memcpy.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e/external/devxlib//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_auxfunc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c device_auxfunc.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e/external/devxlib//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e/external/devxlib//include -c timer.c\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c timer_mod.f90\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e/external/devxlib//include -c timer.c\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e/external/devxlib//external/devxlib/src -I. -I/content/q-e/external/devxlib//include -c timer_mod.f90\n", "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e/external/devxlib//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "ar ruv libdevXlib.a deviceXlib_mod.o device_memcpy.o device_memcpy_mod.o device_auxfunc.o device_auxfunc_mod.o device_fbuff.o device_fbuff_mod.o timer.o timer_mod.o\n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", @@ -705,161 +305,33 @@ "make[1]: Leaving directory '/content/q-e/install'\n", "( cd LAXlib ; make TLDEPS= all || exit 1 )\n", "make[1]: Entering directory '/content/q-e/LAXlib'\n", - "cpp -P -traditional -Uvector -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include laxlib.h -o laxlib.fh\n", - "cpp -P -traditional -Uvector -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include laxlib_hi.h -o laxlib_hi.fh\n", - "cpp -P -traditional -Uvector -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include laxlib_kinds.h -o laxlib_kinds.fh\n", - "cpp -P -traditional -Uvector -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include laxlib_low.h -o laxlib_low.fh\n", - "cpp -P -traditional -Uvector -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include laxlib_mid.h -o laxlib_mid.fh\n", - "cpp -P -traditional -Uvector -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include laxlib_param.h -o laxlib_param.fh\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c la_types.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c la_param.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c la_error.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c la_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c dspev_drv.f90\n", - "\u001b[01m\u001b[Kdspev_drv.f90:187:33:\u001b[m\u001b[K\n", - "\n", - " 142 | CALL MPI_ALLREDUCE( MPI_IN_PLACE, scalef, 1, MPI_DOUBLE_PRECISION, MPI_SUM, comm, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", - "......\n", - " 187 | CALL MPI_ALLREDUCE( vtmp, u, L+2, MPI_DOUBLE_PRECISION, MPI_SUM, comm, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/INTEGER(4)).\n", - "\u001b[01m\u001b[Kdspev_drv.f90:252:27:\u001b[m\u001b[K\n", - "\n", - " 252 | CALL MPI_BCAST( g, 1, MPI_DOUBLE_PRECISION, ri( L ), comm, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 489 | CALL MPI_BCAST( cv, 2*(m-l), MPI_DOUBLE_PRECISION, 0, comm, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mp_diag.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c la_helper.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c zhpev_drv.f90\n", - "\u001b[01m\u001b[Kzhpev_drv.f90:1035:22:\u001b[m\u001b[K\n", - "\n", - " 975 | CALL MPI_BCAST( dvar, 6, MPI_DOUBLE_PRECISION, 0, comm, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 1035 | CALL MPI_BCAST( m, 1, MPI_INTEGER, 0, comm, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (INTEGER(4)/REAL(8)).\n", - "\u001b[01m\u001b[Kzhpev_drv.f90:227:50:\u001b[m\u001b[K\n", - "\n", - " 227 | CALL MPI_ALLREDUCE( MPI_IN_PLACE, xnorm, 1, MPI_DOUBLE_PRECISION, MPI_SUM, comm, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 694 | CALL MPI_ALLREDUCE( MPI_IN_PLACE, work, 2*(n - 1 - i), MPI_DOUBLE_PRECISION, MPI_SUM, comm, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kzhpev_drv.f90:330:35:\u001b[m\u001b[K\n", - "\n", - " 330 | CALL MPI_ALLREDUCE( ctmpv( i + 1 ), apki( i + 1 ), 2*(n - i), MPI_DOUBLE_PRECISION, MPI_SUM, comm, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 694 | CALL MPI_ALLREDUCE( MPI_IN_PLACE, work, 2*(n - 1 - i), MPI_DOUBLE_PRECISION, MPI_SUM, comm, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/INTEGER(4)).\n", - "\u001b[01m\u001b[Kzhpev_drv.f90:392:49:\u001b[m\u001b[K\n", - "\n", - " 392 | CALL MPI_ALLREDUCE( MPI_IN_PLACE, alpha, 2, MPI_DOUBLE_PRECISION, MPI_SUM, comm, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 694 | CALL MPI_ALLREDUCE( MPI_IN_PLACE, work, 2*(n - 1 - i), MPI_DOUBLE_PRECISION, MPI_SUM, comm, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c cdiaghg.f90\n", - "\u001b[01m\u001b[Kcdiaghg.f90:196:18:\u001b[m\u001b[K\n", - "\n", - " 193 | CALL MPI_BCAST( e, SIZE(e), MPI_DOUBLE_PRECISION, root_bgrp, intra_bgrp_comm, info )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 196 | CALL MPI_BCAST( v, SIZE(v), MPI_DOUBLE_COMPLEX, root_bgrp, intra_bgrp_comm, info )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c rdiaghg.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c ptoolkit.f90\n", - "\u001b[01m\u001b[Kptoolkit.f90:193:31:\u001b[m\u001b[K\n", - "\n", - " 193 | CALL mpi_isend( buf, ib, MPI_DOUBLE_PRECISION, ipr-1, ipr, col_comm, sreq, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 581 | CALL mpi_isend( buf, ib, MPI_DOUBLE_COMPLEX, ipr-1, ipr, col_comm, sreq, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kptoolkit.f90:220:33:\u001b[m\u001b[K\n", - "\n", - " 220 | CALL mpi_recv( buf, nrsnd(ipr_old), MPI_DOUBLE_PRECISION, ipr_old-1, ipr, col_comm, istatus, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 608 | CALL mpi_recv( buf, nrsnd(ipr_old), MPI_DOUBLE_COMPLEX, ipr_old-1, ipr, col_comm, istatus, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kptoolkit.f90:1081:22:\u001b[m\u001b[K\n", - "\n", - " 981 | CALL mpi_gather( a, nx*nx, mpi_double_precision, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 1081 | CALL mpi_gather( a, nx*nx, mpi_double_complex, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "\u001b[01m\u001b[Kptoolkit.f90:1106:18:\u001b[m\u001b[K\n", - "\n", - " 1006 | CALL mpi_bcast( ar, ldar * n, mpi_double_precision, 0, comm, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 1106 | CALL mpi_bcast( ar, ldar * n, mpi_double_complex, 0, comm, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "\u001b[01m\u001b[Kptoolkit.f90:3093:32:\u001b[m\u001b[K\n", - "\n", - " 2386 | CALL MPI_SENDRECV_REPLACE(blk, nb*nb, MPI_DOUBLE_PRECISION, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 3093 | CALL MPI_SENDRECV_REPLACE(blk, nb*nb, MPI_REAL, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(4)/REAL(8)).\n", + "cpp -P -traditional -Uvector -D__FFTW3 -I. -I/content/q-e//include laxlib.h -o laxlib.fh\n", + "cpp -P -traditional -Uvector -D__FFTW3 -I. -I/content/q-e//include laxlib_hi.h -o laxlib_hi.fh\n", + "cpp -P -traditional -Uvector -D__FFTW3 -I. -I/content/q-e//include laxlib_kinds.h -o laxlib_kinds.fh\n", + "cpp -P -traditional -Uvector -D__FFTW3 -I. -I/content/q-e//include laxlib_low.h -o laxlib_low.fh\n", + "cpp -P -traditional -Uvector -D__FFTW3 -I. -I/content/q-e//include laxlib_mid.h -o laxlib_mid.fh\n", + "cpp -P -traditional -Uvector -D__FFTW3 -I. -I/content/q-e//include laxlib_param.h -o laxlib_param.fh\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c la_types.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c la_param.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c la_error.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c la_module.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c dspev_drv.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mp_diag.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c la_helper.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c zhpev_drv.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c cdiaghg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c rdiaghg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c ptoolkit.f90\n", "\u001b[01m\u001b[Kptoolkit.f90:3197:43:\u001b[m\u001b[K\n", "\n", - " 1945 | CALL zgemm(TRANSA, TRANSB, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc)\n", + " 2050 | CALL zgemm(TRANSA, TRANSB, m, N, k, alpha, A, lda, B, ldb, beta, C, ldc)\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", "......\n", " 3197 | CALL zgemm( TRANSA, TRANSB, n, n, n, alpha, a, lda, b, ldb, beta, c, ldc)\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "\u001b[01m\u001b[Kptoolkit.f90:5124:29:\u001b[m\u001b[K\n", - "\n", - " 4885 | CALL MPI_REDUCE( ssnd, sll, ldx*ldx, MPI_DOUBLE_COMPLEX, MPI_SUM, jb-1, rcomm, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 5124 | CALL MPI_REDUCE( ssnd, sll, ldx*ldx, MPI_DOUBLE_PRECISION, MPI_SUM, jb-1, rcomm, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kptoolkit.f90:5473:26:\u001b[m\u001b[K\n", - "\n", - " 3985 | CALL MPI_SENDRECV(a, ldx*nx, MPI_DOUBLE_PRECISION, idest, np+np+1, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", - "......\n", - " 5473 | CALL MPI_Sendrecv( c, ldx*ldx, MPI_DOUBLE_COMPLEX, send, 0, buf_recv, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "\u001b[01m\u001b[Kptoolkit.f90:5734:24:\u001b[m\u001b[K\n", - "\n", - " 5361 | CALL MPI_Send(sll, ldx*ldx, MPI_DOUBLE_COMPLEX, idref, 0, comm, ierr)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 5734 | CALL MPI_Send(sll, ldx*ldx, MPI_DOUBLE_PRECISION, idref, 0, comm, ierr)\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kptoolkit.f90:5750:26:\u001b[m\u001b[K\n", - "\n", - " 5377 | CALL MPI_Irecv( B, ldx*ldx, MPI_DOUBLE_COMPLEX, i, 0, comm, req(1), ierr)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 5750 | CALL MPI_Irecv( B, ldx*ldx, MPI_DOUBLE_PRECISION, i, 0, comm, req(1), ierr)\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c transto.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c distools.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c transto.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c distools.f90\n", "ar ruv libqela.a la_types.o la_error.o la_module.o la_helper.o cdiaghg.o rdiaghg.o dspev_drv.o ptoolkit.o transto.o distools.o zhpev_drv.o mp_diag.o la_param.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libqela.a\n", @@ -882,94 +354,39 @@ "make[1]: Entering directory '/content/q-e/FFTXlib'\n", "( cd src ; make all || exit 1 )\n", "make[2]: Entering directory '/content/q-e/FFTXlib/src'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_param.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_support.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c stick_base.f90\n", - "\u001b[01m\u001b[Kstick_base.f90:254:42:\u001b[m\u001b[K\n", - "\n", - " 253 | CALL MPI_ALLREDUCE(MPI_IN_PLACE, st, size(st), MPI_INTEGER, MPI_SUM, comm, ierr)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - " 254 | CALL MPI_ALLREDUCE(MPI_IN_PLACE, ngm, 1, MPI_INTEGER, MPI_SUM, comm, ierr)\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_types.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c scatter_mod.f90\n", - "\u001b[01m\u001b[Kscatter_mod.f90:428:23:\u001b[m\u001b[K\n", - "\n", - " 413 | CALL MPI_ALLGATHERV( f_in(offset_in), recvcount(dfftp%mype2), MPI_DOUBLE_PRECISION, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 428 | CALL MPI_ALLGATHERV( f_aux, recvcount(dfftp%mype3), MPI_DOUBLE_PRECISION, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (scalar and rank-1)\n", - "\u001b[01m\u001b[Kscatter_mod.f90:351:24:\u001b[m\u001b[K\n", - "\n", - " 351 | f_out(offset_in), sendcount(dfft%mype2), MPI_DOUBLE_PRECISION, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 638 | f_aux, sendcount(dfftp%mype3), MPI_DOUBLE_PRECISION, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "\u001b[01m\u001b[Kscatter_mod.f90:249:21:\u001b[m\u001b[K\n", - "\n", - " 249 | CALL MPI_SCATTERV( f_in, sendcount, displs, MPI_DOUBLE_PRECISION, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 637 | CALL MPI_SCATTERV( f_in(1,ibnd), sendcount, displs, MPI_DOUBLE_PRECISION, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "\u001b[01m\u001b[Kscatter_mod.f90:84:23:\u001b[m\u001b[K\n", - "\n", - " 84 | CALL MPI_GATHERV( f_in(offset_in) , recvcount(dfft%mype2), MPI_DOUBLE_PRECISION, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 520 | CALL MPI_GATHERV( f_in(offset_in,jbnd), recvcount(dfftp%mype2), MPI_DOUBLE_PRECISION, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scatter_2d.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_buffers.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scatter_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scatter_2d_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_ggen.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.DFTI.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.ESSL.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fftw_interfaces.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.FFTW.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.FFTW3.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.SX6.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.cuFFT.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scatter.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_parallel.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_parallel_2d.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_smallbox.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_smallbox_type.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_fwinv.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_interfaces.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_interpolate.f90\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c fftw.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c fftw_sp.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c fftw_dp.c\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_error.f90\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c fft_stick.c\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c tg_gather.f90\n", - "\u001b[01m\u001b[Ktg_gather.f90:77:36:\u001b[m\u001b[K\n", - "\n", - " 34 | CALL MPI_ALLREDUCE( MPI_IN_PLACE, tg_v, nsiz, MPI_DOUBLE_PRECISION, MPI_SUM, dffts%comm2, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 77 | CALL MPI_ALLREDUCE( MPI_IN_PLACE, tg_v, nsiz, MPI_DOUBLE_PRECISION, MPI_SUM, dffts%comm2, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_helper_subroutines.f90\n", - "\u001b[01m\u001b[Kfft_helper_subroutines.f90:154:42:\u001b[m\u001b[K\n", - "\n", - " 154 | CALL MPI_ALLREDUCE( MPI_IN_PLACE, tmp_rhos, SIZE(tmp_rhos), MPI_DOUBLE_PRECISION, MPI_SUM, desc%comm2, ierr )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 220 | CALL MPI_ALLREDUCE( MPI_IN_PLACE, tmp_rhos, 2*SIZE(tmp_rhos), MPI_DOUBLE_PRECISION, MPI_SUM, desc%comm2, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_param.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_support.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c stick_base.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_types.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c scatter_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scatter_2d.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_buffers.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scatter_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scatter_2d_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_ggen.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.DFTI.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.ESSL.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fftw_interfaces.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.FFTW.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.FFTW3.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.SX6.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.cuFFT.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scalar.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_scatter.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_parallel.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_parallel_2d.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_smallbox.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_smallbox_type.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_fwinv.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_interfaces.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_interpolate.f90\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c fftw.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c fftw_sp.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c fftw_dp.c\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_error.f90\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c fft_stick.c\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c tg_gather.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c fft_helper_subroutines.f90\n", "ar ruv libqefft.a scatter_mod.o fft_scatter_2d.o fft_scatter_gpu.o fft_scatter_2d_gpu.o fft_ggen.o fft_fwinv.o fft_scalar.o fft_scalar.DFTI.o fft_scalar.ESSL.o fft_scalar.FFTW.o fft_scatter.o fft_scalar.cuFFT.o fftw_interfaces.o fft_scalar.FFTW3.o fft_scalar.SX6.o fft_parallel.o fft_parallel_2d.o fft_interfaces.o fft_interpolate.o stick_base.o fftw.o fftw_sp.o fftw_dp.o fft_smallbox.o fft_smallbox_type.o fft_support.o fft_error.o fft_stick.o fft_types.o tg_gather.o fft_helper_subroutines.o fft_param.o fft_buffers.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libqefft.a\n", @@ -1011,66 +428,66 @@ "make[1]: Leaving directory '/content/q-e/FFTXlib'\n", "( cd upflib ; make TLDEPS= all || exit 1 )\n", "make[1]: Entering directory '/content/q-e/upflib'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_kinds.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_const.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c radial_grids.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c atom.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c pseudo_types.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_io.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_params.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_utils.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c gth.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c uspp_param.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c vloc_mod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_invmat.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_spinorb.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c uspp.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c uspp_data.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c gen_us_dj.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK 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-fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c uspp_data.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c gen_us_dj.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c gen_us_dy.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_us_0.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_us_b0.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c paw_variables.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_us_1.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_us_2_base.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_tab_atwfc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_tab_beta.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_tab_rho.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_tab_rhc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_tab_qrad.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c atomic_number.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c dqvan2.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c dylmr2.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c interp_atwfc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c interp_rhc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c interp_drhc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c qvan2.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_cpmd.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_fhi.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_ncpp.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c splinelib.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c xmltools.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_psml.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_upf_new.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_upf_v1.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_uspp.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_ps.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c spinor.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c sph_ind.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c sph_bes.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c simpsn.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_auxtools.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_parallel_include.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_error.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_ions.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_to_internal.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c write_upf_new.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c ylmr2.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c dom.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c wxml.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c dylmr2_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_us_2_base_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c ylmr2_gpu.f90\n", "ar ruv libupf.a vloc_mod.o gen_us_dj.o gen_us_dy.o init_us_0.o init_us_b0.o init_us_1.o init_us_2_base.o init_tab_atwfc.o init_tab_beta.o init_tab_rho.o init_tab_rhc.o init_tab_qrad.o atom.o atomic_number.o dqvan2.o dylmr2.o gth.o interp_atwfc.o interp_rhc.o interp_drhc.o paw_variables.o pseudo_types.o qvan2.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_data.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o dom.o wxml.o dylmr2_gpu.o init_us_2_base_gpu.o ylmr2_gpu.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libupf.a\n", @@ -1135,13 +552,13 @@ "a - init_us_2_base_gpu.o\n", "a - ylmr2_gpu.o\n", "ranlib libupf.a \n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c virtual_v2.f90\n", - "mpif90 -g -pthread -fopenmp -o virtual_v2.x virtual_v2.o atom.o atomic_number.o dqvan2.o dylmr2.o gth.o interp_atwfc.o interp_rhc.o interp_drhc.o paw_variables.o pseudo_types.o qvan2.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_data.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_gnu_thread -lmkl_core\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c casino_pp.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upfconv.f90\n", - "mpif90 -g -pthread -fopenmp -o upfconv.x upfconv.o casino_pp.o atom.o atomic_number.o dqvan2.o dylmr2.o gth.o interp_atwfc.o interp_rhc.o interp_drhc.o paw_variables.o pseudo_types.o qvan2.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_data.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_gnu_thread -lmkl_core\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c casino2upf.f90\n", - "mpif90 -g -pthread -fopenmp -o casino2upf.x casino2upf.o casino_pp.o atom.o atomic_number.o dqvan2.o dylmr2.o gth.o interp_atwfc.o interp_rhc.o interp_drhc.o paw_variables.o pseudo_types.o qvan2.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_data.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_gnu_thread -lmkl_core\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c virtual_v2.f90\n", + "mpif90 -g -o virtual_v2.x virtual_v2.o atom.o atomic_number.o dqvan2.o dylmr2.o gth.o interp_atwfc.o interp_rhc.o interp_drhc.o paw_variables.o pseudo_types.o qvan2.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_data.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_sequential -lmkl_core\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c casino_pp.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upfconv.f90\n", + "mpif90 -g -o upfconv.x upfconv.o casino_pp.o atom.o atomic_number.o dqvan2.o dylmr2.o gth.o interp_atwfc.o interp_rhc.o interp_drhc.o paw_variables.o pseudo_types.o qvan2.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_data.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_sequential -lmkl_core\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c casino2upf.f90\n", + "mpif90 -g -o casino2upf.x casino2upf.o casino_pp.o atom.o atomic_number.o dqvan2.o dylmr2.o gth.o interp_atwfc.o interp_rhc.o interp_drhc.o paw_variables.o pseudo_types.o qvan2.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_data.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_sequential -lmkl_core\n", "make[1]: Leaving directory '/content/q-e/upflib'\n", "cd install ; make -f extlibs_makefile libmbd\n", "make[1]: Entering directory '/content/q-e/install'\n", @@ -1152,7 +569,7 @@ "remote: Counting objects: 100% (112/112), done.\u001b[K\n", "remote: Compressing objects: 100% (64/64), done.\u001b[K\n", "remote: Total 64 (delta 42), reused 10 (delta 0), pack-reused 0\u001b[K\n", - "Unpacking objects: 100% (64/64), 29.22 KiB | 965.00 KiB/s, done.\n", + "Unpacking objects: 100% (64/64), 29.22 KiB | 1.17 MiB/s, done.\n", "From https://github.com/libmbd/libmbd\n", " * branch 82005cbb65bdf5d32ca021848eec8f19da956a77 -> FETCH_HEAD\n", "Submodule path 'external/mbd': checked out '82005cbb65bdf5d32ca021848eec8f19da956a77'\n", @@ -1160,64 +577,64 @@ "if test ! -d ../MBD; then \\\n", " mkdir ../MBD; \\\n", " cd /content/q-e//external/mbd/src; \\\n", - "export FXX=gfortran; export FXXOPT=\"-O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include \"; \\\n", + "export FXX=gfortran; export FXXOPT=\"-O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include \"; \\\n", "make -f ../../mbd.make; cp *.mod *.a /content/q-e//MBD; cd ../../.. ;fi\n", "make[2]: Entering directory '/content/q-e/external/mbd/src'\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_constants.f90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_gradients.f90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_utils.F90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_damping.F90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_formulas.f90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_lapack.f90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_vdw_param.f90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_geom.F90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_linalg.F90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_matrix.F90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_dipole.F90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_hamiltonian.F90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_rpa.F90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_scs.f90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_methods.F90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_ts.f90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_constants.f90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_gradients.f90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_utils.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_damping.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_formulas.f90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_lapack.f90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_vdw_param.f90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_geom.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_linalg.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_matrix.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_dipole.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_hamiltonian.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_rpa.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_scs.f90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_methods.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_ts.f90\n", "echo '#define MBD_VERSION_MAJOR 0' > version.h\n", "echo '#define MBD_VERSION_MINOR 10'>> version.h\n", "echo '#define MBD_VERSION_PATCH 0' >> version.h\n", "echo '#define MBD_VERSION \"0.10.0\"' >> version.h\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd.F90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_coulomb.f90\n", - "gfortran -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_c_api.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_coulomb.f90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_c_api.F90\n", "ar -r libmbd.a mbd.o mbd_c_api.o mbd_constants.o mbd_coulomb.o mbd_damping.o mbd_dipole.o mbd_formulas.o mbd_geom.o mbd_gradients.o mbd_hamiltonian.o mbd_lapack.o mbd_linalg.o mbd_matrix.o mbd_methods.o mbd_rpa.o mbd_scs.o mbd_ts.o mbd_utils.o mbd_vdw_param.o\n", "ar: creating libmbd.a\n", "make[2]: Leaving directory '/content/q-e/external/mbd/src'\n", "make[1]: Leaving directory '/content/q-e/install'\n", "( cd XClib ; make TLDEPS= all || exit 1 )\n", "make[1]: Entering directory '/content/q-e/XClib'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xclib_utils_and_para.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xclib_error.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_kind.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_constants.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c dft_setting_params.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_dft_list.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c dft_setting_routines.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_funct_corr_lda_lsda.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_funct_exch_lda_lsda.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_funct_corr_gga.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_funct_exch_gga.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_funct_mgga.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_beef_interface.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_drivers_lda_lsda.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_drivers_d_lda_lsda.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_drivers_gga.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_drivers_d_gga.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_drivers_mgga.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_wrapper_d_gga.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_wrapper_d_lda_lsda.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_wrapper_gga.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_wrapper_lda_lsda.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_wrapper_mgga.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_lib.f90\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c pbecor.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c beefun.c\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xclib_utils_and_para.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xclib_error.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_kind.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_constants.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c dft_setting_params.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_dft_list.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c dft_setting_routines.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_funct_corr_lda_lsda.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_funct_exch_lda_lsda.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_funct_corr_gga.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_funct_exch_gga.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_funct_mgga.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_beef_interface.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_drivers_lda_lsda.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_drivers_d_lda_lsda.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_drivers_gga.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_drivers_d_gga.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c qe_drivers_mgga.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_wrapper_d_gga.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_wrapper_d_lda_lsda.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_wrapper_gga.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_wrapper_lda_lsda.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_wrapper_mgga.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -c xc_lib.f90\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c pbecor.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c beefun.c\n", "ar ruv xc_lib.a xclib_utils_and_para.o xclib_error.o qe_kind.o qe_constants.o dft_setting_params.o dft_setting_routines.o qe_dft_list.o qe_funct_corr_lda_lsda.o qe_funct_exch_lda_lsda.o qe_funct_corr_gga.o qe_funct_exch_gga.o qe_funct_mgga.o xc_beef_interface.o qe_drivers_lda_lsda.o qe_drivers_d_lda_lsda.o qe_drivers_gga.o qe_drivers_d_gga.o qe_drivers_mgga.o xc_wrapper_d_gga.o xc_wrapper_d_lda_lsda.o xc_wrapper_gga.o xc_wrapper_lda_lsda.o xc_wrapper_mgga.o xc_lib.o pbecor.o beefun.o\n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating xc_lib.a\n", @@ -1251,62 +668,62 @@ "make[1]: Leaving directory '/content/q-e/XClib'\n", "( cd Modules ; make TLDEPS= all || exit 1 )\n", "make[1]: Entering directory '/content/q-e/Modules'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c kind.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c constants.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c parameters.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c control_flags.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c invmat.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c io_global.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c cell_base.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wannier_new.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c input_parameters.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_images.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c additional_kpoints.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_world.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c parser.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c autopilot.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c basic_algebra_routines.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_bands.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c noncol.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c recvec.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c becmod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c io_files.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c bfgs_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c bspline.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c bz_form.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c set_signal.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c check_stop.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c command_line_options.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c fft_base.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c compute_dipole.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c random_numbers.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c ions_base.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c constraints_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c coulomb_vcut.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c dist.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c electrons_base.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c environ_api_stub.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c environ_base_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c fox_init_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_pools.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qeh5_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c version.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c environment.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c extffield.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c fd_gradient.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c fft_rho.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c fft_wave.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c fsockets.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c xc_rVV10.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c xc_vdW_DF.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c funct.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c generate_function.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c gradutils.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c gvecw.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c lmdif.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c plugin_flags.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c makov_payne.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mdiis.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c kind.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c constants.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c parameters.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c control_flags.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c invmat.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c io_global.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c cell_base.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wannier_new.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c input_parameters.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_images.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c additional_kpoints.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_world.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c parser.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c autopilot.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c basic_algebra_routines.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_bands.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib 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-I/content/q-e//KS_Solvers -c fsockets.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c xc_rVV10.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c xc_vdW_DF.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c funct.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c generate_function.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c gradutils.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c gvecw.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c lmdif.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c plugin_flags.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c makov_payne.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mdiis.f90\n", "\u001b[01m\u001b[Kmdiis.f90:454:44:\u001b[m\u001b[K\n", "\n", " 452 | CALL daxpy(mdiist%nvec, +1.0_DP, mdiist%vbox(1, ibox2), 1, vadd, 1)\n", @@ -1371,162 +788,50 @@ " 451 | CALL dcopy(mdiist%nvec, mdiist%vres(1, ibox2), 1, vadd, 1)\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mm_dispersion.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_exx.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_global.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_wave.f90\n", - "\u001b[01m\u001b[Kmp_wave.f90:807:26:\u001b[m\u001b[K\n", - "\n", - " 807 | CALL MPI_ALLTOALLV( c_dist_pw, sendcount, sdispls, MPI_DOUBLE_COMPLEX, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 925 | CALL MPI_ALLTOALLV( c_dist_pw, sendcount, sdispls, MPI_DOUBLE_PRECISION, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "\u001b[01m\u001b[Kmp_wave.f90:598:27:\u001b[m\u001b[K\n", - "\n", - " 593 | CALL MPI_RECV( igl, ngl, MPI_INTEGER, ROOT, IP+NPROC, gid, istatus, IERR )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 598 | CALL MPI_RECV( ng_ip, 1, MPI_INTEGER, (ip-1), IP, gid, istatus, IERR )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (rank-1 and scalar)\n", - "\u001b[01m\u001b[Kmp_wave.f90:602:27:\u001b[m\u001b[K\n", - "\n", - " 592 | CALL MPI_SEND( ngl, 1 , MPI_INTEGER, ROOT, IP, gid,IERR)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 602 | CALL MPI_SEND( ig_ip, ng_ip, MPI_INTEGER, (ip-1), IP+NPROC, gid, IERR )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (scalar and rank-1)\n", - "\u001b[01m\u001b[Kmp_wave.f90:401:27:\u001b[m\u001b[K\n", - "\n", - " 401 | CALL MPI_SEND( ig_l2g, ngwl, MPI_INTEGER, ROOT, IP, gid,IERR)\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 592 | CALL MPI_SEND( ngl, 1 , MPI_INTEGER, ROOT, IP, gid,IERR)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (scalar and rank-1)\n", - "\u001b[01m\u001b[Kmp_wave.f90:402:27:\u001b[m\u001b[K\n", - "\n", - " 402 | CALL MPI_RECV( mill(1,1),3*ngwl, MPI_INTEGER, ROOT, IP+NPROC, gid, istatus, IERR )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 593 | CALL MPI_RECV( igl, ngl, MPI_INTEGER, ROOT, IP+NPROC, gid, istatus, IERR )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "\u001b[01m\u001b[Kmp_wave.f90:412:27:\u001b[m\u001b[K\n", - "\n", - " 412 | CALL MPI_SEND( mill_ip, 3*ngw_ip, MPI_INTEGER, (ip-1), IP+NPROC, gid, IERR )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 592 | CALL MPI_SEND( ngl, 1 , MPI_INTEGER, ROOT, IP, gid,IERR)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (scalar and rank-2)\n", - "\u001b[01m\u001b[Kmp_wave.f90:300:27:\u001b[m\u001b[K\n", - "\n", - " 300 | CALL MPI_SEND( ig_l2g, ngwl, MPI_INTEGER, ROOT, IP, gid,IERR)\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 592 | CALL MPI_SEND( ngl, 1 , MPI_INTEGER, ROOT, IP, gid,IERR)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (scalar and rank-1)\n", - "\u001b[01m\u001b[Kmp_wave.f90:301:27:\u001b[m\u001b[K\n", - "\n", - " 301 | CALL MPI_RECV( pw(1), ngwl, MPI_DOUBLE_COMPLEX, ROOT, IP+NPROC, gid, istatus, IERR )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 593 | CALL MPI_RECV( igl, ngl, MPI_INTEGER, ROOT, IP+NPROC, gid, istatus, IERR )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/INTEGER(4)).\n", - "\u001b[01m\u001b[Kmp_wave.f90:311:27:\u001b[m\u001b[K\n", - "\n", - " 311 | CALL MPI_SEND( pw_ip, ngw_ip, MPI_DOUBLE_COMPLEX, (ip-1), IP+NPROC, gid, IERR )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 592 | CALL MPI_SEND( ngl, 1 , MPI_INTEGER, ROOT, IP, gid,IERR)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/INTEGER(4)).\n", - "\u001b[01m\u001b[Kmp_wave.f90:191:27:\u001b[m\u001b[K\n", - "\n", - " 191 | CALL MPI_SEND( ig_l2g, ngwl, MPI_INTEGER, ROOT, IP, gid, IERR )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 592 | CALL MPI_SEND( ngl, 1 , MPI_INTEGER, ROOT, IP, gid,IERR)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (scalar and rank-1)\n", - "\u001b[01m\u001b[Kmp_wave.f90:192:27:\u001b[m\u001b[K\n", - "\n", - " 192 | CALL MPI_SEND( mill,3*ngwl, MPI_INTEGER, ROOT, IP+NPROC, gid, IERR )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 592 | CALL MPI_SEND( ngl, 1 , MPI_INTEGER, ROOT, IP, gid,IERR)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (scalar and rank-2)\n", - "\u001b[01m\u001b[Kmp_wave.f90:82:27:\u001b[m\u001b[K\n", - "\n", - " 82 | CALL MPI_SEND( ig_l2g, ngwl, MPI_INTEGER, ROOT, IP, gid, IERR )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 592 | CALL MPI_SEND( ngl, 1 , MPI_INTEGER, ROOT, IP, gid,IERR)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Rank mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (scalar and rank-1)\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c open_close_input_file.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c plugin_arguments.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c plugin_variables.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c pw_dot.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qmmm.f90\n", - "\u001b[01m\u001b[Kqmmm.f90:320:23:\u001b[m\u001b[K\n", - "\n", - " 270 | CALL mpi_recv( irecv_buf, 4, MPI_INTEGER, 0, QMMM_TAG_SIZE, qmmm_comm, MPI_STATUS_IGNORE, ierr )\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 320 | CALL mpi_recv( cell_mm, 9, MPI_DOUBLE_PRECISION, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/INTEGER(4)).\n", - "\u001b[01m\u001b[Kqmmm.f90:144:24:\u001b[m\u001b[K\n", - "\n", - " 144 | CALL mpi_send(nat_qm,1,MPI_INTEGER,0,QMMM_TAG_SIZE,qmmm_comm,ierr)\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 434 | CALL mpi_send(tmp_buf,3*nat_qm,MPI_DOUBLE_PRECISION, 0,QMMM_TAG_FORCE,qmmm_comm,ierr)\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (INTEGER(4)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wypos.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c read_cards.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c read_namelists.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c read_input.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c read_pseudo.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c recvec_subs.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c run_info.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c space_group.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c set_para_diag.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c set_vdw_corr.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c setqf.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c timestep.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c tsvdw.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mbdlib.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wannier_gw.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wavefunctions.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c ws_base.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c io_base.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qes_types_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qes_bcast_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qes_init_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qes_read_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qes_reset_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qes_write_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qes_libs_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qexsd_init.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qexsd_input.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qexsd.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c molecule_types.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qexsd_copy.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c hdf5_qe.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c xsf.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wyckoff.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c zvscal.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wave_gauge.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mm_dispersion.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_exx.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 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pw_dot.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qmmm.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wypos.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c read_cards.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c read_namelists.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c read_input.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c read_pseudo.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c recvec_subs.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c run_info.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c space_group.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c set_para_diag.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c set_vdw_corr.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c setqf.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c timestep.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c tsvdw.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mbdlib.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wannier_gw.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wavefunctions.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c ws_base.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c io_base.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qes_types_module.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules 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-I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c molecule_types.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qexsd_copy.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c hdf5_qe.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c xsf.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wyckoff.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c zvscal.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wave_gauge.f90\n", "\u001b[01m\u001b[Kwave_gauge.f90:154:58:\u001b[m\u001b[K\n", "\n", " 113 | call dgemm('T', 'N', nbnd, nbnd, 2*npw, 2.d0, t_plus, 2*npwx, t_zero, 2*npwx, 0.d0, sa, nbnd)\n", @@ -1535,57 +840,49 @@ " 154 | call dgemm('T', 'N', nbnd, nbnd, nbnd, 1.d0, sa, nbnd, sa, nbnd, 0.d0, ssa, nbnd)\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wavefunctions_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c becmod_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c becmod_subs_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c cuda_subroutines.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c random_numbers_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c atom_weight.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c capital.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c cryst_to_car.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c expint.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c generate_k_along_lines.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c has_xml.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c inpfile.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c int_to_char.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c latgen.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c linpack.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c matches.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c plot_io.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c radial_gradients.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c rgen.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c recips.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c remove_tot_torque.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c sort.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c trimcheck.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c test_input_file.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c date_and_tim.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c volume.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wgauss.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c w0gauss.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c w1gauss.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c deviatoric.f90\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c customize_signals.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c qmmm_aux.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c sockets.c\n", - "cc -O3 -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include -c stack.c\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_rism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c recvec_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c allocate_fft_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c err_rism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c lauefft.f90\n", - "\u001b[01m\u001b[Klauefft.f90:1074:22:\u001b[m\u001b[K\n", - "\n", - " 1074 | CALL MPI_REDUCE(cltmp(1), cltot(1), ntot, MPI_DOUBLE_COMPLEX, MPI_SUM, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "......\n", - " 1145 | CALL MPI_REDUCE(crtmp(1), crtot(1), ntot, MPI_DOUBLE_PRECISION, MPI_SUM, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c radfft.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c rism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvmol.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c chempot.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wavefunctions_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c becmod_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c becmod_subs_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c cuda_subroutines.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c random_numbers_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c atom_weight.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c capital.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c cryst_to_car.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c expint.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c generate_k_along_lines.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c has_xml.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c inpfile.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c int_to_char.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c latgen.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c linpack.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c matches.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c plot_io.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c radial_gradients.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c rgen.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c recips.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c remove_tot_torque.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c sort.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c trimcheck.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c test_input_file.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c date_and_tim.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c volume.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wgauss.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c w0gauss.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c w1gauss.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c deviatoric.f90\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c customize_signals.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c qmmm_aux.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c sockets.c\n", + "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c stack.c\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_rism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c recvec_3drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c allocate_fft_3drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c err_rism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c lauefft.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c radfft.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c rism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvmol.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c chempot.f90\n", "\u001b[01m\u001b[Kchempot.f90:97:58:\u001b[m\u001b[K\n", "\n", " 97 | CALL chempot_for_a_site(rismt%nr, ichempot, beta, rismt%hr(1, isite), rismt%csr(1, isite), &\n", @@ -1596,8 +893,8 @@ " 99 | CALL chempot_for_a_site(rismt%nr, CHEMPOT_GF, beta, rismt%hr(1, isite), rismt%csr(1, isite), &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array passed to array dummy argument ‘\u001b[01m\u001b[Khr\u001b[m\u001b[K’ at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c chempot_lauerism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c closure.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c chempot_lauerism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c closure.f90\n", "\u001b[01m\u001b[Kclosure.f90:54:13:\u001b[m\u001b[K\n", "\n", " 54 | & rismt%usr(1, 1), rismt%hr(1, 1), rismt%csr(1, 1), rismt%gr(1, 1))\n", @@ -1628,68 +925,68 @@ " 85 | & rismt%usg0(1, 1), rismt%hg0(1, 1), rismt%csdg0(1, 1), rismt%gg0(1, 1))\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array passed to array dummy argument ‘\u001b[01m\u001b[Kur\u001b[m\u001b[K’ at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c corrdipole_laue.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c correctat0_vv.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c corrgxy0_laue.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c cryst_to_car_2d.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c data_structure_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c do_1drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c do_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c do_lauerism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_1drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_lauedipole.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_lauegxy0.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_lauelong.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_lauerism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_laueshort.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_lauevoid.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c guess_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c init_1drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c xml_io_rism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c io_rism_xml.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c rism1d_facade.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c molecule_const.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solute.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c init_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c read_mol.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c read_solv.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c input_1drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c rism3d_facade.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c input_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c lauefft_subs.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c lj_forcefield.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c lj_solute.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c molecorr_vv.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c molebridge_vv.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_swap_ax_rism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c normalize_lauerism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c plot_rism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c potential_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c potential_esm.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c potential_vv.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c print_chempot_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c print_chempot_lauerism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c print_chempot_vv.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c print_corr_vv.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvavg.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c print_solvavg.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c rms_residual.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c scale_fft_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c scale_fft_lauerism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_esm.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_force.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_lauerism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_pbc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_stress.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c summary_1drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c summary_3drism.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c suscept_g0.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c suscept_laue.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c suscept_laueint.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src 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cryst_to_car_2d.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c data_structure_3drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c do_1drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD 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-I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_lauelong.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_lauerism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_laueshort.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c eqn_lauevoid.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c guess_3drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c init_1drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c xml_io_rism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c io_rism_xml.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c rism1d_facade.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib 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-I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c lj_forcefield.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c lj_solute.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c molecorr_vv.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c molebridge_vv.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_swap_ax_rism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c normalize_lauerism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c plot_rism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c potential_3drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c potential_esm.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c potential_vv.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c print_chempot_3drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c print_chempot_lauerism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c print_chempot_vv.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c print_corr_vv.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvavg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c print_solvavg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c rms_residual.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c scale_fft_3drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c scale_fft_lauerism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_3drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_esm.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_force.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_lauerism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_pbc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c solvation_stress.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c summary_1drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c summary_3drism.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c suscept_g0.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c suscept_laue.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c suscept_laueint.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c suscept_vv.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c write_rism_type.f90\n", "ar ruv libqemod.a additional_kpoints.o autopilot.o basic_algebra_routines.o becmod.o bfgs_module.o bspline.o bz_form.o cell_base.o check_stop.o command_line_options.o compute_dipole.o constants.o constraints_module.o control_flags.o coulomb_vcut.o dist.o electrons_base.o environ_base_module.o environment.o extffield.o fd_gradient.o fft_base.o fft_rho.o fft_wave.o fsockets.o funct.o generate_function.o gradutils.o gvecw.o input_parameters.o invmat.o io_files.o io_global.o ions_base.o kind.o lmdif.o makov_payne.o mdiis.o mm_dispersion.o mp_bands.o mp_exx.o mp_global.o mp_images.o mp_pools.o mp_wave.o mp_world.o noncol.o open_close_input_file.o parameters.o parser.o plugin_flags.o plugin_arguments.o plugin_variables.o pw_dot.o qmmm.o random_numbers.o read_cards.o read_input.o read_namelists.o read_pseudo.o recvec.o recvec_subs.o run_info.o space_group.o set_para_diag.o set_signal.o set_vdw_corr.o setqf.o timestep.o tsvdw.o mbdlib.o version.o wannier_gw.o wannier_new.o wavefunctions.o ws_base.o xc_vdW_DF.o xc_rVV10.o io_base.o qes_types_module.o qes_libs_module.o qes_write_module.o qes_read_module.o qes_reset_module.o qes_init_module.o qes_bcast_module.o qexsd.o qexsd_copy.o qexsd_init.o qexsd_input.o hdf5_qe.o qeh5_module.o fox_init_module.o xsf.o wyckoff.o wypos.o zvscal.o wave_gauge.o wavefunctions_gpu.o becmod_gpu.o becmod_subs_gpu.o cuda_subroutines.o random_numbers_gpu.o atom_weight.o capital.o cryst_to_car.o expint.o generate_k_along_lines.o has_xml.o inpfile.o int_to_char.o latgen.o linpack.o matches.o plot_io.o radial_gradients.o rgen.o recips.o remove_tot_torque.o sort.o trimcheck.o test_input_file.o date_and_tim.o volume.o wgauss.o w0gauss.o w1gauss.o deviatoric.o customize_signals.o qmmm_aux.o sockets.o stack.o allocate_fft_3drism.o chempot.o chempot_lauerism.o closure.o corrdipole_laue.o correctat0_vv.o corrgxy0_laue.o cryst_to_car_2d.o data_structure_3drism.o do_1drism.o do_3drism.o do_lauerism.o eqn_1drism.o eqn_3drism.o eqn_lauedipole.o eqn_lauegxy0.o eqn_lauelong.o eqn_lauerism.o eqn_laueshort.o eqn_lauevoid.o err_rism.o guess_3drism.o init_1drism.o init_3drism.o input_1drism.o input_3drism.o io_rism_xml.o lauefft.o lauefft_subs.o lj_forcefield.o lj_solute.o molecorr_vv.o molebridge_vv.o molecule_const.o molecule_types.o mp_rism.o mp_swap_ax_rism.o normalize_lauerism.o plot_rism.o potential_3drism.o potential_esm.o potential_vv.o print_chempot_3drism.o print_chempot_lauerism.o print_chempot_vv.o print_corr_vv.o print_solvavg.o radfft.o read_mol.o read_solv.o recvec_3drism.o rism.o rism1d_facade.o rism3d_facade.o rms_residual.o scale_fft_3drism.o scale_fft_lauerism.o solute.o solvation_3drism.o solvation_esm.o solvation_force.o solvation_lauerism.o solvation_pbc.o solvation_stress.o solvavg.o solvmol.o summary_1drism.o summary_3drism.o suscept_g0.o suscept_laue.o suscept_laueint.o suscept_vv.o write_rism_type.o xml_io_rism.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libqemod.a\n", @@ -1908,7 +1205,7 @@ "if test -d Davidson_RCI ; then \\\n", "( cd Davidson_RCI ; make all || exit 1 ) ; fi\n", "make[2]: Entering directory '/content/q-e/KS_Solvers/Davidson_RCI'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c david_rci.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c david_rci.f90\n", "ar ruv libdavid_rci.a david_rci.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libdavid_rci.a\n", @@ -1918,7 +1215,7 @@ "if test -d Davidson ; then \\\n", " ( cd Davidson ; make all || exit 1 ) ; fi\n", "make[2]: Entering directory '/content/q-e/KS_Solvers/Davidson'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c cegterg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c cegterg.f90\n", "\u001b[01m\u001b[Kcegterg.f90:1652:66:\u001b[m\u001b[K\n", "\n", " 207 | CALL ZGEMM( 'C','N', nbase, my_n, kdim, ONE, psi, kdmx, hpsi(1,n_start), kdmx, ZERO, hc(1,n_start), nvecx )\n", @@ -1935,7 +1232,7 @@ " 1593 | v(1,ir), kdmx, w(1,ic), kdmx, ZERO, work, nx )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c regterg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c regterg.f90\n", "\u001b[01m\u001b[Kregterg.f90:318:79:\u001b[m\u001b[K\n", "\n", " 204 | CALL DGEMM( 'T','N', nbase, my_n, npw2, 2.D0 , psi, npwx2, hpsi(1,n_start), npwx2, 0.D0, hr(1,n_start), nvecx )\n", @@ -1960,8 +1257,8 @@ " 1498 | v(1,ir), npwx2, w(1,ic), npwx2, 0.D0, work, nx )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c cegterg_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c regterg_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c cegterg_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c regterg_gpu.f90\n", "ar ruv libdavid.a cegterg.o regterg.o cegterg_gpu.o regterg_gpu.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libdavid.a\n", @@ -1974,20 +1271,22 @@ "if test -d CG ; then \\\n", " ( cd CG ; make all || exit 1 ) ; fi\n", "make[2]: Entering directory '/content/q-e/KS_Solvers/CG'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ccgdiagg.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c rcgdiagg.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c rcgdiagg_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ccgdiagg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c rcgdiagg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c rcgdiagg_gpu.f90\n", "\u001b[01m\u001b[Krcgdiagg_gpu.f90:151:123:\u001b[m\u001b[K\n", "\n", - " 151 | CALL cgcudaDGEMV( 'T', npw2, m_end-m_start+1, 2.D0, psi_d(1,m_start), npwx2, spsi_d, 1, 0.D0, lagrange_d(m_start), 1 )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + " 151 | , m_end-m_start+1, 2.D0, psi_d(1,m_start), npwx2, spsi_d, 1, 0.D0, lagrange_d(m_start), 1 )\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch in argument ‘\u001b[01m\u001b[Ka\u001b[m\u001b[K’ at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K; passed COMPLEX(8) to REAL(8)\n", "\u001b[01m\u001b[Krcgdiagg_gpu.f90:259:124:\u001b[m\u001b[K\n", "\n", - " 259 | CALL cgcudaDGEMV( 'T', npw2, m_end-m_start+1, 2.D0, psi_d(1,m_start), npw2, scg_d, 1, 0.D0, lagrange_d(m_start), 1 )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + " 259 | w2, m_end-m_start+1, 2.D0, psi_d(1,m_start), npw2, scg_d, 1, 0.D0, lagrange_d(m_start), 1 )\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch in argument ‘\u001b[01m\u001b[Ka\u001b[m\u001b[K’ at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K; passed COMPLEX(8) to REAL(8)\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ccgdiagg_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ccgdiagg_gpu.f90\n", "\u001b[01m\u001b[Kccgdiagg_gpu.f90:235:12:\u001b[m\u001b[K\n", "\n", " 235 | e(m) = ksDdot( kdim2, psi_d(1,m), 1, hpsi_d, 1 )\n", @@ -2040,8 +1339,8 @@ "if test -d PPCG ; then \\\n", " ( cd PPCG ; make all || exit 1 ) ; fi\n", "make[2]: Entering directory '/content/q-e/KS_Solvers/PPCG'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c generic_cublas.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ppcg_gamma.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c generic_cublas.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ppcg_gamma.f90\n", "\u001b[01m\u001b[Kppcg_gamma.f90:159:72:\u001b[m\u001b[K\n", "\n", " 146 | CALL DGEMM('T','N', nbnd, my_n, npw2, 2.D0, psi, npwx2, hpsi(1,n_start), npwx2, 0.D0, G(1,n_start), nbnd)\n", @@ -2074,7 +1373,7 @@ " 1414 | CALL DGER( nr, nc, -1.D0, v(1,ir), npwx2, w(1,ic), npwx2, work, nx )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ppcg_gamma_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ppcg_gamma_gpu.f90\n", "\u001b[01m\u001b[Kppcg_gamma_gpu.f90:187:78:\u001b[m\u001b[K\n", "\n", " 175 | CALL gpu_DGEMM('T','N', nbnd, my_n, npw2, 2.D0, psi_d, npwx2, hpsi_d(1,n_start), npwx2, 0.D0, G_d(1,n_start), nbnd)\n", @@ -2095,8 +1394,9 @@ "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Missing actual argument for argument ‘\u001b[01m\u001b[K_formal_37\u001b[m\u001b[K’ at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K\n", "\u001b[01m\u001b[Kppcg_gamma_gpu.f90:723:92:\u001b[m\u001b[K\n", "\n", - " 723 | call gpu_threaded_assign( buffer1_d, buffer_d, npwx, nact, .false., act_idx_d )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + " 723 | call gpu_threaded_assign( buffer1_d, buffer_d, npwx, nact, .false., act_idx_d )\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Missing actual argument for argument ‘\u001b[01m\u001b[K_formal_37\u001b[m\u001b[K’ at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K\n", "\u001b[01m\u001b[Kppcg_gamma_gpu.f90:1836:68:\u001b[m\u001b[K\n", "\n", @@ -2122,7 +1422,7 @@ " 1831 | IF ( gstart == 2 ) CALL DGER( nwanted, nwanted, -1.D0, psi_t, npwx2, hpsi_t, npwx2, G, nbnd )\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ppcg_k.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ppcg_k.f90\n", "\u001b[01m\u001b[Kppcg_k.f90:1374:53:\u001b[m\u001b[K\n", "\n", " 147 | CALL ZGEMM('C','N', nbnd, my_n, kdim, C_ONE, psi, kdimx, hpsi(1,n_start), kdimx, C_ZERO, G(1,n_start), nbnd)\n", @@ -2131,7 +1431,7 @@ " 1374 | CALL ZGEMM( 'C','N', nr, nc, kdim, C_ONE, v(1,ir), kdimx, w(1,ic), kdimx, C_ZERO, work, nx )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ppcg_k_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c ppcg_k_gpu.f90\n", "\u001b[01m\u001b[Kppcg_k_gpu.f90:1664:53:\u001b[m\u001b[K\n", "\n", " 1664 | CALL ZGEMM( 'C','N', nr, nc, kdim, C_ONE, v(1,ir), kdimx, w(1,ic), kdimx, C_ZERO, work, nx )\n", @@ -2153,11 +1453,11 @@ "if test -d ParO ; then \\\n", " ( cd ParO ; make all || exit 1 ) ; fi\n", "make[2]: Entering directory '/content/q-e/KS_Solvers/ParO'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c paro_gamma.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c paro_gamma_new.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c paro_k.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c paro_k_new.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c pcg_gamma.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c paro_gamma.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c paro_gamma_new.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c paro_k.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c paro_k_new.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c pcg_gamma.f90\n", "\u001b[01m\u001b[Kpcg_gamma.f90:118:51:\u001b[m\u001b[K\n", "\n", " 115 | CALL DGEMV( 'T', npw2, nbnd, 2.0_DP, spsi0, npwx2, z, 1, 0.0_DP, spsi0vec, 1 )\n", @@ -2166,8 +1466,8 @@ " 118 | CALL DGEMV( 'N', npw2, nbnd, -1.D0, psi0, npwx2, spsi0vec, 1, 1.0_DP, z, 1 )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c pcg_k.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c bpcg_gamma.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c pcg_k.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c bpcg_gamma.f90\n", "\u001b[01m\u001b[Kbpcg_gamma.f90:164:66:\u001b[m\u001b[K\n", "\n", " 161 | CALL MYDGEMM( 'T','N', nbnd,nnew,npw2, 2.D0, spsi0, npwx2, z(:,nactive+1), npwx2, 0.D0, spsi0vec, nbnd )\n", @@ -2176,7 +1476,7 @@ " 164 | CALL MYDGEMM( 'N','N', npw2,nnew,nbnd,-1.D0, psi0, npwx2, spsi0vec, nbnd, 1.D0, z(:,nactive+1), npwx2 )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c bpcg_k.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I. -c bpcg_k.f90\n", "ar ruv libparo.a paro_gamma.o paro_gamma_new.o paro_k.o paro_k_new.o pcg_gamma.o pcg_k.o bpcg_gamma.o bpcg_k.o\n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libparo.a\n", @@ -2193,9 +1493,9 @@ "if test -d RMM ; then \\\n", " ( cd RMM ; make all || exit 1 ) ; fi\n", "make[2]: Entering directory '/content/q-e/KS_Solvers/RMM'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../PPCG -I ../../UtilXlib -I. -c crmmdiagg.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../PPCG -I ../../UtilXlib -I. -c crmmdiagg_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../PPCG -I ../../UtilXlib -I. -c rrmmdiagg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../PPCG -I ../../UtilXlib -I. -c crmmdiagg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../PPCG -I ../../UtilXlib -I. -c crmmdiagg_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../PPCG -I ../../UtilXlib -I. -c rrmmdiagg.f90\n", "\u001b[01m\u001b[Krrmmdiagg.f90:696:29:\u001b[m\u001b[K\n", "\n", " 696 | vnrm = SQRT( DDOT( ndim, vr, 1, u1, 1 ) )\n", @@ -2212,7 +1512,7 @@ " 690 | CALL DGEMV( 'N', ndim, kdim, 1._DP, x1, ndim, h3(:,imin), 1, 0._DP, vr, 1 )\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../PPCG -I ../../UtilXlib -I. -c rrmmdiagg_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../PPCG -I ../../UtilXlib -I. -c rrmmdiagg_gpu.f90\n", "ar ruv librmm.a crmmdiagg.o crmmdiagg_gpu.o rrmmdiagg.o rrmmdiagg_gpu.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating librmm.a\n", @@ -2225,7 +1525,7 @@ "if test -d DENSE ; then \\\n", " ( cd DENSE ; make all || exit 1 ) ; fi\n", "make[2]: Entering directory '/content/q-e/KS_Solvers/DENSE'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_HSpsi_gamma.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_HSpsi_gamma.f90\n", "\u001b[01m\u001b[Krotate_HSpsi_gamma.f90:145:58:\u001b[m\u001b[K\n", "\n", " 88 | CALL MYDGEMM( 'T','N', nstart,my_n,kdim, 2.D0, psi,kdmx, hpsi(1,n_start),kdmx, 0.D0, hh(1,n_start),nstart )\n", @@ -2242,7 +1542,7 @@ " 498 | CALL DGEMM( 'N', 'N', kdim, my_nbnd, nstart, 1.D0, psi, kdmx, v, nstart, 0.D0, aux, kdmx )\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_HSpsi_k.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_HSpsi_k.f90\n", "\u001b[01m\u001b[Krotate_HSpsi_k.f90:429:63:\u001b[m\u001b[K\n", "\n", " 429 | CALL ZGEMM( 'C','N', nr,nc,my_kdim, ( 1.D0, 0.D0 ), v(npw_s,ir),kdmx, w(npw_s,ic),kdmx, ( 0.D0, 0.D0 ), work,nx )\n", @@ -2251,7 +1551,7 @@ " 524 | CALL ZGEMM( 'N', 'N', kdim, my_nbnd, nstart, (1.D0,0.D0), psi, kdmx, v, nstart, (0.D0,0.D0), aux, kdmx )\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_wfc_gamma.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_wfc_gamma.f90\n", "\u001b[01m\u001b[Krotate_wfc_gamma.f90:128:70:\u001b[m\u001b[K\n", "\n", " 88 | CALL DGEMM( 'T','N', nstart, my_n, npw2, 2.D0, psi, npwx2, aux(1,n_start), npwx2, 0.D0, hr(1,n_start), nstart )\n", @@ -2276,7 +1576,7 @@ " 335 | CALL DGER( nr, nc, -1.D0, v(1,ir), npwx2, w(1,ic), npwx2, work, nx )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_wfc_k.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_wfc_k.f90\n", "\u001b[01m\u001b[Krotate_wfc_k.f90:327:64:\u001b[m\u001b[K\n", "\n", " 87 | call ZGEMM( 'C','N', nstart, my_n, kdim, (1.D0,0.D0), psi, kdmx, aux(1,n_start), kdmx, (0.D0,0.D0), hc(1,n_start), nstart )\n", @@ -2285,22 +1585,25 @@ " 327 | CALL ZGEMM( 'C', 'N', nr, nc, kdim, ( 1.D0, 0.D0 ), v(1,ir), kdmx, w(1,ic), kdmx, ( 0.D0, 0.D0 ), work, nx )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_driver.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_wfc_gamma_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_driver.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_wfc_gamma_gpu.f90\n", "\u001b[01m\u001b[Krotate_wfc_gamma_gpu.f90:107:95:\u001b[m\u001b[K\n", "\n", - " 107 | npwx2, aux_d(1,n_start), npwx2, hr_d(1,n_start), nstart )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + " 107 | npwx2, aux_d(1,n_start), npwx2, hr_d(1,n_start), nstart )\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch in argument ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K; passed COMPLEX(8) to REAL(8)\n", "\u001b[01m\u001b[Krotate_wfc_gamma_gpu.f90:121:98:\u001b[m\u001b[K\n", "\n", - " 121 | npwx2, aux_d(1,n_start), npwx2, sr_d(1,n_start), nstart )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + " 121 | npwx2, aux_d(1,n_start), npwx2, sr_d(1,n_start), nstart )\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch in argument ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K; passed COMPLEX(8) to REAL(8)\n", "\u001b[01m\u001b[Krotate_wfc_gamma_gpu.f90:129:98:\u001b[m\u001b[K\n", "\n", - " 129 | npwx2, psi_d(1,n_start), npwx2, sr_d(1,n_start), nstart )\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + " 129 | npwx2, psi_d(1,n_start), npwx2, sr_d(1,n_start), nstart )\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch in argument ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K; passed COMPLEX(8) to REAL(8)\n", "\u001b[01m\u001b[Krotate_wfc_gamma_gpu.f90:154:28:\u001b[m\u001b[K\n", "\n", @@ -2310,9 +1613,9 @@ " 154 | npwx2, vr_d(n_start,1), nstart, 0.D0, aux_d, npwx2 )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_wfc_k_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_driver_cuf.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_xpsi_k.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_wfc_k_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_driver_cuf.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_xpsi_k.f90\n", "\u001b[01m\u001b[Krotate_xpsi_k.f90:422:64:\u001b[m\u001b[K\n", "\n", " 110 | psi, kdmx, hpsi(1,n_start), kdmx, (0.D0, 0.D0), hc(1,n_start), nstart )\n", @@ -2321,8 +1624,8 @@ " 422 | CALL ZGEMM( 'C', 'N', nr, nc, kdim, ( 1.D0, 0.D0 ), v(1,ir), kdmx, w(1,ic), kdmx, ( 0.D0, 0.D0 ), work, nx )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_xpsi_k_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_xpsi_gamma.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_xpsi_k_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_xpsi_gamma.f90\n", "\u001b[01m\u001b[Krotate_xpsi_gamma.f90:176:42:\u001b[m\u001b[K\n", "\n", " 111 | psi, npwx2, hpsi(1,n_start), npwx2, 0.D0, hr(1,n_start), nstart )\n", @@ -2347,7 +1650,7 @@ " 434 | CALL DGER( nr, nc, -1.D0, v(1,ir), npwx2, w(1,ic), npwx2, work, nx )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_xpsi_gamma_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c rotate_xpsi_gamma_gpu.f90\n", "\u001b[01m\u001b[Krotate_xpsi_gamma_gpu.f90:194:44:\u001b[m\u001b[K\n", "\n", " 126 | npwx2, hpsi_d(1,n_start), npwx2, 0.D0, hr_d(1,n_start), nstart )\n", @@ -2356,7 +1659,7 @@ " 194 | tpsi_d(1,n_start), npwx2, vr_d(n_start,1), nstart, 0.D0, evc_d, npwx2 )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c gram_schmidt_gamma.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c gram_schmidt_gamma.f90\n", "\u001b[01m\u001b[Kgram_schmidt_gamma.f90:358:16:\u001b[m\u001b[K\n", "\n", " 336 | spsi(1,jbnd_start), npwx2, 0._DP, sr(ibnd_start,jbnd_start), ibnd_size )\n", @@ -2373,9 +1676,9 @@ " 233 | sr(ibnd_start), 1, 1._DP, phi(1,ibnd), 1 )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c gram_schmidt_k.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c gram_schmidt_gamma_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c gram_schmidt_k_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c gram_schmidt_k.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c gram_schmidt_gamma_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I ../../LAXlib -I ../../UtilXlib -I ../../Modules -I. -c gram_schmidt_k_gpu.f90\n", "ar ruv libdense.a rotate_HSpsi_gamma.o rotate_HSpsi_k.o rotate_wfc_gamma.o rotate_wfc_k.o rotate_driver.o rotate_wfc_gamma_gpu.o rotate_wfc_k_gpu.o rotate_driver_cuf.o rotate_xpsi_k.o rotate_xpsi_k_gpu.o rotate_xpsi_gamma.o rotate_xpsi_gamma_gpu.o gram_schmidt_gamma.o gram_schmidt_k.o gram_schmidt_gamma_gpu.o gram_schmidt_k_gpu.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libdense.a\n", @@ -2445,16 +1748,16 @@ "a - PPCG/ppcg_gamma_gpu.o\n", "a - PPCG/ppcg_k_gpu.o\n", "ranlib libks_solvers.a\n", - "cpp -P -traditional -Uvector -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I. -I/content/q-e//include ks_solver_interfaces.h -o ks_solver_interfaces.fh\n", + "cpp -P -traditional -Uvector -D__FFTW3 -I. -I/content/q-e//include ks_solver_interfaces.h -o ks_solver_interfaces.fh\n", "make[1]: Leaving directory '/content/q-e/KS_Solvers'\n", "( cd dft-d3 ; make TLDEPS= all || exit 1 )\n", "make[1]: Entering directory '/content/q-e/dft-d3'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c common.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c sizes.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c pars.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c core.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c api.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c dftd3_qe.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c common.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c sizes.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c pars.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c core.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c api.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c dftd3_qe.f90\n", "ar ruv libdftd3qe.a common.o sizes.o pars.o core.o api.o dftd3_qe.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libdftd3qe.a\n", @@ -2471,16 +1774,16 @@ "make[1]: Entering directory '/content/q-e/PW'\n", "( cd src ; make libpw.a || exit 1 )\n", "make[2]: Entering directory '/content/q-e/PW/src'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pwcom.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pwcom_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c start_k.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c symm_base.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c a2fmod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_bfield.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c extfield.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_efield.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_dmft_occ.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_vuspsi.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pwcom.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pwcom_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c start_k.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c symm_base.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c a2fmod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_bfield.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c extfield.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_efield.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_dmft_occ.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_vuspsi.f90\n", "\u001b[01m\u001b[Kadd_vuspsi.f90:141:57:\u001b[m\u001b[K\n", "\n", " 125 | deeq(1,1,na,current_spin), nhm, &\n", @@ -2489,12 +1792,12 @@ " 141 | CALL DGEMM( 'N', 'N', ( 2 * n ), m, nkb, 1.D0, vkb, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_gatefield.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_paw_to_deeq.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c atomic_wfc_mod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c ldaU.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c buffers.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c paw_onecenter.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_gatefield.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_paw_to_deeq.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c atomic_wfc_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c ldaU.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c buffers.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c paw_onecenter.f90\n", "\u001b[01m\u001b[Kpaw_onecenter.f90:230:64:\u001b[m\u001b[K\n", "\n", " 230 | CALL simpson( kkbeta, rho_lm(1,lm,is), g(i%t)%rab(1), integral )\n", @@ -2511,7 +1814,7 @@ " 1637 | g(i%t)%rab, integral_r )\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c scf_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c scf_mod.f90\n", "\u001b[01m\u001b[Kscf_mod.f90:687:56:\u001b[m\u001b[K\n", "\n", " 680 | CALL DCOPY(rlen_rho,rho%of_g,1,io_buffer(start_rho),1)\n", @@ -2528,12 +1831,12 @@ " 707 | IF (lda_plus_u_co) CALL DCOPY(rlen_ldaU,io_buffer(start_ldaU),1,rho%ns, 1)\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_vhub_to_deeq.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c scf_mod_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c realus.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c addusdens.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c addusforce.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c addusstress.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_vhub_to_deeq.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c scf_mod_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c realus.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c addusdens.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c addusforce.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c addusstress.f90\n", "\u001b[01m\u001b[Kaddusstress.f90:209:31:\u001b[m\u001b[K\n", "\n", " 174 | qgm, 2*ngm_l, tbecsum, nij, 0.0_dp, aux2, 2*ngm_l )\n", @@ -2542,128 +1845,112 @@ " 209 | aux2, 2*ngm_l, 0.0_dp, fac, 3 )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_fft.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_locpot.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_nlpot.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_wfc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c atomic_rho.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c atomic_wfc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c average_pp.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c acfdt_in_pw.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c newd.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c beef.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c bp_mod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c init_us_2.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c bp_c_phase.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c bp_strings.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c g_psi_mod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c g_psi_mod_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c exx_base.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_common_mod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_ewald_mod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_force_mod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_hartree_mod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_local_mod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_stres_mod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c dynamics_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_dynamics.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_relaxation.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c rism_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c gcscf_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_enums.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_indices.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_wavefunction.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_forces.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_input.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_context.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_base.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c additional_cusolver_subs.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_wavefunction_subs.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_occupations.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_wavefunction_subs_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_wfcO_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_wfcO.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_functions.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_functions_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sic.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c scissor.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c c_bands.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c c_phase_field.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c orbm_kubo.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c cdiagh.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c Coul_cut_2D.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c exx_band.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c paw_exx.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c us_exx.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c exx.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c paw_symmetry.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c paw_init.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c symme.f90\n", - "\u001b[01m\u001b[Ksymme.f90:787:25:\u001b[m\u001b[K\n", - "\n", - " 778 | CALL mpi_alltoallv (rhog (1,is) , sendcnt, sdispls, MPI_DOUBLE_COMPLEX,&\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 787 | CALL mpi_alltoallv ( g , sendcnt, sdispls, MPI_DOUBLE_PRECISION, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c tetra.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c xdm_dispersion.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c clean_pw.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c close_files.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c commutator_Hx_psi.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c commutator_Vhubx_psi.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c compute_becsum.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c compute_deff.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c compute_dip.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c compute_rho.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c compute_qdipol.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c compute_qdipol_so.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c compute_ux.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c coset.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c d_matrix.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c data_structure.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c divide_class.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c divide_class_so.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c divide_et_impera.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c rotate_wfc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c update_pot.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c run_driver.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c efermig.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c efermit.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c environ_io_stub.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c environ_pw_module.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c two_chem.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c io_rho_xml.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c loc_scdm.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c loc_scdm_k.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c newd_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c electrons.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c eqvect.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c martyna_tuckerman.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c ewald.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c ewald_dipole.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_capacitance.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_dyn_calcavg.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_dyn_printavg.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_hessian.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_input.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c find_group.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c forces_bp_efield.f90\n", - "\u001b[01m\u001b[Kforces_bp_efield.f90:597:43:\u001b[m\u001b[K\n", - "\n", - " 595 | CALL MPI_ALLTOALL( aux_proc, max_aux, MPI_DOUBLE_COMPLEX, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - " 596 | aux_rcv, max_aux, MPI_DOUBLE_COMPLEX, intra_bgrp_comm, ierr )\n", - " 597 | CALL MPI_ALLTOALL( aux_proc_ind, max_aux, MPI_INTEGER, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (INTEGER(4)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_cc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_corr.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_ew.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_hub.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_fft.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_locpot.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_nlpot.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_wfc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c atomic_rho.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c atomic_wfc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c average_pp.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c acfdt_in_pw.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c newd.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c beef.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c bp_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c init_us_2.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c bp_c_phase.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c bp_strings.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c g_psi_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c g_psi_mod_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c exx_base.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_common_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_ewald_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_force_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_hartree_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_local_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm_stres_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c esm.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c dynamics_module.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_dynamics.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_relaxation.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c rism_module.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_module.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c gcscf_module.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_enums.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_indices.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_wavefunction.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_forces.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_input.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp 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-I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c compute_rho.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c compute_qdipol.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c compute_qdipol_so.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c compute_ux.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c coset.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c d_matrix.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c data_structure.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c divide_class.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c divide_class_so.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c divide_et_impera.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c rotate_wfc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c update_pot.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c run_driver.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c efermig.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c efermit.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c environ_io_stub.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c environ_pw_module.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c two_chem.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c io_rho_xml.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c loc_scdm.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c loc_scdm_k.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c newd_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c electrons.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c eqvect.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c martyna_tuckerman.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c ewald.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c ewald_dipole.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_capacitance.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_dyn_calcavg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_dyn_printavg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_hessian.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c fcp_input.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c find_group.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c forces_bp_efield.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_cc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_corr.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_ew.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_hub.f90\n", "\u001b[01m\u001b[Kforce_hub.f90:2009:20:\u001b[m\u001b[K\n", "\n", " 1880 | CALL MYDGEMM( 'T','N',ldim, nbnd, 2*npw, 2.0_DP, dwfc, 2*npwx, spsi, &\n", @@ -2672,111 +1959,95 @@ " 2009 | wfatdbeta, nwfcU, betapsi(1,nb_s), nh(nt), 1.0_dp, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_lc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_us.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_forces_subs.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c forces.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c g_psi.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c gcscf_input.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c gen_at_dj.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c gen_at_dy.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c get_locals.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c gk_sort.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c gradcorr.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c gweights.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c g2_kin.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c hs_psi.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c hs_1psi.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c h_epsi_her_apply.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c h_epsi_her_set.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c h_psi.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c h_psi_meta.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c hinit0.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c hinit1.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c hubbard.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c init_ns.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c init_q_aeps.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c init_run.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c init_vloc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c input.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c irrek.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c iweights.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c intersite_V.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c init_nsg.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c nsg_adj.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp 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-I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c make_pointlists.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c manypw.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c memory_report.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c mix_rho.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c move_ions.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c multable.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c n_plane_waves.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c new_ns.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c new_nsb.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c new_nsg.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c new_occ.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c ns_adj.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c non_scf.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c offset_atom_wfc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c openfil.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c orthoatwfc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c output_tau.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c para.f90\n", - "\u001b[01m\u001b[Kpara.f90:341:20:\u001b[m\u001b[K\n", - "\n", - " 260 | CALL MPI_SEND( vec, (length*nks), MPI_DOUBLE_PRECISION, 0, 17, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 341 | CALL MPI_SEND( ivec, (length*nks), MPI_INTEGER, 0, 17, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (INTEGER(4)/REAL(8)).\n", - "\u001b[01m\u001b[Kpara.f90:366:23:\u001b[m\u001b[K\n", - "\n", - " 285 | CALL MPI_RECV( vec(1,nbase+1), (length*fine), MPI_DOUBLE_PRECISION, &\n", - " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", - "......\n", - " 366 | CALL MPI_RECV( ivec(1,nbase+1), (length*fine), MPI_INTEGER, &\n", - " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", - "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (INTEGER(4)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_print_energies.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_scf_energy.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_scf_potential.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_init_ions.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_init_cell.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_init_potential.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_initbase.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_clean.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_check.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_clock.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_summary.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_initialization.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_ext_forces.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_int_forces.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_read_input.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plus_u_full.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c potinit.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c print_clock_pw.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c print_ks_energies.f90\n", 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-I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_qexsd_step.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pw_init_qexsd_input.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pw2blip.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pw2casino.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pw2casino_write.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c rdiagh.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c read_conf_from_file.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c read_file_new.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c remove_atomic_rho.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c report_mag.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp 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-I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c ruotaijk.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c run_pwscf.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_1psi.f90\n", - "mpif90 -O3 -g 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-I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_us.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_forces_subs.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c forces.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules 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-I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_print_energies.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_scf_energy.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_scf_potential.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_init_ions.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_init_cell.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_init_potential.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_initbase.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_clean.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_check.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_clock.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_summary.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_initialization.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_ext_forces.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_int_forces.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plugin_read_input.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c plus_u_full.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c potinit.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c print_clock_pw.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c print_ks_energies.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pw_restart_new.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c punch.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_qexsd_step.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pw_init_qexsd_input.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pw2blip.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pw2casino.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pw2casino_write.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c rdiagh.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c read_conf_from_file.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c read_file_new.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c remove_atomic_rho.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c report_mag.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c restart_in_electrons.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c rho2zeta.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c ruotaijk.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c run_pwscf.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_1psi.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_psi.f90\n", "\u001b[01m\u001b[Ks_psi.f90:256:56:\u001b[m\u001b[K\n", "\n", " 243 | qq_at(1,1,na), nhm, becp%r(ofsbeta(na)+1,1),&\n", @@ -2785,36 +2056,36 @@ " 256 | CALL DGEMM( 'N', 'N', 2 * n, m, nkb, 1.D0, vkb, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c save_in_cbands.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c save_in_electrons.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c scale_h.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c set_kplusq.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c set_kup_and_kdw.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c set_occupations.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c set_rhoc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c set_spin_vars.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c set_vrs.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c setlocal.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c setup.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stop_run.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_cc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp 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-I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_har.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_hub.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_knl.f90\n", - "mpif90 -O3 -g 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-I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_nonloc_dft.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_mgga.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stress.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c save_in_cbands.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c save_in_electrons.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib 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stres_ewa.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_gradcorr.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_har.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_hub.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_knl.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_loc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_us.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_nonloc_dft.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_mgga.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stress.f90\n", "\u001b[01m\u001b[Kstress.f90:110:75:\u001b[m\u001b[K\n", "\n", " 110 | nspin, dfftp, g, alat, omega, sigmaxc, rho%kin_r )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K More actual than formal arguments in procedure call at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c struct_fact.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sum_band.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c struct_fact.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sum_band.f90\n", "\u001b[01m\u001b[Ksum_band.f90:1032:30:\u001b[m\u001b[K\n", "\n", " 999 | 1.0_dp, becp%r(ofsbeta(na)+1,1), nkb, &\n", @@ -2823,19 +2094,19 @@ " 1032 | 1.0_dp, auxk1, 2*this_bgrp_nbnd, auxk2, 2*this_bgrp_nbnd, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sumkg.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sumkt.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c summary.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c symmetrize_at.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c transform_becsum_so.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c transform_becsum_nc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c transform_qq_so.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c trnvecc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c usnldiag.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c v_of_rho.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vcsmd.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vcsubs.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vhpsi.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sumkg.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sumkt.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c summary.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c symmetrize_at.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c transform_becsum_so.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c transform_becsum_nc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c transform_qq_so.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c trnvecc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c usnldiag.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c v_of_rho.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vcsmd.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vcsubs.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vhpsi.f90\n", "\u001b[01m\u001b[Kvhpsi.f90:381:28:\u001b[m\u001b[K\n", "\n", " 378 | rvaux,ldimx, projauxr,ldimx, 0.0_dp, rtemp, ldimx)\n", @@ -2844,20 +2115,20 @@ " 381 | wfcUaux, 2*np, rtemp, ldimx, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vloc_psi.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c utils.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wfcinit.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c write_ns.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wsweight.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c weights.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c ortho_wfc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wannier_init.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wannier_check.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wannier_clean.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wannier_occ.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c g_psi_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c h_psi_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vhpsi_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vloc_psi.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c utils.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wfcinit.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c write_ns.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wsweight.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c weights.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c ortho_wfc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wannier_init.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wannier_check.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wannier_clean.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wannier_occ.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c g_psi_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c h_psi_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vhpsi_gpu.f90\n", "\u001b[01m\u001b[Kvhpsi_gpu.f90:158:22:\u001b[m\u001b[K\n", "\n", " 154 | vns_d(1,1,na), ldimax, &\n", @@ -2866,11 +2137,11 @@ " 158 | wfcU_d(1,offsetU(na)+1), 2*ldap, rtemp_d, ldimaxt, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_psi_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vloc_psi_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c usnldiag_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_vuspsi_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sum_band_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_psi_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vloc_psi_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c usnldiag_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_vuspsi_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sum_band_gpu.f90\n", "\u001b[01m\u001b[Ksum_band_gpu.f90:1151:30:\u001b[m\u001b[K\n", "\n", " 1117 | 1.0_dp, becp_d%r_d(ofsbeta(na)+1,1), nkb, &\n", @@ -2879,14 +2150,14 @@ " 1151 | 1.0_dp, auxk1_d, 2*this_bgrp_nbnd, auxk2_d, 2*this_bgrp_nbnd, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_paw_to_deeq_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_vhub_to_deeq_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c rotate_wfc_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c hs_1psi_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c hs_psi_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_1psi_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c utils_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c atomic_wfc_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_paw_to_deeq_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_vhub_to_deeq_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c rotate_wfc_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c hs_1psi_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c hs_psi_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_1psi_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c utils_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c atomic_wfc_gpu.f90\n", "ar ruv libpw.a a2fmod.o add_bfield.o add_efield.o add_dmft_occ.o add_vuspsi.o add_gatefield.o add_paw_to_deeq.o add_vhub_to_deeq.o addusdens.o addusforce.o addusstress.o allocate_fft.o allocate_locpot.o allocate_nlpot.o allocate_wfc.o atomic_rho.o atomic_wfc.o atomic_wfc_mod.o average_pp.o acfdt_in_pw.o newd.o beef.o bp_mod.o bp_c_phase.o bp_strings.o buffers.o c_bands.o c_phase_field.o orbm_kubo.o cdiagh.o clean_pw.o close_files.o commutator_Hx_psi.o commutator_Vhubx_psi.o compute_becsum.o compute_deff.o compute_dip.o compute_rho.o compute_qdipol.o compute_qdipol_so.o compute_ux.o coset.o Coul_cut_2D.o d_matrix.o data_structure.o divide_class.o divide_class_so.o divide_et_impera.o rotate_wfc.o run_driver.o dynamics_module.o efermig.o efermit.o electrons.o environ_pw_module.o eqvect.o esm.o esm_common_mod.o esm_ewald_mod.o esm_force_mod.o esm_hartree_mod.o esm_local_mod.o esm_stres_mod.o ewald.o ewald_dipole.o extfield.o exx_base.o exx_band.o exx.o fcp_capacitance.o fcp_dyn_calcavg.o fcp_dyn_printavg.o fcp_dynamics.o fcp_hessian.o fcp_input.o fcp_module.o fcp_relaxation.o find_group.o forces_bp_efield.o force_cc.o force_corr.o force_ew.o force_hub.o force_lc.o force_us.o forces.o g_psi.o g_psi_mod.o gcscf_input.o gcscf_module.o gen_at_dj.o gen_at_dy.o get_locals.o gk_sort.o gradcorr.o gweights.o g2_kin.o hs_psi.o hs_1psi.o h_epsi_her_apply.o h_epsi_her_set.o h_psi.o h_psi_meta.o hinit0.o hinit1.o hubbard.o init_ns.o init_q_aeps.o init_run.o init_us_2.o init_vloc.o input.o io_rho_xml.o irrek.o iweights.o intersite_V.o init_nsg.o nsg_adj.o start_k.o kpoint_grid.o lchk_tauxk.o ldaU.o make_pointlists.o manypw.o martyna_tuckerman.o memory_report.o mix_rho.o move_ions.o multable.o n_plane_waves.o new_ns.o new_nsb.o new_nsg.o new_occ.o ns_adj.o non_scf.o offset_atom_wfc.o openfil.o orthoatwfc.o output_tau.o para.o paw_exx.o paw_init.o paw_onecenter.o paw_symmetry.o plugin_print_energies.o plugin_scf_energy.o plugin_scf_potential.o plugin_init_ions.o plugin_init_cell.o plugin_init_potential.o plugin_initbase.o plugin_clean.o plugin_check.o plugin_clock.o plugin_summary.o plugin_initialization.o plugin_ext_forces.o plugin_int_forces.o plugin_read_input.o plus_u_full.o potinit.o print_clock_pw.o print_ks_energies.o punch.o pw_restart_new.o add_qexsd_step.o pw_init_qexsd_input.o pwcom.o pw2blip.o pw2casino.o pw2casino_write.o rdiagh.o read_conf_from_file.o read_file_new.o realus.o remove_atomic_rho.o report_mag.o restart_in_electrons.o rho2zeta.o rism_module.o ruotaijk.o run_pwscf.o s_1psi.o s_psi.o save_in_cbands.o save_in_electrons.o scale_h.o loc_scdm.o loc_scdm_k.o scf_mod.o set_kplusq.o set_kup_and_kdw.o set_occupations.o set_rhoc.o set_spin_vars.o set_vrs.o setlocal.o setup.o scissor.o sic.o stop_run.o stres_cc.o stres_ewa.o stres_gradcorr.o stres_har.o stres_hub.o stres_knl.o stres_loc.o stres_us.o stres_nonloc_dft.o stres_mgga.o stress.o struct_fact.o sum_band.o sumkg.o sumkt.o summary.o symme.o symm_base.o symmetrize_at.o tetra.o transform_becsum_so.o transform_becsum_nc.o transform_qq_so.o trnvecc.o two_chem.o update_pot.o us_exx.o usnldiag.o v_of_rho.o vcsmd.o vcsubs.o vhpsi.o vloc_psi.o utils.o xdm_dispersion.o wfcinit.o write_ns.o wsweight.o weights.o ortho_wfc.o wannier_init.o wannier_check.o wannier_clean.o wannier_occ.o oscdft_input.o oscdft_context.o oscdft_base.o oscdft_enums.o oscdft_indices.o oscdft_wavefunction.o oscdft_wavefunction_subs.o oscdft_functions.o oscdft_occupations.o oscdft_wfcO.o oscdft_forces.o oscdft_forces_subs.o g_psi_mod_gpu.o g_psi_gpu.o h_psi_gpu.o vhpsi_gpu.o s_psi_gpu.o vloc_psi_gpu.o pwcom_gpu.o scf_mod_gpu.o usnldiag_gpu.o add_vuspsi_gpu.o sum_band_gpu.o newd_gpu.o add_paw_to_deeq_gpu.o add_vhub_to_deeq_gpu.o rotate_wfc_gpu.o hs_1psi_gpu.o hs_psi_gpu.o s_1psi_gpu.o utils_gpu.o atomic_wfc_gpu.o oscdft_wavefunction_subs_gpu.o oscdft_wfcO_gpu.o oscdft_functions_gpu.o additional_cusolver_subs.o\n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libpw.a\n", @@ -3182,65 +2453,48 @@ "make[2]: Entering directory '/content/q-e/PW/src'\n", "if test -n \"\" ; then \\\n", "( cd ../.. ; make || exit 1 ) ; fi\n", - "mpif90 -O3 -g -fallow-argument-mismatch -fopenmp -cpp -fopenmp -D__OPENMP -D__FFTW3 -D__MPI -D__SCALAPACK -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pwscf.f90\n", - "mpif90 -g -pthread -fopenmp -o pw.x \\\n", - " pwscf.o libpw.a ../../KS_Solvers/libks_solvers.a ../../dft-d3/libdftd3qe.a /content/q-e//Modules/libqemod.a /content/q-e//upflib/libupf.a /content/q-e//XClib/xc_lib.a /content/q-e//FFTXlib/src/libqefft.a /content/q-e//LAXlib/libqela.a /content/q-e//UtilXlib/libutil.a /content/q-e//MBD/libmbd.a -L/content/q-e//external/devxlib/src -ldevXlib -lfftw3 -lmkl_gf_lp64 -lmkl_gnu_thread -lmkl_core \n", - "/usr/bin/ld: ../../KS_Solvers/libks_solvers.a(ppcg_gamma.o): in function `ppcg_gamma_':\n", - "/content/q-e/KS_Solvers/PPCG/ppcg_gamma.f90:963: undefined reference to `descinit_'\n", - "/usr/bin/ld: ../../KS_Solvers/libks_solvers.a(ppcg_gamma.o): in function `cholqr_dmat':\n", - "/content/q-e/KS_Solvers/PPCG/ppcg_gamma.f90:966: undefined reference to `pdpotrf_'\n", - "/usr/bin/ld: /content/q-e/KS_Solvers/PPCG/ppcg_gamma.f90:975: undefined reference to `pdtrtri_'\n", - "/usr/bin/ld: ../../KS_Solvers/libks_solvers.a(ppcg_k.o): in function `ppcg_k_':\n", - "/content/q-e/KS_Solvers/PPCG/ppcg_k.f90:923: undefined reference to `descinit_'\n", - "/usr/bin/ld: ../../KS_Solvers/libks_solvers.a(ppcg_k.o): in function `cholqr_dmat':\n", - "/content/q-e/KS_Solvers/PPCG/ppcg_k.f90:926: undefined reference to `pzpotrf_'\n", - "/usr/bin/ld: /content/q-e/KS_Solvers/PPCG/ppcg_k.f90:935: undefined reference to `pztrtri_'\n", - "/usr/bin/ld: ../../KS_Solvers/libks_solvers.a(ppcg_gamma_gpu.o): in function `ppcg_gamma_gpu_':\n", - "/content/q-e/KS_Solvers/PPCG/ppcg_gamma_gpu.f90:1100: undefined reference to `descinit_'\n", - "/usr/bin/ld: ../../KS_Solvers/libks_solvers.a(ppcg_gamma_gpu.o): in function `cholqr_dmat':\n", - "/content/q-e/KS_Solvers/PPCG/ppcg_gamma_gpu.f90:1103: undefined reference to `pdpotrf_'\n", - "/usr/bin/ld: /content/q-e/KS_Solvers/PPCG/ppcg_gamma_gpu.f90:1112: undefined reference to `pdtrtri_'\n", - "/usr/bin/ld: ../../KS_Solvers/libks_solvers.a(ppcg_k_gpu.o): in function `ppcg_k_gpu_':\n", - "/content/q-e/KS_Solvers/PPCG/ppcg_k_gpu.f90:1207: undefined reference to `descinit_'\n", - "/usr/bin/ld: ../../KS_Solvers/libks_solvers.a(ppcg_k_gpu.o): in function `cholqr_dmat':\n", - "/content/q-e/KS_Solvers/PPCG/ppcg_k_gpu.f90:1210: undefined reference to `pzpotrf_'\n", - "/usr/bin/ld: /content/q-e/KS_Solvers/PPCG/ppcg_k_gpu.f90:1219: undefined reference to `pztrtri_'\n", - "/usr/bin/ld: /content/q-e//LAXlib/libqela.a(la_helper.o): in function `laxlib_start_drv_':\n", - "/content/q-e/LAXlib/la_helper.f90:250: undefined reference to `blacs_gridinit_'\n", - "/usr/bin/ld: /content/q-e//LAXlib/libqela.a(cdiaghg.o): in function `laxlib_pcdiaghg_':\n", - "/content/q-e/LAXlib/cdiaghg.f90:512: undefined reference to `descinit_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/cdiaghg.f90:519: undefined reference to `pzpotrf_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/cdiaghg.f90:542: undefined reference to `pztrtri_'\n", - "/usr/bin/ld: /content/q-e//LAXlib/libqela.a(rdiaghg.o): in function `laxlib_prdiaghg_':\n", - "/content/q-e/LAXlib/rdiaghg.f90:487: undefined reference to `descinit_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/rdiaghg.f90:493: undefined reference to `pdpotrf_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/rdiaghg.f90:513: undefined reference to `pdtrtri_'\n", - "/usr/bin/ld: /content/q-e//LAXlib/libqela.a(dspev_drv.o): in function `__dspev_module_MOD_pdsyevd_drv':\n", - "/content/q-e/LAXlib/dspev_drv.f90:652: undefined reference to `descinit_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/dspev_drv.f90:725: undefined reference to `pdsyevd_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/dspev_drv.f90:735: undefined reference to `pdsyevd_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/dspev_drv.f90:725: undefined reference to `pdsyevd_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/dspev_drv.f90:735: undefined reference to `pdsyevd_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/dspev_drv.f90:735: undefined reference to `pdsyevd_'\n", - "/usr/bin/ld: /content/q-e//LAXlib/libqela.a(dspev_drv.o):/content/q-e/LAXlib/dspev_drv.f90:725: more undefined references to `pdsyevd_' follow\n", - "/usr/bin/ld: /content/q-e//LAXlib/libqela.a(zhpev_drv.o): in function `__zhpev_module_MOD_pzheevd_drv':\n", - "/content/q-e/LAXlib/zhpev_drv.f90:1474: undefined reference to `descinit_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/zhpev_drv.f90:1549: undefined reference to `pzheevd_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/zhpev_drv.f90:1562: undefined reference to `pzheevd_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/zhpev_drv.f90:1549: undefined reference to `pzheevd_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/zhpev_drv.f90:1562: undefined reference to `pzheevd_'\n", - "/usr/bin/ld: /content/q-e/LAXlib/zhpev_drv.f90:1562: undefined reference to `pzheevd_'\n", - "/usr/bin/ld: /content/q-e//LAXlib/libqela.a(zhpev_drv.o):/content/q-e/LAXlib/zhpev_drv.f90:1549: more undefined references to `pzheevd_' follow\n", - "/usr/bin/ld: /content/q-e//LAXlib/libqela.a(mp_diag.o): in function `__laxlib_processors_grid_MOD_laxlib_end_drv':\n", - "/content/q-e/LAXlib/mp_diag.f90:57: undefined reference to `blacs_gridexit_'\n", - "collect2: error: ld returned 1 exit status\n", - "make[2]: *** [Makefile:309: pw.x] Error 1\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pwscf.f90\n", + "mpif90 -g -o pw.x \\\n", + " pwscf.o libpw.a ../../KS_Solvers/libks_solvers.a ../../dft-d3/libdftd3qe.a /content/q-e//Modules/libqemod.a /content/q-e//upflib/libupf.a /content/q-e//XClib/xc_lib.a /content/q-e//FFTXlib/src/libqefft.a /content/q-e//LAXlib/libqela.a /content/q-e//UtilXlib/libutil.a /content/q-e//MBD/libmbd.a -L/content/q-e//external/devxlib/src -ldevXlib -lfftw3 -lmkl_gf_lp64 -lmkl_sequential -lmkl_core \n", + "( cd ../../bin; ln -fs ../PW/src/pw.x . ; \\\n", + " ln -fs ../PW/src/pw.x dist.x ; ln -fs ../PW/src/pw.x manypw.x ; )\n", "make[2]: Leaving directory '/content/q-e/PW/src'\n", - "make[1]: *** [Makefile:9: pw] Error 1\n", + "( cd tools ; make all || exit 1 )\n", + "make[2]: Entering directory '/content/q-e/PW/tools'\n", + "if test -n \"\" ; then \\\n", + "( cd ../.. ; make || exit 1 ) ; fi\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../src -I../../dft-d3/ -c ev.f90\n", + "mpif90 -g -o ev.x \\\n", + "\tev.o ../src/libpw.a ../../KS_Solvers/libks_solvers.a ../../dft-d3/libdftd3qe.a /content/q-e//Modules/libqemod.a /content/q-e//upflib/libupf.a /content/q-e//XClib/xc_lib.a /content/q-e//FFTXlib/src/libqefft.a /content/q-e//LAXlib/libqela.a /content/q-e//UtilXlib/libutil.a /content/q-e//MBD/libmbd.a -L/content/q-e//external/devxlib/src -ldevXlib -lfftw3 -lmkl_gf_lp64 -lmkl_sequential -lmkl_core \n", + "( cd ../../bin ; ln -fs ../PW/tools/ev.x . )\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../src -I../../dft-d3/ -c kpoints.f90\n", + "mpif90 -g -o kpoints.x \\\n", + "\tkpoints.o ../src/libpw.a ../../KS_Solvers/libks_solvers.a ../../dft-d3/libdftd3qe.a /content/q-e//Modules/libqemod.a /content/q-e//upflib/libupf.a /content/q-e//XClib/xc_lib.a /content/q-e//FFTXlib/src/libqefft.a /content/q-e//LAXlib/libqela.a /content/q-e//UtilXlib/libutil.a /content/q-e//MBD/libmbd.a -L/content/q-e//external/devxlib/src -ldevXlib -lfftw3 -lmkl_gf_lp64 -lmkl_sequential -lmkl_core \n", + "( cd ../../bin ; ln -fs ../PW/tools/kpoints.x . )\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../src -I../../dft-d3/ -c pwi2xsf.f90\n", + "mpif90 -g -o pwi2xsf.x \\\n", + "\tpwi2xsf.o ../src/libpw.a ../../KS_Solvers/libks_solvers.a ../../dft-d3/libdftd3qe.a /content/q-e//Modules/libqemod.a /content/q-e//upflib/libupf.a /content/q-e//XClib/xc_lib.a /content/q-e//FFTXlib/src/libqefft.a /content/q-e//LAXlib/libqela.a /content/q-e//UtilXlib/libutil.a /content/q-e//MBD/libmbd.a -L/content/q-e//external/devxlib/src -ldevXlib -lfftw3 -lmkl_gf_lp64 -lmkl_sequential -lmkl_core \n", + "( cd ../../bin ; ln -fs ../PW/tools/pwi2xsf.x . )\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../src -I../../dft-d3/ -c ibrav2cell.f90\n", + "mpif90 -g -o ibrav2cell.x \\\n", + "\tibrav2cell.o ../src/libpw.a ../../KS_Solvers/libks_solvers.a ../../dft-d3/libdftd3qe.a /content/q-e//Modules/libqemod.a /content/q-e//upflib/libupf.a /content/q-e//XClib/xc_lib.a /content/q-e//FFTXlib/src/libqefft.a /content/q-e//LAXlib/libqela.a /content/q-e//UtilXlib/libutil.a /content/q-e//MBD/libmbd.a -L/content/q-e//external/devxlib/src -ldevXlib -lfftw3 -lmkl_gf_lp64 -lmkl_sequential -lmkl_core \n", + "( cd ../../bin ; ln -fs ../PW/tools/ibrav2cell.x . )\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../src -I../../dft-d3/ -c cell2ibrav.f90\n", + "mpif90 -g -o cell2ibrav.x \\\n", + "\tcell2ibrav.o ../src/libpw.a ../../KS_Solvers/libks_solvers.a ../../dft-d3/libdftd3qe.a /content/q-e//Modules/libqemod.a /content/q-e//upflib/libupf.a /content/q-e//XClib/xc_lib.a /content/q-e//FFTXlib/src/libqefft.a /content/q-e//LAXlib/libqela.a /content/q-e//UtilXlib/libutil.a /content/q-e//MBD/libmbd.a -L/content/q-e//external/devxlib/src -ldevXlib -lfftw3 -lmkl_gf_lp64 -lmkl_sequential -lmkl_core \n", + "( cd ../../bin ; ln -fs ../PW/tools/cell2ibrav.x . )\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../src -I../../dft-d3/ -c scan_ibrav.f90\n", + "mpif90 -g -o scan_ibrav.x \\\n", + "\tscan_ibrav.o ../src/libpw.a ../../KS_Solvers/libks_solvers.a ../../dft-d3/libdftd3qe.a /content/q-e//Modules/libqemod.a /content/q-e//upflib/libupf.a /content/q-e//XClib/xc_lib.a /content/q-e//FFTXlib/src/libqefft.a /content/q-e//LAXlib/libqela.a /content/q-e//UtilXlib/libutil.a /content/q-e//MBD/libmbd.a -L/content/q-e//external/devxlib/src -ldevXlib -lfftw3 -lmkl_gf_lp64 -lmkl_sequential -lmkl_core \n", + "( cd ../../bin ; ln -fs ../PW/tools/scan_ibrav.x . )\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../src -I../../dft-d3/ -c rism1d.f90\n", + "mpif90 -g -o rism1d.x \\\n", + "\trism1d.o ../src/libpw.a ../../KS_Solvers/libks_solvers.a ../../dft-d3/libdftd3qe.a /content/q-e//Modules/libqemod.a /content/q-e//upflib/libupf.a /content/q-e//XClib/xc_lib.a /content/q-e//FFTXlib/src/libqefft.a /content/q-e//LAXlib/libqela.a /content/q-e//UtilXlib/libutil.a /content/q-e//MBD/libmbd.a -L/content/q-e//external/devxlib/src -ldevXlib -lfftw3 -lmkl_gf_lp64 -lmkl_sequential -lmkl_core \n", + "( cd ../../bin ; ln -fs ../PW/tools/rism1d.x . )\n", + "make[2]: Leaving directory '/content/q-e/PW/tools'\n", "make[1]: Leaving directory '/content/q-e/PW'\n", - "make: *** [Makefile:71: pw] Error 1\n", - "CPU times: user 5.2 s, sys: 675 ms, total: 5.87 s\n", - "Wall time: 10min 14s\n" + "CPU times: user 5.74 s, sys: 727 ms, total: 6.46 s\n", + "Wall time: 9min 43s\n" ] } ] @@ -3256,12 +2510,12 @@ ], "metadata": { "id": "-r8cV1GmNUxu", - "outputId": "6e0506c3-6d5a-4cc9-876b-dd5c4831ef08", + "outputId": "936206c4-8bee-4959-84f9-afa2065d42bf", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": 2, + "execution_count": 3, "outputs": [ { "output_type": "stream", @@ -3276,14 +2530,14 @@ " cython-doc python3-numpy\n", "The following NEW packages will be installed:\n", " cython3 libxc-dev libxc9 python3-mpi4py\n", - "0 upgraded, 4 newly installed, 0 to remove and 25 not upgraded.\n", + "0 upgraded, 4 newly installed, 0 to remove and 18 not upgraded.\n", "Need to get 13.7 MB of archives.\n", "After this operation, 49.6 MB of additional disk space will be used.\n", "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 cython3 amd64 0.29.28-1ubuntu3 [1,336 kB]\n", "Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libxc9 amd64 5.1.7-1ubuntu1 [3,900 kB]\n", "Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libxc-dev amd64 5.1.7-1ubuntu1 [8,038 kB]\n", "Get:4 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-mpi4py amd64 3.1.3-1build2 [465 kB]\n", - "Fetched 13.7 MB in 2s (7,564 kB/s)\n", + "Fetched 13.7 MB in 2s (7,902 kB/s)\n", "Selecting previously unselected package cython3.\n", "(Reading database ... 121133 files and directories currently installed.)\n", "Preparing to unpack .../cython3_0.29.28-1ubuntu3_amd64.deb ...\n", @@ -3302,17 +2556,17 @@ "Setting up libxc9:amd64 (5.1.7-1ubuntu1) ...\n", "Setting up libxc-dev (5.1.7-1ubuntu1) ...\n", "Processing triggers for libc-bin (2.35-0ubuntu3.1) ...\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", "\n", "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", + "\n", "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", "\n" ] } @@ -3323,7 +2577,7 @@ "source": [ "%%time\n", "import os\n", - "os.chdir('qmcpack/build')\n", + "os.chdir('/content/qmcpack/build')\n", "!cmake .." ], "metadata": { @@ -3331,9 +2585,9 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "ed8d648b-1a18-4044-d9c9-14d14ac5e299" + "outputId": "a1f2ab8b-0ac0-4cde-9942-b209588b27f9" }, - "execution_count": 3, + "execution_count": 5, "outputs": [ { "output_type": "stream", @@ -3446,7 +2700,7 @@ "-- Ready to parse QMCPACK source tree\n", "\u001b[0msed supports -E\u001b[0m\n", "\u001b[0mGit branch: develop\u001b[0m\n", - "\u001b[0mGit commit hash: b304904cca47ce861c7d4c11d8e76e6b2f735979\u001b[0m\n", + "\u001b[0mGit commit hash: 6f7eefeb33f860f99e6a91ab27d0584ca8b6fae9\u001b[0m\n", "-- Performing Test ISNAN_WORKS\n", "-- Performing Test ISNAN_WORKS - Success\n", "-- ppconvert enabled.\n", @@ -3459,11 +2713,11 @@ "\u001b[0mAdding system tests for QMCPACK\u001b[0m\n", "\u001b[0mAdding example tests for QMCPACK\u001b[0m\n", "\u001b[0mAdding Nexus tests\u001b[0m\n", - "-- Configuring done (39.1s)\n", - "-- Generating done (0.5s)\n", + "-- Configuring done (41.5s)\n", + "-- Generating done (0.6s)\n", "-- Build files have been written to: /content/qmcpack/build\n", - "CPU times: user 320 ms, sys: 39 ms, total: 358 ms\n", - "Wall time: 40 s\n" + "CPU times: user 392 ms, sys: 49 ms, total: 441 ms\n", + "Wall time: 42.5 s\n" ] } ] @@ -3479,155 +2733,155 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "972f2901-9b2f-469a-ad50-fe602e820765" + "outputId": "75753c81-8e38-4277-bf66-b4b9d770c2ce" }, - "execution_count": 4, + "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "[ 0%] \u001b[32mBuilding CXX object src/Platforms/Host/CMakeFiles/platform_host_runtime.dir/sysutil.cpp.o\u001b[0m\n", "[ 0%] \u001b[32mBuilding CXX object src/Platforms/CPU/CMakeFiles/platform_cpu_runtime.dir/math.cpp.o\u001b[0m\n", + "[ 0%] \u001b[32mBuilding CXX object src/Platforms/Host/CMakeFiles/platform_host_runtime.dir/sysutil.cpp.o\u001b[0m\n", "[ 0%] \u001b[32m\u001b[1mLinking CXX static library libplatform_cpu_runtime.a\u001b[0m\n", "[ 0%] Built target platform_cpu_runtime\n", - "[ 0%] \u001b[32mBuilding CXX object src/Utilities/CMakeFiles/cxx_helpers.dir/ModernStringUtils.cpp.o\u001b[0m\n", "[ 1%] \u001b[32mBuilding CXX object 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src/Estimators/tests/CMakeFiles/test_estimators_mpi.dir/EstimatorManagerNewTest.cpp.o\u001b[0m\n", @@ -4092,8 +3346,8 @@ "[ 79%] \u001b[32mBuilding CXX object src/Estimators/tests/CMakeFiles/test_estimators.dir/test_SpinDensityInput.cpp.o\u001b[0m\n", "[ 80%] \u001b[32mBuilding CXX object src/QMCDrivers/CMakeFiles/qmcdriver.dir/QMCDriver.cpp.o\u001b[0m\n", "[ 81%] \u001b[32mBuilding CXX object src/Estimators/tests/CMakeFiles/test_estimators.dir/test_SpinDensityNew.cpp.o\u001b[0m\n", - "[ 81%] \u001b[32mBuilding CXX object src/Estimators/tests/CMakeFiles/test_estimators.dir/test_InputSection.cpp.o\u001b[0m\n", "[ 81%] \u001b[32mBuilding CXX object src/QMCDrivers/CMakeFiles/qmcdriver.dir/QMCDriverInput.cpp.o\u001b[0m\n", + "[ 81%] \u001b[32mBuilding CXX object src/Estimators/tests/CMakeFiles/test_estimators.dir/test_InputSection.cpp.o\u001b[0m\n", "[ 81%] \u001b[32mBuilding CXX object src/QMCDrivers/CMakeFiles/qmcdriver.dir/QMCDriverNew.cpp.o\u001b[0m\n", "[ 81%] \u001b[32mBuilding CXX object src/Estimators/tests/CMakeFiles/test_estimators.dir/test_EstimatorManagerInput.cpp.o\u001b[0m\n", "[ 81%] \u001b[32mBuilding CXX object src/Estimators/tests/CMakeFiles/test_estimators.dir/test_ScalarEstimatorInputs.cpp.o\u001b[0m\n", @@ -4108,8 +3362,8 @@ "[ 82%] \u001b[32mBuilding CXX object src/Estimators/tests/CMakeFiles/test_estimators.dir/test_PerParticleHamiltonianLogger.cpp.o\u001b[0m\n", "[ 82%] \u001b[32mBuilding CXX object src/Estimators/tests/CMakeFiles/test_estimators.dir/test_EstimatorManagerCrowd.cpp.o\u001b[0m\n", "[ 83%] \u001b[32mBuilding CXX object src/QMCDrivers/CMakeFiles/qmcdriver.dir/WFOpt/OutputMatrix.cpp.o\u001b[0m\n", - "[ 83%] \u001b[32mBuilding CXX object src/Estimators/tests/CMakeFiles/test_estimators.dir/test_MagnetizationDensityInput.cpp.o\u001b[0m\n", "[ 83%] \u001b[32mBuilding CXX object src/QMCDrivers/CMakeFiles/qmcdriver.dir/WFOpt/WFOptDriverInput.cpp.o\u001b[0m\n", + "[ 83%] \u001b[32mBuilding CXX object 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+ "[ 91%] \u001b[32mBuilding CXX object src/QMCDrivers/tests/CMakeFiles/test_new_drivers.dir/SetupPools.cpp.o\u001b[0m\n", "[ 91%] \u001b[32mBuilding CXX object src/QMCDrivers/tests/CMakeFiles/test_drivers.dir/test_vmc.cpp.o\u001b[0m\n", "[ 91%] \u001b[32mBuilding CXX object src/QMCDrivers/tests/CMakeFiles/test_new_drivers.dir/test_Crowd.cpp.o\u001b[0m\n", "[ 91%] \u001b[32mBuilding CXX object src/QMCDrivers/tests/CMakeFiles/test_drivers.dir/test_dmc.cpp.o\u001b[0m\n", @@ -4199,8 +3453,8 @@ "[ 95%] \u001b[32mBuilding CXX object src/QMCDrivers/tests/CMakeFiles/test_new_drivers_mpi.dir/SetupPools.cpp.o\u001b[0m\n", "[ 95%] \u001b[32mBuilding CXX object src/QMCDrivers/tests/CMakeFiles/test_new_drivers.dir/test_WFOptDriverInput.cpp.o\u001b[0m\n", "[ 95%] \u001b[32m\u001b[1mLinking CXX executable test_new_drivers\u001b[0m\n", - "[ 95%] \u001b[32mBuilding CXX object src/QMCDrivers/tests/CMakeFiles/test_new_drivers_mpi.dir/test_WalkerControl.cpp.o\u001b[0m\n", "[ 95%] Built target test_new_drivers\n", + "[ 95%] \u001b[32mBuilding CXX object src/QMCDrivers/tests/CMakeFiles/test_new_drivers_mpi.dir/test_WalkerControl.cpp.o\u001b[0m\n", "[ 95%] \u001b[32mBuilding CXX object src/QMCDrivers/tests/CMakeFiles/test_new_drivers_mpi.dir/test_QMCDriverNew.cpp.o\u001b[0m\n", "[ 95%] \u001b[32mBuilding CXX object src/QMCApp/CMakeFiles/qmc.dir/QMCAppBase.cpp.o\u001b[0m\n", "[ 96%] \u001b[32mBuilding CXX object src/QMCApp/CMakeFiles/qmc.dir/QMCMain.cpp.o\u001b[0m\n", @@ -4225,8 +3479,8 @@ "[100%] \u001b[32m\u001b[1mLinking CXX executable test_tools\u001b[0m\n", "[100%] Built target qmcfinitesize\n", "[100%] Built target test_tools\n", - "CPU times: user 22.2 s, sys: 2.99 s, total: 25.2 s\n", - "Wall time: 47min 43s\n" + "CPU times: user 27.7 s, sys: 3.43 s, total: 31.1 s\n", + "Wall time: 51min 24s\n" ] } ] @@ -4238,18 +3492,18 @@ ], "metadata": { "id": "PWb5jOGJbDmN", - "outputId": "d145ce00-df60-4c37-872a-cf2e2be358f9", + "outputId": "1d8ee087-623a-4f29-8c89-c8f2426d6f76", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": 10, + "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "/content/qmcpack/external_codes/quantum_espresso\n" + "/content/qmcpack/build\n" ] } ] @@ -4259,5022 +3513,5050 @@ "source": [ "import os\n", "os.chdir('/content/qmcpack/external_codes/quantum_espresso')\n", - "!./download_and_patch_qe6.3.sh" + "!./download_and_patch_qe7.0.sh" ], "metadata": { "id": "4QgdG6AMxReT", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "8e9d34a8-2c70-436e-8491-ecf854aaec1e" + "outputId": "cc8639ac-79db-4400-8c71-c30bc800b0fa" }, - "execution_count": 11, + "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", - "q-e-qe-6.3/GUI/Guib/doc/diagrams/PWgui.png\n", - "q-e-qe-6.3/GUI/Guib/doc/diagrams/myGUI.dia\n", - "q-e-qe-6.3/GUI/Guib/doc/diagrams/myGUI.png\n", - 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"q-e-qe-7.0/upflib/upf_kinds.h\n", + "q-e-qe-7.0/upflib/upf_parallel_include.f90\n", + "q-e-qe-7.0/upflib/upf_params.f90\n", + "q-e-qe-7.0/upflib/upf_spinorb.f90\n", + "q-e-qe-7.0/upflib/upf_to_internal.f90\n", + "q-e-qe-7.0/upflib/upf_utils.f90\n", + "q-e-qe-7.0/upflib/upfconv.f90\n", + "q-e-qe-7.0/upflib/uspp.f90\n", + "q-e-qe-7.0/upflib/uspp_data.f90\n", + "q-e-qe-7.0/upflib/virtual_v2.f90\n", + "q-e-qe-7.0/upflib/write_upf_new.f90\n", + "q-e-qe-7.0/upflib/xmltools.f90\n", + "q-e-qe-7.0/upflib/ylmr2.f90\n", + "q-e-qe-7.0/upflib/ylmr2_gpu.f90\n", + "patching file CMakeLists.txt\n", + "patching file PP/CMakeLists.txt\n", "patching file PP/src/Makefile\n", "patching file PP/src/pw2qmcpack.f90\n", - "--- SUCCESS: qe-6.3 patched for pw2qmcpack converter\n", - "There are two ways to build\n", - "1) if your system already has HDF5 installed with Fortran, use the --with-hdf5 configuration option.\n", - " Example configure: ./configure --with-hdf5=path_to_hdf5_include_and_lib\n", - " Currently HDF5 support in QE itself is preliminary. To enable use of pw2qmcpack\n", - " but use the old non-HDF5 I/O within QE, replace '-D__HDF5' with '-D__HDF5_C' in make.inc.\n", - "2) if your system has HDF5 with C only, manually edit make.inc by adding '-D__HDF5_C -DH5_USE_16_API'\n", - " in 'DFLAGS' and provide include and library path in 'IFLAGS' and 'HDF5_LIB'\n" + "patching file UtilXlib/CMakeLists.txt\n", + "patching file UtilXlib/Makefile\n", + "patching file UtilXlib/esh5_interfaces.c\n", + "patching file install/configure\n", + "--- SUCCESS: qe-7.0 patched for pw2qmcpack converter\n", + "Recommend building QE via [CMake](https://gitlab.com/QEF/q-e/-/wikis/Developers/CMake-build-system).\n", + " Require Fortran enabled HDF5. HDF5 has been turned on by default.\n", + " mkdir build_mpi\n", + " cd build_mpi\n", + " cmake -DCMAKE_C_COMPILER=mpicc -DCMAKE_Fortran_COMPILER=mpif90 ..\n", + " make -j 16\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!ls" + ], + "metadata": { + "id": "89ahT_tNbLSH", + "outputId": "d4b8fccc-7380-4202-c02a-e3ddc761fa80", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "add_pw2qmcpack_to_qe-6.3.diff\t build_qe_olcf_summit.sh\tdownload_and_patch_qe7.0.sh\n", + "add_pw2qmcpack_to_qe-6.4.1.diff download_and_patch_qe6.3.sh\tqe-7.0\n", + "add_pw2qmcpack_to_qe-6.4.diff\t download_and_patch_qe6.4.1.sh\tqe-7.0.tar.gz\n", + "add_pw2qmcpack_to_qe-6.7.0.diff download_and_patch_qe6.4.sh\tREADME\n", + "add_pw2qmcpack_to_qe-6.8.diff\t download_and_patch_qe6.7.0.sh\n", + "add_pw2qmcpack_to_qe-7.0.diff\t download_and_patch_qe6.8.sh\n" ] } ] @@ -9283,12 +8565,12 @@ "cell_type": "code", "source": [ "import os\n", - "os.chdir('/content/qmcpack/external_codes/quantum_espresso/qe-6.3')" + "os.chdir('/content/qmcpack/external_codes/quantum_espresso/qe-7.0')" ], "metadata": { "id": "6F9WmSUtbKx5" }, - "execution_count": 15, + "execution_count": 12, "outputs": [] }, { @@ -9298,17 +8580,25 @@ ], "metadata": { "id": "GBhim6i_bKvn", - "outputId": "b51592cd-6506-4a84-d42c-272f0cee2284", + "outputId": "0a5e961e-5341-49ad-f565-4fd562295b49", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": 16, + "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ + "\rdirectory LAXlib : ok\rdirectory FFTXlib : ok\n", + "WARNING! dependencies not found in directory UtilXlib:\n", + "# some_file.o: @some_module@ \n", + "esh5_interfaces.o : @hdf5.h@\n", + "esh5_interfaces.o : @hdf5_hl.h@\n", + "File UtilXlib/make.depend is broken\n", + "directory QEHeat/src : ok\n", + "directory ACFDT/src : not present in /content/qmcpack/external_codes/quantum_espresso/qe-7.0/install\n", "checking build system type... x86_64-pc-linux-gnu\n", "checking ARCH... x86_64\n", "checking setting AR... ... ar\n", @@ -9321,61 +8611,35 @@ "checking for suffix of object files... o\n", "checking whether we are using the GNU Fortran compiler... yes\n", "checking whether gfortran accepts -g... yes\n", - "checking for gcc... gcc\n", - "checking whether we are using the GNU C compiler... yes\n", - "checking whether gcc accepts -g... yes\n", - "checking for gcc option to accept ISO C89... none needed\n", - "checking whether we are using the GNU Fortran compiler... (cached) yes\n", - "checking whether gfortran accepts -g... (cached) yes\n", - "checking for Fortran flag to compile .f90 files... none\n", + "checking for mpiifort... no\n", "checking for mpif90... mpif90\n", "checking whether we are using the GNU Fortran compiler... yes\n", "checking whether mpif90 accepts -g... yes\n", "checking version of mpif90... gfortran 11.4\n", + "checking for Fortran flag to compile .f90 files... none\n", "setting F90... gfortran\n", "setting MPIF90... mpif90\n", - "checking whether we are using the GNU C compiler... (cached) yes\n", - "checking whether gcc accepts -g... (cached) yes\n", - "checking for gcc option to accept ISO C89... (cached) none needed\n", - "setting CC... gcc\n", + "checking for cc... cc\n", + "checking whether we are using the GNU C compiler... yes\n", + "checking whether cc accepts -g... yes\n", + "checking for cc option to accept ISO C89... none needed\n", + "setting CC... cc\n", "setting CFLAGS... -O3\n", - "checking for gfortran... gfortran\n", - "checking whether we are using the GNU Fortran 77 compiler... yes\n", - "checking whether gfortran accepts -g... yes\n", - "setting F77... gfortran\n", "using F90... gfortran\n", - "setting FFLAGS... -O3 -g\n", - "setting F90FLAGS... $(FFLAGS) -x f95-cpp-input\n", + "setting FFLAGS... -O3 -g -fallow-argument-mismatch\n", + "setting F90FLAGS... $(FFLAGS) -cpp\n", "setting FFLAGS_NOOPT... -O0 -g\n", "setting CPP... cpp\n", - "setting CPPFLAGS... -P -traditional\n", + "setting CPPFLAGS... -P -traditional -Uvector\n", "setting LD... mpif90\n", "setting LDFLAGS... -g\n", "checking whether make sets $(MAKE)... yes\n", "checking whether Fortran files must be preprocessed... no\n", + "checking whether we are using the GNU Fortran 77 compiler... yes\n", + "checking whether gfortran accepts -g... yes\n", "checking for library containing dgemm... -lmkl_gf_lp64\n", "setting BLAS_LIBS... -lmkl_gf_lp64 -lmkl_sequential -lmkl_core\n", - "checking for library containing dspev... none required\n", - "checking how to run the C preprocessor... gcc -E\n", - "checking for grep that handles long lines and -e... /usr/bin/grep\n", - "checking for egrep... /usr/bin/grep -E\n", - "checking for ANSI C header files... yes\n", - "checking for sys/types.h... yes\n", - "checking for sys/stat.h... yes\n", - "checking for stdlib.h... yes\n", - "checking for string.h... yes\n", - "checking for memory.h... yes\n", - "checking for strings.h... yes\n", - "checking for inttypes.h... yes\n", - "checking for stdint.h... yes\n", - "checking for unistd.h... yes\n", - "checking FFT... checking for DftiComputeForward in -lmkl_intel_lp64... yes\n", - "checking for /opt/intel/Compiler/*/*/mkl/include/mkl_dfti.f90... no\n", - "checking for /opt/intel/mkl/*/include/mkl_dfti.f90... no\n", - "checking for /opt/intel/mkl*/include/mkl_dfti.f90... no\n", - "checking for /include/mkl_dfti.f90... no\n", - "checking for library containing dfftw_execute_dft... -lfftw3\n", - " -lfftw3 \n", + "checking FFT... \n", "checking MASS... \n", "checking for library containing mpi_init... none required\n", "checking for library containing pdgemr2d... no\n", @@ -9389,15 +8653,15 @@ "checking for ranlib... ranlib\n", "checking for wget... wget -O\n", "setting WGET... wget -O\n", - "setting DFLAGS... -D__FFTW3 -D__MPI\n", - "setting IFLAGS... -I$(TOPDIR)/include -I$(TOPDIR)/FoX/finclude -I$(TOPDIR)/S3DE/iotk/include/\n", + "setting DFLAGS... -D__DFTI -D__MPI\n", + "setting IFLAGS... -I$(TOPDIR)/include -I$(TOPDIR)/FoX/finclude -I/opt/intel/mkl/include\n", "configure: creating ./config.status\n", "config.status: creating install/make_lapack.inc\n", "config.status: creating include/configure.h\n", "config.status: creating make.inc\n", "config.status: creating configure.msg\n", "config.status: creating install/make_wannier90.inc\n", - "config.status: creating include/c_defs.h\n", + "config.status: creating include/qe_cdefs.h\n", "--------------------------------------------------------------------\n", "ESPRESSO can take advantage of several optimized numerical libraries\n", "(essl, fftw, mkl...). This configure script attempts to find them,\n", @@ -9407,8 +8671,10 @@ "The following libraries have been found:\n", " BLAS_LIBS= -lmkl_gf_lp64 -lmkl_sequential -lmkl_core\n", " LAPACK_LIBS=\n", - " FFT_LIBS= -lfftw3 \n", + " FFT_LIBS=\n", " HDF5_LIBS=\n", + " \n", + "\n", "Please check if this is what you expect.\n", "\n", "If any libraries are missing, you may specify a list of directories\n", @@ -9432,47 +8698,32 @@ ], "metadata": { "id": "Mifx2wClbKtV", - "outputId": "693f0a65-9684-4159-fce4-a25cca381886", + "outputId": "7ed31bda-381a-45b6-82c5-a94302e4fc13", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": 18, + "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "basisset.py\t\t pyscf_analyzer.py\n", - "bundle.py\t\t pyscf_input.py\n", - "debug.py\t\t pyscf_sim.py\n", - "developer.py\t\t qmcpack_analyzer_base.py\n", - "execute.py\t\t qmcpack_analyzer.py\n", - "fileio.py\t\t qmcpack_converters.py\n", - "gamess_analyzer.py\t qmcpack_input.py\n", - "gamess_input.py\t\t qmcpack_method_analyzers.py\n", - "gamess.py\t\t qmcpack_property_analyzers.py\n", - "gaussian_process.py\t qmcpack.py\n", - "generic.py\t\t qmcpack_quantity_analyzers.py\n", - "grid_functions.py\t qmcpack_result_analyzers.py\n", - "hdfreader.py\t\t quantum_package_analyzer.py\n", - "machines.py\t\t quantum_package_input.py\n", - "memory.py\t\t quantum_package.py\n", - "nexus_base.py\t\t rmg_analyzer.py\n", - "nexus.py\t\t rmg_input.py\n", - "numerics.py\t\t rmg.py\n", - "observables.py\t\t simulation.py\n", - "periodic_table.py\t structure.py\n", - "physical_system.py\t superstring.py\n", - "plotting.py\t\t template_simulation.py\n", - "project_manager.py\t testing.py\n", - "pseudopotential.py\t unit_converter.py\n", - "pwscf_analyzer.py\t utilities.py\n", - "pwscf_data_reader.py\t vasp_analyzer.py\n", - "pwscf_input.py\t\t vasp_input.py\n", - "pwscf_postprocessors.py vasp.py\n", - "pwscf.py\t\t versions.py\n", - "__pycache__\t\t xmlreader.py\n" + "basisset.py\t nexus_base.py\t pyscf_analyzer.py\t\t rmg_analyzer.py\n", + "bundle.py\t nexus.py\t\t pyscf_input.py\t\t rmg_input.py\n", + "debug.py\t numerics.py\t pyscf_sim.py\t\t rmg.py\n", + "developer.py\t observables.py\t qmcpack_analyzer_base.py\t simulation.py\n", + "execute.py\t periodic_table.py\t qmcpack_analyzer.py\t structure.py\n", + "fileio.py\t physical_system.py qmcpack_converters.py\t superstring.py\n", + "gamess_analyzer.py plotting.py\t qmcpack_input.py\t\t template_simulation.py\n", + "gamess_input.py project_manager.py qmcpack_method_analyzers.py testing.py\n", + "gamess.py\t pseudopotential.py qmcpack_property_analyzers.py unit_converter.py\n", + "gaussian_process.py pwscf_analyzer.py\t qmcpack.py\t\t utilities.py\n", + "generic.py\t pwscf_data_reader.py qmcpack_quantity_analyzers.py vasp_analyzer.py\n", + "grid_functions.py pwscf_input.py\t qmcpack_result_analyzers.py vasp_input.py\n", + "hdfreader.py\t pwscf_postprocessors.py quantum_package_analyzer.py vasp.py\n", + "machines.py\t pwscf.py\t\t quantum_package_input.py\t versions.py\n", + "memory.py\t __pycache__\t quantum_package.py\t xmlreader.py\n" ] } ] @@ -9552,7 +8803,7 @@ "base_uri": "https://localhost:8080/" } }, - "execution_count": 9, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -9681,263 +8932,6 @@ } ] }, - { - "cell_type": "code", - "source": [ - "from jarvis.core.atoms import Atoms\n", - "from jarvis.db.figshare import data\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "dft_3d = data('dft_3d')\n", - "df=pd.DataFrame(dft_3d)\n", - "#Filter based on elements\n", - "def has_elements(atoms_dict = {}, my_element = ['Al','O']):\n", - " atoms = Atoms.from_dict(atoms_dict)\n", - " elements = atoms.elements\n", - " if set(my_element)==set(elements):\n", - " return True\n", - " else:\n", - " return np.nan\n", - "\n", - "df['el'] = df['atoms'].apply(lambda x: has_elements(atoms_dict = x, my_element = ['Si']))\n", - "df1 = df.dropna()\n", - "df_eform_filter = df1[df1['formation_energy_peratom']==0]\n", - "df_eform_filter\n", - "\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 361 - }, - "id": "FuLEmYXq9CKn", - "outputId": "0b35902f-b4c0-46e9-b73e-7deb4099cdc4" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Obtaining 3D dataset 76k ...\n", - "Reference:https://www.nature.com/articles/s41524-020-00440-1\n", - "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n", - "Loading the zipfile...\n", - "Loading completed.\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " jid spg_number spg_symbol formula formation_energy_peratom \\\n", - "25912 JVASP-24792 227 Fd-3m Si 0.0 \n", - "42489 JVASP-1002 227 Fd-3m Si -0.0 \n", - "\n", - " func optb88vdw_bandgap \\\n", - "25912 OptB88vdW 0.731 \n", - "42489 OptB88vdW 0.731 \n", - "\n", - " atoms slme \\\n", - "25912 {'lattice_mat': [[3.3641546699579417, -2.54062... 33.23 \n", - "42489 {'lattice_mat': [[3.3641499856336465, -2.50271... 33.23 \n", - "\n", - " magmom_oszicar ... poisson \\\n", - "25912 0.0 ... na \n", - "42489 0.0 ... 0.21 \n", - "\n", - " raw_files nat bulk_modulus_kv \\\n", - "25912 [DFPT,JVASP-24792.zip,https://ndownloader.figs... 2 na \n", - "42489 [FD-ELAST,JVASP-1002.zip,https://ndownloader.f... 2 87.27 \n", - "\n", - " shear_modulus_gv mbj_bandgap hse_gap reference search el \n", - "25912 na 1.277 na mp-149 -Si True \n", - "42489 63.28 1.277 1.22 mp-149 -Si True \n", - "\n", - "[2 rows x 65 columns]" - ], - "text/html": [ - "\n", - "
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25912JVASP-24792227Fd-3mSi0.0OptB88vdW0.731{'lattice_mat': [[3.3641546699579417, -2.54062...33.230.0...na[DFPT,JVASP-24792.zip,https://ndownloader.figs...2nana1.277namp-149-SiTrue
42489JVASP-1002227Fd-3mSi-0.0OptB88vdW0.731{'lattice_mat': [[3.3641499856336465, -2.50271...33.230.0...0.21[FD-ELAST,JVASP-1002.zip,https://ndownloader.f...287.2763.281.2771.22mp-149-SiTrue
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\".false.\",\n", - " },\n", - " \"electrons\": {\n", - " \"diagonalization\": \"'david'\",\n", - " \"mixing_mode\": \"'local-TF'\",\n", - " \"mixing_beta\": 0.3,\n", - " \"conv_thr\": \"1d-9\",\n", - " },\n", - " \"ions\": {\"ion_dynamics\": \"'bfgs'\"},\n", - " \"cell\": {\"cell_dynamics\": \"'bfgs'\", \"cell_dofree\": \"'all'\"},\n", - " }\n", - "qejob_relax = QEjob(\n", - " atoms=a_atoms,\n", - " input_params=relax,\n", - " output_file=\"relax.out\",\n", - " qe_cmd=qe_cmd,\n", - " jobname=\"relax\",\n", - " kpoints=kp,\n", - " input_file=\"arelax.in\",\n", - " url=None,\n", - " psp_dir=None,\n", - " psp_temp_name=None,\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "49lHcBLQ3VcZ", - "outputId": "4b61523b-9c8e-424a-9eb2-764156df8ce2" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Please cite for PSPs:\n", - "https://doi.org/10.1016/j.commatsci.2013.08.053\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "%%time\n", - "info = qejob_relax.runjob()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "r_0vpTN63m2U", - "outputId": "a14418ce-f3f7-48ff-b278-209ca20d8bbc" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Sanitizing Atoms.\n", - "cmd /content/q-e/bin/pw.x" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "anfNRjlNMO39" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "#Other data available in xml file\n", - "import pprint\n", - "pprint.pprint(ds.data)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Q6Drasbh5rK-", - "outputId": "6ca6f4ec-c735-41d9-b96c-06c9370ff3a5" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", - " '3.587914876297408E-01 '\n", - " '3.956384663773496E-01 '\n", - " '4.602641673201964E-01',\n", - " '@size': '8'},\n", - " 'k_point': {'#text': '8.822181873924251E-11 '\n", - " '3.377975356063768E-01 '\n", - " '3.677474007517164E-01',\n", - " '@weight': '6.0105184072126424E-003'},\n", - " 'npw': '1378',\n", - " 'occupations': {'#text': 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"authorship_tag": "ABX9TyO/oHYPcg60alMh4AXI4eLD", + "authorship_tag": "ABX9TyOdNN7GE6yjjzwkmY4CXX6p", "include_colab_link": true }, "kernelspec": { @@ -30,7 +30,7 @@ { "cell_type": "markdown", "source": [ - "#This notebook shows a few examples to use [JARVIS-Tools](https://github.com/usnistgov/jarvis), [AtomQC](https://github.com/usnistgov/atomqc) and [Qiskit](https://qiskit.org/) packages to generate electronic bandstructure for [Aluminum](https://www.ctcms.nist.gov/~knc6/static/JARVIS-DFT/JVASP-816.xml). \n", + "#This notebook shows a few examples to use [JARVIS-Tools](https://github.com/usnistgov/jarvis), [AtomQC](https://github.com/usnistgov/atomqc) and [Qiskit](https://qiskit.org/) packages to generate electronic bandstructure for [Aluminum](https://www.ctcms.nist.gov/~knc6/static/JARVIS-DFT/JVASP-816.xml).\n", "#For details see: https://iopscience.iop.org/article/10.1088/1361-648X/ac1154/meta" ], "metadata": { @@ -53,10 +53,10 @@ "base_uri": "https://localhost:8080/" }, "id": "6tqFWYPHewZW", - "outputId": "2225ec88-787e-4574-f294-33af243ef78e" + "outputId": "998fabfb-7152-455f-eaa0-dbb02e73341e" }, "source": [ - "!pip install jarvis-tools qiskit\n" + "!pip install -q jarvis-tools qiskit==0.41.1\n" ], "execution_count": 1, "outputs": [ @@ -64,84 +64,33 @@ "output_type": "stream", "name": "stdout", "text": [ - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Collecting jarvis-tools\n", - " Downloading jarvis_tools-2023.1.8-py2.py3-none-any.whl (973 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m973.3/973.3 KB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting qiskit\n", - " Downloading qiskit-0.41.0.tar.gz (14 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.8/dist-packages (from jarvis-tools) (2.25.1)\n", - "Collecting xmltodict>=0.11.0\n", - 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This behaviour is the source of the following dependency conflicts.\n", + "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.28.2 which is incompatible.\n", + "yfinance 0.2.31 requires requests>=2.31, but you have requests 2.28.2 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" ] } ] @@ -209,7 +158,7 @@ ], "metadata": { "id": "HgCLxw4ZKtNw", - "outputId": "c7eaca0b-635e-4f97-a64f-65b1f1999d36", + "outputId": "31484b52-ab68-4894-b1c8-14b2310d8aa8", "colab": { "base_uri": "https://localhost:8080/" } @@ -228,7 +177,7 @@ "output_type": "stream", "name": "stderr", "text": [ - "100%|██████████| 6.00M/6.00M [00:02<00:00, 2.85MiB/s]\n" + "100%|██████████| 6.00M/6.00M [00:00<00:00, 7.13MiB/s]\n" ] }, { @@ -271,7 +220,7 @@ "base_uri": "https://localhost:8080/" }, "id": "SKUiv7WWjAk6", - "outputId": "831d294e-d367-4782-c344-84452136c84a" + "outputId": "04b2ca35-9f96-4ee2-90c8-52ca67ca2378" }, "execution_count": 3, "outputs": [ @@ -307,6 +256,7 @@ { "cell_type": "code", "source": [ + "%%time\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from jarvis.io.qiskit.inputs import get_bandstruct\n", @@ -316,32 +266,18 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 560 + "height": 519 }, "id": "cABTcdL9jUbL", - "outputId": "c1436b07-92ca-4fb3-ee7b-44340e15a3b4" + "outputId": "a7305f6b-f1ad-4f2b-8b4c-b6a496ea5547" }, - "execution_count": 6, + "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ - "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1317: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n", - "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1317: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n", - "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1317: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n", - "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1317: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n", - "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1317: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n", - "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1317: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n", - "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1317: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n", - "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1317: ComplexWarning: Casting complex values to real discards the imaginary part\n", + "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py:1335: ComplexWarning: Casting complex values to real discards the imaginary part\n", " return np.asarray(x, float)\n" ] }, @@ -349,13 +285,19 @@ "output_type": "display_data", "data": { "text/plain": [ - "
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" ], - "image/png": 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\n" + "image/png": 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\n" }, - "metadata": { - "needs_background": "light" - } + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 7min 18s, sys: 7.94 s, total: 7min 26s\n", + "Wall time: 7min 32s\n" + ] } ] }, @@ -369,9 +311,9 @@ "base_uri": "https://localhost:8080/" }, "id": "nsScEN5isOUY", - "outputId": "41cd4f4c-e57b-4f4d-cfb2-ef7461b1537f" + "outputId": "22ee7c31-2d5f-43b5-9072-93d610dfe448" }, - "execution_count": 8, + "execution_count": 5, "outputs": [ { "output_type": "execute_result", @@ -381,7 +323,7 @@ ] }, "metadata": {}, - "execution_count": 8 + "execution_count": 5 } ] }, @@ -395,9 +337,9 @@ "base_uri": "https://localhost:8080/" }, "id": "i39QMiGesq8e", - "outputId": "c21aa72e-2574-411a-bc86-52ae3a7c8adf" + "outputId": "3c963448-15de-47de-b5b8-1bf6b9badfaa" }, - "execution_count": 10, + "execution_count": 6, "outputs": [ { "output_type": "execute_result", @@ -407,7 +349,7 @@ ] }, "metadata": {}, - "execution_count": 10 + "execution_count": 6 } ] }, @@ -422,7 +364,7 @@ "metadata": { "id": "mEQ1T6GwswL_" }, - "execution_count": 17, + "execution_count": 7, "outputs": [] }, { @@ -436,15 +378,15 @@ "base_uri": "https://localhost:8080/" }, "id": "CUR6oV1Atvcp", - "outputId": "1056a650-5919-4322-f877-c8b2f2ef8489" + "outputId": "5d08efeb-069c-4982-8441-c29343adc134" }, - "execution_count": 20, + "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "0.0029637342224714384\n" + "0.0029975642955934255\n" ] } ] @@ -461,7 +403,7 @@ "id": "lBgWjCAfwMoK", "outputId": "0588be63-0c09-453e-a40b-fad974840bab" }, - "execution_count": 25, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -487,7 +429,7 @@ "id": "660T4gJEwO-A", "outputId": "7431f4d0-d0f2-4192-fdf4-257aae9cdc58" }, - "execution_count": 26, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -524,7 +466,7 @@ "id": "f4snNdGFwR1r", "outputId": "d9973a84-9c35-47ec-fc03-1accb4183bf8" }, - "execution_count": 27, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -550,7 +492,7 @@ "id": "koGNa7mewIdx", "outputId": "b86923e0-af47-45aa-8e84-0307cdc9bc2b" }, - "execution_count": 24, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -599,7 +541,7 @@ "id": "H6oZFhLpuN95", "outputId": "72721f0e-9cdc-4f7d-9f66-f2cf95ad6723" }, - "execution_count": 21, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -672,7 +614,7 @@ "base_uri": "https://localhost:8080/" } }, - "execution_count": 31, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -695,7 +637,7 @@ "base_uri": "https://localhost:8080/" } }, - "execution_count": 32, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -719,7 +661,7 @@ "id": "I4TSo6kruWYN", "outputId": "31a32723-4919-4114-a38a-b09d8f7f7828" }, - "execution_count": 30, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -783,7 +725,7 @@ "base_uri": "https://localhost:8080/" } }, - "execution_count": 28, + "execution_count": null, "outputs": [ { "output_type": "stream", diff --git a/jarvis-tools-notebooks/ThreeBodyTB_julia.ipynb b/jarvis-tools-notebooks/ThreeBodyTB_julia.ipynb index f9e7b0d..d2c3b59 100644 --- a/jarvis-tools-notebooks/ThreeBodyTB_julia.ipynb +++ b/jarvis-tools-notebooks/ThreeBodyTB_julia.ipynb @@ -159,26 +159,26 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "f7fc91da-65bd-4c00-c758-82ea99aa7ec3" + "outputId": "de503ebd-e579-4d40-cd42-62693a930343" }, "source": [ "versioninfo()" ], - "execution_count": null, + "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Julia Version 1.8.2\n", - "Commit 36034abf260 (2022-09-29 15:21 UTC)\n", + "Julia Version 1.9.2\n", + "Commit e4ee485e909 (2023-07-05 09:39 UTC)\n", "Platform Info:\n", " OS: Linux (x86_64-linux-gnu)\n", " CPU: 2 × Intel(R) Xeon(R) CPU @ 2.20GHz\n", " WORD_SIZE: 64\n", " LIBM: libopenlibm\n", - " LLVM: libLLVM-13.0.1 (ORCJIT, broadwell)\n", - " Threads: 2 on 2 virtual cores\n", + " LLVM: libLLVM-14.0.6 (ORCJIT, broadwell)\n", + " Threads: 3 on 2 virtual cores\n", "Environment:\n", " LD_LIBRARY_PATH = /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n", " JULIA_NUM_THREADS = 2\n" @@ -218,18 +218,607 @@ "base_uri": "https://localhost:8080/" }, "id": "YjM_qq54lCcs", - "outputId": "8ef2a5ed-23c5-4506-8afe-bb0a3abd85c6" + "outputId": "fb7b10d1-7a27-4520-fa8d-fd8ba7d41fd6" }, - "execution_count": null, + "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ + "\u001b[32m\u001b[1m Cloning\u001b[22m\u001b[39m git-repo `https://github.com/usnistgov/ThreeBodyTB.jl`\n", "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m git-repo `https://github.com/usnistgov/ThreeBodyTB.jl`\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `~/.julia/registries/General.toml`\n", + "\u001b[32m\u001b[1m 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"\u001b[33m⌅\u001b[39m \u001b[90m[e9f186c6] \u001b[39m\u001b[92m+ Libffi_jll v3.2.2+1\u001b[39m\n", + " \u001b[90m[d4300ac3] \u001b[39m\u001b[92m+ Libgcrypt_jll v1.8.7+0\u001b[39m\n", + " \u001b[90m[7e76a0d4] \u001b[39m\u001b[92m+ Libglvnd_jll v1.6.0+0\u001b[39m\n", + " \u001b[90m[7add5ba3] \u001b[39m\u001b[92m+ Libgpg_error_jll v1.42.0+0\u001b[39m\n", + " \u001b[90m[94ce4f54] \u001b[39m\u001b[92m+ Libiconv_jll v1.17.0+0\u001b[39m\n", + " \u001b[90m[4b2f31a3] \u001b[39m\u001b[92m+ Libmount_jll v2.35.0+0\u001b[39m\n", + " \u001b[90m[89763e89] \u001b[39m\u001b[92m+ Libtiff_jll v4.5.1+1\u001b[39m\n", + " \u001b[90m[38a345b3] \u001b[39m\u001b[92m+ Libuuid_jll v2.36.0+0\u001b[39m\n", + " \u001b[90m[856f044c] \u001b[39m\u001b[92m+ MKL_jll v2023.2.0+0\u001b[39m\n", + " \u001b[90m[e7412a2a] \u001b[39m\u001b[92m+ Ogg_jll v1.3.5+1\u001b[39m\n", + "\u001b[33m⌅\u001b[39m \u001b[90m[458c3c95] \u001b[39m\u001b[92m+ OpenSSL_jll v1.1.23+0\u001b[39m\n", + " \u001b[90m[efe28fd5] \u001b[39m\u001b[92m+ OpenSpecFun_jll v0.5.5+0\u001b[39m\n", + " \u001b[90m[91d4177d] \u001b[39m\u001b[92m+ Opus_jll v1.3.2+0\u001b[39m\n", + " \u001b[90m[30392449] \u001b[39m\u001b[92m+ Pixman_jll v0.42.2+0\u001b[39m\n", + " \u001b[90m[c0090381] \u001b[39m\u001b[92m+ Qt6Base_jll v6.5.2+2\u001b[39m\n", + " \u001b[90m[a44049a8] \u001b[39m\u001b[92m+ Vulkan_Loader_jll v1.3.243+0\u001b[39m\n", + " \u001b[90m[a2964d1f] \u001b[39m\u001b[92m+ Wayland_jll v1.21.0+1\u001b[39m\n", + " \u001b[90m[2381bf8a] \u001b[39m\u001b[92m+ Wayland_protocols_jll v1.25.0+0\u001b[39m\n", + " \u001b[90m[02c8fc9c] \u001b[39m\u001b[92m+ XML2_jll v2.11.5+0\u001b[39m\n", + " \u001b[90m[aed1982a] \u001b[39m\u001b[92m+ XSLT_jll v1.1.34+0\u001b[39m\n", + " \u001b[90m[ffd25f8a] \u001b[39m\u001b[92m+ XZ_jll v5.4.4+0\u001b[39m\n", + " \u001b[90m[f67eecfb] \u001b[39m\u001b[92m+ Xorg_libICE_jll v1.0.10+1\u001b[39m\n", + " \u001b[90m[c834827a] \u001b[39m\u001b[92m+ Xorg_libSM_jll v1.2.3+0\u001b[39m\n", + " \u001b[90m[4f6342f7] 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constraints from upgrading. To see why use `status --outdated -m`\n", + "\u001b[32m\u001b[1m Building\u001b[22m\u001b[39m ThreeBodyTB → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/b962e819de62f97743a053c6c1b8ea0c7a3137fa/build.log`\n", + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mTensorCore\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mLaTeXStrings\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mAbstractFFTs\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mStatsAPI\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mContour\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mSuppressor\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mConcurrentUtilities\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mPipe\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mUnPack\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mMeasures\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mXorg_libICE_jll\u001b[39m\n", + "\u001b[32m ✓ 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\u001b[39mMKL_jll [856f044c-d86e-5d09-b602-aeab76dc8ba7]\u001b[33m\n", + "│ \u001b[39m\u001b[32m\u001b[1m Downloading\u001b[22m\u001b[39m artifact: MKL\u001b[33m\n", + "└ \u001b[39m\n", "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", - "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/.julia/environments/v1.8/Project.toml`\n", - "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/.julia/environments/v1.8/Manifest.toml`\n" + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.9/Project.toml`\n", + " \u001b[90m[91a5bcdd] \u001b[39m\u001b[92m+ Plots v1.39.0\u001b[39m\n", + "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/.julia/environments/v1.9/Manifest.toml`\n" ] } ] @@ -243,12 +832,73 @@ "id": "CkBWVGsymuU5" } }, + { + "cell_type": "code", + "source": [ + "# https://www.ctcms.nist.gov/~knc6/static/JARVIS-DFT/JVASP-1109.xml\n", + "pos=\"\"\"Sn4S4\n", + "1.0\n", + "4.033601508001088 1e-16 0.0\n", + "0.0 4.394493698354189 0.0\n", + "0.0 0.0 11.424318648637847\n", + "Sn S\n", + "4 4\n", + "Cartesian\n", + "1.0084 1.6378941450641458 7.094661401047861\n", + "3.0252 2.756595854935855 4.329658598952143\n", + "3.0252 3.8351391450641454 10.041818598952139\n", + "1.0084 0.5593508549358553 1.3825014010478602\n", + "1.0084 0.1224744620369969 4.008692432047574\n", + "3.0252 4.272015537963004 7.415627567952426\n", + "3.0252 2.3197194620369967 1.7034675679524252\n", + "1.0084 2.074770537963003 9.720852432047574\n", + "\"\"\"\n", + "open(\"POSCAR\",\"w\") do file\n", + "write(file,string(pos))\n", + "end\n", + "c = makecrys(\"POSCAR\")" + ], + "metadata": { + "id": "iKWCP2C2L1YW", + "outputId": "06842142-cdac-4789-fad2-b18731857d09", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Units: Å\n", + "\n", + "A1= 4.03360 0.00000 0.00000\n", + "A2= 0.00000 4.39450 0.00000\n", + "A3= 0.00000 0.00000 11.42432\n", + "\n", + "Sn 0.25000 0.37272 0.62101\n", + "Sn 0.75000 0.62728 0.37899\n", + "Sn 0.75000 0.87272 0.87899\n", + "Sn 0.25000 0.12728 0.12101\n", + "S 0.25000 0.02787 0.35089\n", + "S 0.75000 0.97213 0.64911\n", + "S 0.75000 0.52787 0.14911\n", + "S 0.25000 0.47213 0.85089\n" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, { "cell_type": "code", "source": [ "A = [0 2.73 2.73; 2.73 0 2.73; 2.73 2.73 0] #angstrom lattice vectors\n", "pos = [0 0 0; 0.25 0.25 0.25] #crystal coordinates\n", "atoms = [\"Al\", \"P\"] #strings or symbols are fine\n", + "\n", "c = makecrys(A, pos, atoms)" ], "metadata": { @@ -256,9 +906,9 @@ "base_uri": "https://localhost:8080/" }, "id": "n2fIIVknkwmY", - "outputId": "4374a6c3-ead7-4b5c-98cb-55057018e2a1" + "outputId": "c7a77b69-7702-4e0d-b7af-f686843d9e62" }, - "execution_count": null, + "execution_count": 3, "outputs": [ { "output_type": "execute_result", @@ -275,7 +925,7 @@ ] }, "metadata": {}, - "execution_count": 42 + "execution_count": 3 } ] }, @@ -296,7 +946,7 @@ "base_uri": "https://localhost:8080/", "height": 456 }, - "outputId": "d10c65b6-f6c0-484c-f400-0b2277757752" + "outputId": "02cf4e4a-3e12-4134-deed-5da726886e94" }, "source": [ "c222 = c * [2,2,2]\n", @@ -315,7 +965,78 @@ { "output_type": "display_data", "data": { - "image/svg+xml": "\n\n\n \n \n \n\n\n\n \n \n \n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" + "image/png": 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", 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"execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", + "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/els/coef.el.2bdy.Sn.xml.gz\n", + "Tuple{Symbol, Symbol}\n", + "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/els/coef.el.3bdy.Sn.xml.gz\n", + "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/binary/coef.el.2bdy.S.Sn.xml.gz\n", + "Tuple{Symbol, Symbol}\n", + "Tuple{Symbol, Symbol}\n", + "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/binary/coef.el.3bdy.S.Sn.xml.gz\n", + "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/els/coef.el.2bdy.S.xml.gz\n", + "Tuple{Symbol, Symbol}\n", + "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/els/coef.el.3bdy.S.xml.gz\n", "\n", - " 0.008080 seconds (64.86 k allocations: 2.263 MiB)\n", - "DO CLASSICAL true\n", + "calc tb\n", "\n", "START SCF ----------------\n", - "SCF CALC 0001 energy -11.02359198 \n", - "SCF CALC 0002 energy -11.02321829 en_diff: 3.736900E-04 dq_diff: 1.744166E+00 mix: 1.000000E+00 \n", - "SCF CALC 0003 energy -11.02244087 en_diff: 7.774142E-04 dq_diff: 1.665710E+00 mix: 1.000000E+00 \n", - "SCF CALC 0004 energy -10.90581647 en_diff: 1.166244E-01 dq_diff: 2.711302E-03 mix: 1.000000E+00 \n", - "SCF CALC 0005 energy -10.90558098 en_diff: 2.354937E-04 dq_diff: 8.664067E-04 mix: 1.000000E+00 \n", + "SCF CALC 0001 energy -37.51018690 \n", + "SCF CALC 0002 energy -37.50406504 en_diff: 6.121863E-03 dq_diff: 5.250650E+00 mix: 1.000000E+00 \n", + "SCF CALC 0003 energy -37.49160900 en_diff: 1.245604E-02 dq_diff: 4.936322E+00 mix: 1.000000E+00 \n", + "SCF CALC 0004 energy -36.52558896 en_diff: 9.660200E-01 dq_diff: 2.664226E-01 mix: 1.000000E+00 \n", + "SCF CALC 0005 energy -36.46117203 en_diff: 6.441693E-02 dq_diff: 9.083951E-02 mix: 1.000000E+00 \n", + "SCF CALC 0006 energy -36.38924636 en_diff: 7.192566E-02 dq_diff: 9.905912E-02 mix: 1.000000E+00 \n", + "SCF CALC 0007 energy -36.42879743 en_diff: 3.955107E-02 dq_diff: 4.597046E-03 mix: 1.000000E+00 \n", + "SCF CALC 0008 energy -36.42512050 en_diff: 3.676935E-03 dq_diff: 5.117216E-03 mix: 1.000000E+00 \n", + "SCF CALC 0009 energy -36.42731532 en_diff: 2.194828E-03 dq_diff: 6.794504E-04 mix: 1.000000E+00 \n", + "SCF CALC 0010 energy -36.42712317 en_diff: 1.921548E-04 dq_diff: 1.717279E-04 mix: 1.000000E+00 \n", "\n", - "YES convergence in 5 iters, energy -10.905580977225343 eV \n", + "YES convergence in 10 iters, energy -36.42712316978109 eV \n", "END SCF ------------------\n", "\n", - "ΔQ = [-0.82, 0.82]\n", + "ΔQ = [-0.46, -0.46, -0.46, -0.46, 0.46, 0.46, 0.46, 0.46]\n", "\n", "scf_energy success, done\n", "\n", - "Formation energy: -0.717 eV/atom\n", + "Formation energy: -0.501 eV/atom\n", "\n" ] } @@ -392,9 +1127,9 @@ "base_uri": "https://localhost:8080/" }, "id": "VxnUjEvLn_Iv", - "outputId": "33846bf6-b97d-482a-ec59-7a93abaeaf22" + "outputId": "5aaf3a7a-f68f-4997-d63d-7c49e8c51ebd" }, - "execution_count": null, + "execution_count": 11, "outputs": [ { "output_type": "execute_result", @@ -405,39 +1140,104 @@ "\n", "Units: Å\n", "\n", - "A1= 0.00000 2.73000 2.73000\n", - "A2= 2.73000 0.00000 2.73000\n", - "A3= 2.73000 2.73000 0.00000\n", + "A1= 4.03360 0.00000 0.00000\n", + "A2= 0.00000 4.39450 0.00000\n", + "A3= 0.00000 0.00000 11.42432\n", "\n", - "Al 0.00000 0.00000 0.00000\n", - "P 0.25000 0.25000 0.25000\n", + "Sn 0.25000 0.37272 0.62101\n", + "Sn 0.75000 0.62728 0.37899\n", + "Sn 0.75000 0.87272 0.87899\n", + "Sn 0.25000 0.12728 0.12101\n", + "S 0.25000 0.02787 0.35089\n", + "S 0.75000 0.97213 0.64911\n", + "S 0.75000 0.52787 0.14911\n", + "S 0.25000 0.47213 0.85089\n", "\n", "\n", - "nelec: 8.0; nspin (hoppings): 1\n", + "nelec: 40.0; nspin (hoppings): 1\n", "within_fit: true ; scf: true; scfspin: false\n", - "calculated energy: -10.906 eV\n", - "formation energy: -0.717 eV\n", - "charges : [-0.82, 0.82]\n", + "calculated energy: -36.427 eV\n", + "formation energy: -0.501 eV\n", + "charges : [-0.46, -0.46, -0.46, -0.46, 0.46, 0.46, 0.46, 0.46]\n", "\n", "\n", - "tight binding real space object; nwan = 8, nr = 99, nonorth = true, scf = true, scfmagnetic = false, nspin = 1\n", + "tight binding real space object; nwan = 32, nr = 55, nonorth = true, scf = true, scfmagnetic = false, nspin = 1\n", "\n", "\n", "\n" ] }, "metadata": {}, - "execution_count": 27 + "execution_count": 11 }, { "output_type": "stream", "name": "stdout", "text": [ - "efermi : -1.458 eV\n" + "efermi : -3.256 eV\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "println(\"directgap, indirectgap, gaptype, bandwidth\",string(ThreeBodyTB.BandStruct.band_summary(tbc)))" + ], + "metadata": { + "id": "kwkQ7iQk0T9z", + "outputId": "da50262f-5453-44af-d0bf-a97c786c605d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "using kgrid [8, 8, 4]\n", + "directgap, indirectgap, gaptype, bandwidth(1.1343580062045882, 0.6989805320623194, :indirect, 13.650057267504023)\n" ] } ] }, + { + "cell_type": "code", + "source": [ + "#vects, vals, hk, sk, vals0 = ThreeBodyTB.TB.Hk(tbc,[.5,.5,0])" + ], + "metadata": { + "id": "LuHt4zK17KkS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#using LinearAlgebra\n", + "#abs.(vects^2)" + ], + "metadata": { + "id": "13ht9BQA7RhU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#using Plots\n", + "#heatmap(abs.(vects^2), color = :greys)" + ], + "metadata": { + "id": "hdwP_8kx-HLw" + }, + "execution_count": null, + "outputs": [] + }, { "cell_type": "markdown", "source": [ @@ -455,48 +1255,8840 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 875 + "height": 1000 }, "id": "7MzdWNMbl4uD", - "outputId": "bf236d20-9049-4345-cb2d-5e80cfda0354" + "outputId": "3324612a-01df-4ea2-efe2-c0afc7042936" }, - "execution_count": null, + "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Symmetry info\n", - "Space group # 216 F-43m F -4 2 3\n", - "Point group -43m\n", - "spg 216 F-43m F\n", - "7.296 7.296 7.296 | 60.0 60.0 60.0\n", - "centering F true\n", - "align fermi -0.10712655296178408 -------------------------\n", + "Space group # 62 Pnma -P 2ac 2n\n", + "Point group mmm\n", + "spg 62 Pnma P\n", + "21.589 7.622 8.304 | 90.0 90.0 90.0\n", + "need to rerun in standard structure for symmetry\n", + "calc tb\n", + "DO CLASSICAL true\n", + "\n", + "START SCF ----------------\n", + "SCF CALC 0001 energy -36.42716677 \n", + "SCF CALC 0002 energy -36.42715825 en_diff: 8.517569E-06 dq_diff: 2.644200E-04 mix: 1.000000E+00 \n", + "\n", + "YES convergence in 2 iters, energy -36.42715825165702 eV \n", + "END SCF ------------------\n", + "\n", + "ΔQ = [-0.46, -0.46, -0.46, -0.46, 0.46, 0.46, 0.46, 0.46]\n", + "\n", + "align fermi -0.23928810074255694 -------------------------\n", "no display\n", - "grid [16, 16, 16]\n", - "dos fermi -0.10620771246645189 xxxxxxxxxxxxx---------------\n", + "grid [6, 13, 13]\n", + "dos fermi -0.23658050818398535 xxxxxxxxxxxxx---------------\n", "nspin 1\n", "Projection type: atomic\n", "PROJ\n", - "(\"P\", [5, 6, 7, 8], 4)\n", - "(\"Al\", [1, 2, 3, 4], 4)\n", - "proj_inds [5, 6, 7, 8]\n", - "proj_inds [1, 2, 3, 4]\n", - "(4096, 8, 2, 1)\n", - "Int DOS 7.999997037813593\n", - "Int DOS occ 3.999999964331952\n", - "Int pDOS occ 1 : 2.7964786418386187\n", - "Int pDOS occ 2 : 1.2035213224933339\n", - "Int pDOS all 1 : 3.999999090482648\n", - "Int pDOS all 2 : 3.999997947330942\n", - "DOS flit range (-14.523154815030251, 10.86007830775062)\n" + "(\"Sn\", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], 16)\n", + "(\"S\", [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], 16)\n", + "proj_inds [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\n", + "proj_inds [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]\n", + "(1014, 32, 2, 1)\n", + "Int DOS 31.999983243879903\n", + "Int DOS occ 19.994042467219874\n", + "Int pDOS occ 1 : 7.275583260299482\n", + "Int pDOS occ 2 : 12.718459206920393\n", + "Int pDOS all 1 : 15.999988935543461\n", + "Int pDOS all 2 : 15.999994308336435\n", + "DOS flit range (-15.056696574375582, 7.996930787952257)\n" ] }, { "output_type": "display_data", "data": { - "image/svg+xml": "\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n 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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, "metadata": {} } diff --git a/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb b/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb index 3be7832..d3f662d 100644 --- a/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb +++ b/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb @@ -28,256 +28,1384 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", "source": [ - "# Train ALIGNN-FF model, analyze the results and upload to JARVIS-Leaderbaord\n", - "1. Install `jarvis_leaderboard`, `alignn` and related libraries using pip\n", - "2. EV-curve and phonon bandstructure for FCC Copper\n", - "3. Use pretrained models for optimizing a few cubic systems\n", - "4. Train a Copper force-field from scratch\n", - "5. Analyze training results\n", - "6. Make JARVIS-Leaderboard entry\n" + "import os\n", + "os.chdir('/content')\n", + "if not os.path.exists('jarvis_leaderboard'):\n", + " !git clone https://github.com/usnistgov/jarvis_leaderboard.git\n", + "os.chdir('jarvis_leaderboard')\n", + "!pip install -e .\n", + "os.chdir('/content')" ], "metadata": { - "id": "fEA4sANdo0VT" - } + "id": "_k6ga4THb2B9", + "outputId": "fd5d8348-a1cd-4197-8db3-728b62814c29", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'jarvis_leaderboard'...\n", + "remote: Enumerating objects: 57856, done.\u001b[K\n", + "remote: Counting objects: 100% (6368/6368), done.\u001b[K\n", + "remote: Compressing objects: 100% (792/792), done.\u001b[K\n", + "remote: Total 57856 (delta 3607), reused 5829 (delta 3298), pack-reused 51488\u001b[K\n", + "Receiving objects: 100% (57856/57856), 381.00 MiB | 15.75 MiB/s, done.\n", + "Resolving deltas: 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This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires kaleido, which is not installed.\n", + "lida 0.0.10 requires python-multipart, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\n", + "llmx 0.0.15a0 requires cohere, which is not installed.\n", + "llmx 0.0.15a0 requires openai, which is not installed.\n", + "llmx 0.0.15a0 requires tiktoken, which is not installed.\n", + "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed annotated-types-0.6.0 colorama-0.4.6 ghp-import-2.1.0 jarvis-leaderboard-2023.12.16 jarvis-tools-2023.12.12 mergedeep-1.3.4 mkdocs-1.5.3 mkdocs-material-9.5.3 mkdocs-material-extensions-1.3.1 paginate-0.5.6 pathspec-0.12.1 pydantic-2.5.3 pydantic-core-2.14.6 pymdown-extensions-10.7 pyyaml-env-tag-0.1 rouge-1.0.1 spglib-2.2.0 typing-extensions-4.9.0 watchdog-3.0.0 xmltodict-0.13.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "!pip install -q dgl -f https://data.dgl.ai/wheels/cu118/repo.html\n", + "!pip install -q dglgo -f https://data.dgl.ai/wheels-test/repo.html\n", + "!pip install -q alignn" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "A2yF-sgRv6a0", + "outputId": "c1fd5aaf-6339-4244-9482-2a4b40154baf" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.8/93.8 MB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.5/63.5 kB\u001b[0m \u001b[31m737.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K 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\u001b[32m63.3/63.3 MB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m153.0/153.0 kB\u001b[0m \u001b[31m20.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for lit (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires kaleido, which is not installed.\n", + "lida 0.0.10 requires python-multipart, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\n", + "llmx 0.0.15a0 requires cohere, which is not installed.\n", + "llmx 0.0.15a0 requires openai, which is not installed.\n", + "llmx 0.0.15a0 requires tiktoken, which is not installed.\n", + "confection 0.1.4 requires pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4, but you have pydantic 1.8.1 which is incompatible.\n", + "dglgo 0.0.2 requires pydantic>=1.9.0, but you have pydantic 1.8.1 which is incompatible.\n", + "inflect 7.0.0 requires pydantic>=1.9.1, but you have pydantic 1.8.1 which is incompatible.\n", + "spacy 3.6.1 requires pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4, but you have pydantic 1.8.1 which is incompatible.\n", + "thinc 8.1.12 requires pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4, but you have pydantic 1.8.1 which is incompatible.\n", + "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.0.0 which is incompatible.\n", + "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.0.0 which is incompatible.\n", + "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.0.0 which is incompatible.\n", + "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.0.0 which is incompatible.\n", + "jarvis-leaderboard 2023.12.16 requires pydantic>=2.3.0, but you have pydantic 1.8.1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mCPU times: user 1.95 s, sys: 303 ms, total: 2.26 s\n", + "Wall time: 4min 1s\n" + ] + } + ] }, { "cell_type": "markdown", "source": [ - "# 1. Installation" + "Example configuration file" ], "metadata": { - "id": "pxn9Yf1dnO9L" + "id": "OIfymESSDare" } }, + { + "cell_type": "code", + "source": [ + "!wget https://gist.githubusercontent.com/knc6/eb04b911cd5428bb2ac79b7622c0da26/raw/ffdcbbccc9488d536890a3a5ffd69313a2a458bd/config_mlearn_cu.json" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FoYHqz8av6YD", + "outputId": "42d33bcf-cbc0-4314-835e-8d203fbd0d89" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-01-08 02:10:10-- https://gist.githubusercontent.com/knc6/eb04b911cd5428bb2ac79b7622c0da26/raw/ffdcbbccc9488d536890a3a5ffd69313a2a458bd/config_mlearn_cu.json\n", + "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...\n", + "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|185.199.111.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 2034 (2.0K) [text/plain]\n", + "Saving to: ‘config_mlearn_cu.json’\n", + "\n", + "config_mlearn_cu.js 100%[===================>] 1.99K --.-KB/s in 0s \n", + "\n", + "2024-01-08 02:10:10 (26.5 MB/s) - ‘config_mlearn_cu.json’ saved [2034/2034]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!wget https://figshare.com/ndownloader/files/40357663 -O mlearn.json.zip" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "C7dK40z_xaEc", + "outputId": "638e801a-cc93-4109-b47f-4f3edd0f9f81" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-01-08 02:10:10-- https://figshare.com/ndownloader/files/40357663\n", + "Resolving figshare.com (figshare.com)... 54.155.30.132, 34.241.157.25, 2a05:d018:1f4:d000:fd71:4bc5:e7e5:3f50, ...\n", + "Connecting to figshare.com (figshare.com)|54.155.30.132|:443... connected.\n", + "HTTP request 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connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 2542319 (2.4M) [application/zip]\n", + "Saving to: ‘mlearn.json.zip’\n", + "\n", + "mlearn.json.zip 100%[===================>] 2.42M 1.91MB/s in 1.3s \n", + "\n", + "2024-01-08 02:10:13 (1.91 MB/s) - ‘mlearn.json.zip’ saved [2542319/2542319]\n", + "\n" + ] + } + ] + }, { "cell_type": "code", "source": [ "!pwd" ], "metadata": { - "id": "OcfP8iv-cSRq", - "outputId": "7437b28f-4301-4791-db35-323fff39322e", "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/" + }, + "id": "Hw3dVfYRxorg", + "outputId": "0d168143-0035-44dd-e0f0-33bf63bf32ec" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# withut cut-off function, grad weight 0.2\n", + "%%time\n", + "import os,json,torch\n", + "from jarvis.core.atoms import Atoms\n", + "from jarvis.db.jsonutils import loadjson, dumpjson\n", + "import json,zipfile\n", + "import zipfile\n", + "import json\n", + "import glob\n", + "import pandas as pd\n", + "import numpy as np\n", + "from jarvis.core.atoms import Atoms\n", + "import os\n", + "from alignn.ff.ff import AlignnAtomwiseCalculator, default_path, ForceField\n", + "import torch\n", + "from ase.stress import full_3x3_to_voigt_6_stress, voigt_6_to_full_3x3_stress\n", + "from jarvis.db.figshare import data\n", + "import subprocess\n", + "from subprocess import Popen, PIPE\n", + "mlearn = json.loads(\n", + " zipfile.ZipFile(\"/content/mlearn.json.zip\").read(\n", + " \"mlearn.json\"\n", + " )\n", + " )\n", + "example_config = loadjson(\"config_mlearn_cu.json\")\n", + "\n", + "run_dir='/content'\n", + "elements = [\"Si\",\"Cu\"] #,\"Ni\",\"Ge\",\"Mo\",\"Li\"]\n", + "mem = []\n", + "for element in elements:\n", + " os.chdir(run_dir)\n", + " dir_name = \"alff_\" + element\n", + " cmd='rm -rf '+dir_name\n", + " os.system(cmd)\n", + " if not os.path.exists(dir_name):\n", + " os.makedirs(dir_name)\n", + " benchmark_energies = (\n", + " \"jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/mlearn_\"\n", + " + element\n", + " + \"_energy.json.zip\"\n", + " )\n", + "\n", + " temp_energies = benchmark_energies.split(\"/\")[-1].split(\".zip\")[0]\n", + " energies = json.loads(\n", + " zipfile.ZipFile(benchmark_energies).read(temp_energies)\n", + " )\n", + " train_ids = list(energies[\"train\"].keys())\n", + " test_ids = list(energies[\"test\"].keys())\n", + " example_config[\"n_train\"] = len(train_ids)\n", + " example_config[\"n_val\"] = len(test_ids)\n", + " example_config[\"n_test\"] = len(test_ids)\n", + " example_config[\"model\"][\"graphwise_weight\"] = 1\n", + " example_config[\"model\"][\"gradwise_weight\"] = 1\n", + " example_config[\"epochs\"] = 100\n", + " example_config[\"batch_size\"] = 2\n", + "\n", + " example_config[\"keep_data_order\"] = True\n", + " config_name = dir_name + \"/config_\" + element + \".json\"\n", + " dumpjson(data=example_config, filename=config_name)\n", + "\n", + " train_energies = []\n", + " train_forces = []\n", + " train_stresses = []\n", + " train_structures = []\n", + " for i in mlearn:\n", + " if i[\"jid\"] in train_ids:\n", + " # print(i)\n", + " train_energies.append(i[\"energy\"])\n", + " train_forces.append(i[\"forces\"])\n", + " train_stresses.append(i[\"stresses\"])\n", + " atoms = Atoms.from_dict(i[\"atoms\"])\n", + " info = {}\n", + " info[\"jid\"] = i[\"jid\"]\n", + " info[\"atoms\"] = i[\"atoms\"]\n", + " # alignn uses intensive/energy oer atom quanitity\n", + " info[\"total_energy\"] = i[\"energy\"] / atoms.num_atoms\n", + " info[\"forces\"] = i[\"forces\"]\n", + " info[\"stresses\"] = i[\"stresses\"]\n", + " mem.append(info)\n", + " # Val same as test\n", + " test_energies = []\n", + " test_forces = []\n", + " test_stresses = []\n", + " test_structures = []\n", + " for i in mlearn:\n", + " if i[\"jid\"] in test_ids:\n", + " # print(i)\n", + " test_energies.append(i[\"energy\"])\n", + " test_forces.append(i[\"forces\"])\n", + " test_stresses.append(i[\"stresses\"])\n", + " atoms = Atoms.from_dict(i[\"atoms\"])\n", + " info = {}\n", + " info[\"jid\"] = i[\"jid\"]\n", + " info[\"atoms\"] = i[\"atoms\"]\n", + " # alignn uses intensive/energy oer atom quanitity\n", + " info[\"total_energy\"] = i[\"energy\"] / atoms.num_atoms\n", + " info[\"forces\"] = i[\"forces\"]\n", + " info[\"stresses\"] = i[\"stresses\"]\n", + " mem.append(info)\n", + " test_energies = []\n", + " test_forces = []\n", + " test_stresses = []\n", + " test_structures = []\n", + " for i in mlearn:\n", + " if i[\"jid\"] in test_ids:\n", + " # print(i)\n", + " test_energies.append(i[\"energy\"])\n", + " test_forces.append(i[\"forces\"])\n", + " test_stresses.append(i[\"stresses\"])\n", + " atoms = Atoms.from_dict(i[\"atoms\"])\n", + " #test_structures.append(atoms.pymatgen_converter())\n", + " info = {}\n", + " info[\"jid\"] = i[\"jid\"]\n", + " info[\"atoms\"] = i[\"atoms\"]\n", + " # alignn uses intensive/energy oer atom quanitity\n", + " info[\"total_energy\"] = i[\"energy\"] / atoms.num_atoms\n", + " info[\"forces\"] = i[\"forces\"]\n", + " info[\"stresses\"] = i[\"stresses\"]\n", + " mem.append(info)\n", + " filename = dir_name + \"/id_prop.json\"\n", + " dumpjson(data=mem, filename=filename)\n", + " cmd = (\n", + " \"train_folder_ff.py --root_dir \"\n", + " + dir_name\n", + " + \" --config \"\n", + " + config_name\n", + " + \" --output_dir \"\n", + " + dir_name\n", + " )\n", + " #cmd=\"train_folder_ff.py -h\"\n", + "\n", + " print(cmd)\n", + " #os.system(cmd)\n", + " subprocess.call(cmd, stdout=PIPE,shell=True)\n", + " #p1 = Popen(cmd, stdout=PIPE, shell=True)\n", + "\n", + "\n", + "################\n", + " model_path = dir_name\n", + "\n", + " # calc = AlignnAtomwiseCalculator(path=model_path)\n", + " calc = AlignnAtomwiseCalculator(\n", + " path=model_path,\n", + " force_mult_natoms=False,\n", + " force_multiplier=1,\n", + " stress_wt=-4800,\n", + " )\n", + "\n", + "\n", + "\n", + " def get_alignn_forces(atoms):\n", + " energy = 0.0\n", + " forces = np.zeros((atoms.num_atoms, 3))\n", + " stress = np.zeros((3, 3))\n", + " # try:\n", + " ase_atoms = atoms.ase_converter()\n", + " ase_atoms.calc = calc # M3GNetCalculator(potential=potential)\n", + " forces = np.array(ase_atoms.get_forces())\n", + " energy = ase_atoms.get_potential_energy()\n", + " stress = voigt_6_to_full_3x3_stress(ase_atoms.get_stress())\n", + " # except:\n", + " # print ('Failed for',atoms)\n", + " # pass\n", + " return energy, forces, stress\n", + "\n", + " # df = pd.DataFrame(mdata)\n", + " df = pd.DataFrame(\n", + " json.loads(\n", + " zipfile.ZipFile(\"mlearn.json.zip\").read(\n", + " \"mlearn.json\"\n", + " )\n", + " )\n", + " )\n", + " print(df)\n", + " #for i in glob.glob(\"../../benchmarks/AI/MLFF/*energy*.zip\"):\n", + " for i in glob.glob(\"/content/jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/*energy*.zip\"):\n", + " if \"mlearn\" in i and element in i:\n", + " fname_e = (\n", + " \"AI-MLFF-energy-\"\n", + " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " + \"-test-mae.csv\"\n", + " )\n", + " fname_f = (\n", + " \"AI-MLFF-forces-\"\n", + " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " + \"-test-multimae.csv\"\n", + " )\n", + " fname_s = (\n", + " \"AI-MLFF-stresses-\"\n", + " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " + \"-test-multimae.csv\"\n", + " )\n", + " f_e = open(fname_e, \"w\")\n", + " f_f = open(fname_f, \"w\")\n", + " f_s = open(fname_s, \"w\")\n", + "\n", + " f_e.write(\"id,prediction\\n\")\n", + " f_f.write(\"id,prediction\\n\")\n", + " f_s.write(\"id,prediction\\n\")\n", + "\n", + " print(i)\n", + " dat = json.loads(\n", + " zipfile.ZipFile(i).read(i.split(\"/\")[-1].split(\".zip\")[0])\n", + " )\n", + " print(dat[\"test\"])\n", + " for key, val in dat[\"test\"].items():\n", + " entry = df[df[\"jid\"] == key]\n", + " atoms = Atoms.from_dict(entry.atoms.values[0])\n", + " # print(key,val,df[df['jid']==key],atoms)\n", + " # energy,forces=get_alignn_forces(atoms)\n", + " energy, forces, stress = get_alignn_forces(atoms)\n", + " print(key, val, energy, atoms.num_atoms)\n", + " line = key + \",\" + str(energy) + \"\\n\"\n", + " f_e.write(line)\n", + " line = (\n", + " key\n", + " + \",\"\n", + " + str(\";\".join(map(str, np.array(forces).flatten())))\n", + " + \"\\n\"\n", + " )\n", + " f_f.write(line)\n", + " line = (\n", + " key\n", + " + \",\"\n", + " + str(\";\".join(map(str, np.array(stress).flatten())))\n", + " + \"\\n\"\n", + " )\n", + " f_s.write(line)\n", + " f_e.close()\n", + " f_f.close()\n", + " f_s.close()\n", + " zname = fname_e + \".zip\"\n", + " with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " myzip.write(fname_e)\n", + "\n", + " zname = fname_f + \".zip\"\n", + " with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " myzip.write(fname_f)\n", + "\n", + " zname = fname_s + \".zip\"\n", + " with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " myzip.write(fname_s)\n", + " # cmd = \"zip \" + fname_e + \".zip \" + fname_e\n", + " # os.system(cmd)\n", + " # cmd = \"zip \" + fname_f + \".zip \" + fname_f\n", + " # os.system(cmd)\n", + " # cmd = \"zip \" + fname_s + \".zip \" + fname_s\n", + " # os.system(cmd)\n", + " # cmd = \"rm \" + fname_e\n", + " # os.system(cmd)\n", + " # cmd = \"rm \" + fname_f\n", + " # os.system(cmd)\n", + " # cmd='rm '+fname_s\n", + " # os.system(cmd)\n", + " # break" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K8t_1dRwv6Vm", + "outputId": "9bcad2e9-ab2d-45d3-c72d-c15f7cd2913f" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DGL backend not selected or invalid. Assuming PyTorch for now.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable. Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n", + "train_folder_ff.py --root_dir alff_Si --config alff_Si/config_Si.json --output_dir alff_Si\n", + " jid atoms energy \\\n", + "0 Ni-1 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -604.262250 \n", + "1 Ni-2 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -603.933146 \n", + "2 Ni-3 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -603.735818 \n", + "3 Ni-4 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -604.967429 \n", + "4 Ni-5 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -602.935586 \n", + "... ... ... ... \n", + "1561 Li-266 {'lattice_mat': [[4.8442, 0.0, 0.0], [2.4221, ... -21.410502 \n", + "1562 Li-267 {'lattice_mat': [[3.429887, 0.0, 0.0], [-1.714... -17.848979 \n", + "1563 Li-268 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797287 \n", + "1564 Li-269 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797589 \n", + "1565 Li-270 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797713 \n", + "\n", + " forces \\\n", + "0 [[-0.1928178, -1.87931786, -0.66374007], [-0.0... \n", + "1 [[-0.71128299, -1.10528691, -2.20081632], [0.9... \n", + "2 [[-1.04325671, 0.32574515, 0.30692968], [-0.75... \n", + "3 [[-1.136544, 1.56868294, -1.09210495], [-1.274... \n", + "4 [[3.11483946, -0.55002862, 0.11103391], [1.460... \n", + "... ... \n", + "1561 [[-0.0, -0.0, -0.00808912], [-0.0, -0.0, -0.01... \n", + "1562 [[0.0, 0.00387929, 0.00568886], [0.0, 0.002831... \n", + "1563 [[-0.0, 0.0, 0.0], [0.0, -0.0, -0.0]] \n", + "1564 [[0.0, -0.0, -0.0], [-0.0, 0.0, 0.0]] \n", + "1565 [[0.0, 0.0, 0.0], [0.0, -0.0, -0.0]] \n", + "\n", + " stresses \n", + "0 [41.40636025, 41.1876322, 51.06529127, 1.04252... \n", + "1 [44.88288149, 44.70823804, 44.73856806, 1.6211... \n", + "2 [52.07802628, 45.27925996, 47.31874643, -4.461... \n", + "3 [38.03742565, 44.72767184, 39.73179484, -5.093... \n", + "4 [47.1482229, 51.19066271, 46.54196562, 4.22313... \n", + "... ... \n", + "1561 [3.17420482, 3.62499364, 2.54337296, -0.926102... \n", + "1562 [2.38724801, 3.61190439, 2.1257323, 0.83494626... \n", + "1563 [3.65117155, 4.28869211, 3.70795776, -0.817898... \n", + "1564 [2.79230607, 2.46389956, 3.81491553, 1.9486931... \n", + "1565 [2.74849324, 3.62957339, 3.05891903, -2.404687... \n", + "\n", + "[1566 rows x 5 columns]\n", + "/content/jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/mlearn_Si_energy.json.zip\n", + "{'Si-215': -297.62773938, 'Si-216': -295.77170067, 'Si-217': -291.28958206, 'Si-218': -296.24088456, 'Si-219': -294.41361742, 'Si-220': -334.75283939, 'Si-221': -334.69215136, 'Si-222': -184.71808052, 'Si-223': -121.41180043, 'Si-224': -338.93899696, 'Si-225': -338.83557056, 'Si-226': -335.68901422, 'Si-227': -333.7064957, 'Si-228': -344.85564046, 'Si-229': -344.81108268, 'Si-230': -298.83222646, 'Si-231': -298.96501782, 'Si-232': -295.20943762, 'Si-233': -291.86293882, 'Si-234': -344.74080048, 'Si-235': -344.74080047, 'Si-236': -344.74080046, 'Si-237': -341.22165747, 'Si-238': -341.22165734, 'Si-239': -341.22165747}\n", + "Si-215 -297.62773938 -297.5837001800537 63\n", + "Si-216 -295.77170067 -295.0032305717468 63\n", + "Si-217 -291.28958206 -293.4000463485718 63\n", + "Si-218 -296.24088456 -294.19183015823364 63\n", + "Si-219 -294.41361742 -294.7480344772339 63\n", + "Si-220 -334.75283939 -333.7381525039673 63\n", + "Si-221 -334.69215136 -334.02672386169434 63\n", + "Si-222 -184.71808052 -185.19737434387207 36\n", + "Si-223 -121.41180043 -121.2330322265625 24\n", + "Si-224 -338.93899696 -337.5583190917969 64\n", + "Si-225 -338.83557056 -337.2057800292969 64\n", + "Si-226 -335.68901422 -333.75701904296875 64\n", + "Si-227 -333.7064957 -331.44317626953125 64\n", + "Si-228 -344.85564046 -342.9688720703125 64\n", + "Si-229 -344.81108268 -342.98297119140625 64\n", + "Si-230 -298.83222646 -299.63824462890625 64\n", + "Si-231 -298.96501782 -299.7249450683594 64\n", + "Si-232 -295.20943762 -296.39654541015625 64\n", + "Si-233 -291.86293882 -294.197021484375 64\n", + "Si-234 -344.74080048 -343.7108459472656 64\n", + "Si-235 -344.74080047 -343.7108459472656 64\n", + "Si-236 -344.74080046 -343.7108154296875 64\n", + "Si-237 -341.22165747 -340.8216552734375 64\n", + "Si-238 -341.22165734 -340.8216552734375 64\n", + "Si-239 -341.22165747 -340.8216247558594 64\n", + "train_folder_ff.py --root_dir alff_Cu --config alff_Cu/config_Cu.json --output_dir alff_Cu\n", + " jid atoms energy \\\n", + "0 Ni-1 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -604.262250 \n", + "1 Ni-2 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -603.933146 \n", + "2 Ni-3 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -603.735818 \n", + "3 Ni-4 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -604.967429 \n", + "4 Ni-5 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -602.935586 \n", + "... ... ... ... \n", + "1561 Li-266 {'lattice_mat': [[4.8442, 0.0, 0.0], [2.4221, ... -21.410502 \n", + "1562 Li-267 {'lattice_mat': [[3.429887, 0.0, 0.0], [-1.714... -17.848979 \n", + "1563 Li-268 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797287 \n", + "1564 Li-269 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797589 \n", + "1565 Li-270 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797713 \n", + "\n", + " forces \\\n", + "0 [[-0.1928178, -1.87931786, -0.66374007], [-0.0... \n", + "1 [[-0.71128299, -1.10528691, -2.20081632], [0.9... \n", + "2 [[-1.04325671, 0.32574515, 0.30692968], [-0.75... \n", + "3 [[-1.136544, 1.56868294, -1.09210495], [-1.274... \n", + "4 [[3.11483946, -0.55002862, 0.11103391], [1.460... \n", + "... ... \n", + "1561 [[-0.0, -0.0, -0.00808912], [-0.0, -0.0, -0.01... \n", + "1562 [[0.0, 0.00387929, 0.00568886], [0.0, 0.002831... \n", + "1563 [[-0.0, 0.0, 0.0], [0.0, -0.0, -0.0]] \n", + "1564 [[0.0, -0.0, -0.0], [-0.0, 0.0, 0.0]] \n", + "1565 [[0.0, 0.0, 0.0], [0.0, -0.0, -0.0]] \n", + "\n", + " stresses \n", + "0 [41.40636025, 41.1876322, 51.06529127, 1.04252... \n", + "1 [44.88288149, 44.70823804, 44.73856806, 1.6211... \n", + "2 [52.07802628, 45.27925996, 47.31874643, -4.461... \n", + "3 [38.03742565, 44.72767184, 39.73179484, -5.093... \n", + "4 [47.1482229, 51.19066271, 46.54196562, 4.22313... \n", + "... ... \n", + "1561 [3.17420482, 3.62499364, 2.54337296, -0.926102... \n", + "1562 [2.38724801, 3.61190439, 2.1257323, 0.83494626... \n", + "1563 [3.65117155, 4.28869211, 3.70795776, -0.817898... \n", + "1564 [2.79230607, 2.46389956, 3.81491553, 1.9486931... \n", + "1565 [2.74849324, 3.62957339, 3.05891903, -2.404687... \n", + "\n", + "[1566 rows x 5 columns]\n", + "/content/jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/mlearn_Cu_energy.json.zip\n", + "{'Cu-263': -426.34693886, 'Cu-264': -426.45385646, 'Cu-265': -425.01326987, 'Cu-266': -426.97520565, 'Cu-267': -79.00234871, 'Cu-268': -118.16599421, 'Cu-269': -438.26781043999995, 'Cu-270': -385.13787522, 'Cu-271': -386.18300465, 'Cu-272': -388.35838097, 'Cu-273': -382.75924663, 'Cu-274': -437.61277748, 'Cu-275': -438.12589017999994, 'Cu-276': -428.12859755, 'Cu-277': -427.94279199, 'Cu-278': -384.02788406, 'Cu-279': -386.5230829, 'Cu-280': -425.23869566, 'Cu-281': -441.06709672, 'Cu-282': -432.02783401, 'Cu-283': -440.83133328, 'Cu-284': -442.58333391, 'Cu-285': -442.90668030999996, 'Cu-286': -440.67418483, 'Cu-287': -442.13760915, 'Cu-288': -438.08648284000003, 'Cu-289': -442.69816755, 'Cu-290': -442.24332075999996, 'Cu-291': -439.86968034999995, 'Cu-292': -441.52548536999996, 'Cu-293': -439.26306012999993}\n", + "Cu-263 -426.34693886 -479.3446445465088 107\n", + "Cu-264 -426.45385646 -479.2659692764282 107\n", + "Cu-265 -425.01326987 -476.3047790527344 107\n", + "Cu-266 -426.97520565 -480.5781421661377 107\n", + "Cu-267 -79.00234871 -92.25543975830078 20\n", + "Cu-268 -118.16599421 -138.3808708190918 30\n", + "Cu-269 -438.26781043999995 -496.9332160949707 108\n", + "Cu-270 -385.13787522 -450.40187644958496 108\n", + "Cu-271 -386.18300465 -449.5513286590576 108\n", + "Cu-272 -388.35838097 -449.3940010070801 108\n", + "Cu-273 -382.75924663 -449.12281036376953 108\n", + "Cu-274 -437.61277748 -495.86627197265625 108\n", + "Cu-275 -438.12589017999994 -497.02570724487305 108\n", + "Cu-276 -428.12859755 -478.41927909851074 108\n", + "Cu-277 -427.94279199 -477.94358825683594 108\n", + "Cu-278 -384.02788406 -449.49957275390625 108\n", + "Cu-279 -386.5230829 -450.4080047607422 108\n", + "Cu-280 -425.23869566 -474.8027515411377 108\n", + "Cu-281 -441.06709672 -501.6529426574707 108\n", + "Cu-282 -432.02783401 -502.14794540405273 108\n", + "Cu-283 -440.83133328 -503.11838150024414 108\n", + "Cu-284 -442.58333391 -502.99159240722656 108\n", + "Cu-285 -442.90668030999996 -502.5403633117676 108\n", + "Cu-286 -440.67418483 -500.6858024597168 108\n", + "Cu-287 -442.13760915 -503.0722904205322 108\n", + "Cu-288 -438.08648284000003 -497.8186798095703 108\n", + "Cu-289 -442.69816755 -502.7782344818115 108\n", + "Cu-290 -442.24332075999996 -502.4828395843506 108\n", + "Cu-291 -439.86968034999995 -503.07986068725586 108\n", + "Cu-292 -441.52548536999996 -501.98304748535156 108\n", + "Cu-293 -439.26306012999993 -500.2321014404297 108\n", + "CPU times: user 1min 7s, sys: 21.6 s, total: 1min 29s\n", + "Wall time: 1h 39min 45s\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "kzqntqx1reGB" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Plot energy & force scatter plot" + ], + "metadata": { + "id": "RK5MfRNrcvMn" + } + }, + { + "cell_type": "code", + "source": [ + "print(dir_name)" + ], + "metadata": { + "id": "7GptVaXrOZoB", + "outputId": "37fe8dce-07c8-4cea-9630-d0cc364c198a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "alff_Cu\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "HhMpYddv148y" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!pwd" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "d1KRguhm145-", + "outputId": "882fd41e-9e89-4e68-d392-c3c0f93a2db8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Silicon" + ], + "metadata": { + "id": "R4As7DqgQWO4" + } + }, + { + "cell_type": "code", + "source": [ + "from jarvis.db.jsonutils import loadjson\n", + "from matplotlib.gridspec import GridSpec\n", + "import numpy as np\n", + "from sklearn.metrics import mean_absolute_error\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "out_dir = 'alff_Si'\n", + "%matplotlib inline\n", + "# Plot training hostory for validation set\n", + "json_path = os.path.join(out_dir, \"history_val.json\")\n", + "v = loadjson(json_path)\n", + "ens = []\n", + "fs = []\n", + "for i in v:\n", + " ens.append(i[0])\n", + " fs.append(i[2])\n", + "the_grid = GridSpec(1, 2)\n", + "plt.rcParams.update({\"font.size\": 18})\n", + "plt.figure(figsize=(12, 5))\n", + "plt.subplot(the_grid[0])\n", + "plt.title(\"(a) Energy\")\n", + "plt.plot(ens)\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"eV\")\n", + "plt.subplot(the_grid[1])\n", + "plt.title(\"(b) Forces\")\n", + "plt.plot(fs)\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"eV/A\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "# plt.savefig(\"history.png\")\n", + "# plt.close()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 436 + }, + "id": "CytZnalMv6S3", + "outputId": "31c39021-9fa7-4a65-8db8-68f92df9d10e" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "the_grid = GridSpec(1, 2)\n", + "json_path = os.path.join(out_dir, \"Val_results.json\")\n", + "test = loadjson(json_path)\n", + "plt.rcParams.update({\"font.size\": 18})\n", + "plt.figure(figsize=(12, 5))\n", + "plt.subplot(the_grid[0])\n", + "xx = []\n", + "yy = []\n", + "factor = 1\n", + "for i in test:\n", + " for j, k in zip(i[\"target_out\"], i[\"pred_out\"]):\n", + " xx.append(j)\n", + " yy.append(k)\n", + "xx = np.array(xx) * factor\n", + "yy = np.array(yy) * factor\n", + "\n", + "x_bar = np.mean(xx)\n", + "baseline_mae = mean_absolute_error(\n", + " np.array(xx),\n", + " np.array([x_bar for i in range(len(xx))]),\n", + ")\n", + "print(\"Val\")\n", + "print(\"Baseline MAE: eV\", baseline_mae)\n", + "print(\"MAE eV\", mean_absolute_error(xx, yy))\n", + "\n", + "plt.plot(xx, yy, \".\")\n", + "plt.ylabel(\"ALIGNN Energy (eV)\")\n", + "plt.xlabel(\"DFT Energy (eV)\")\n", + "plt.subplot(the_grid[1])\n", + "xx = []\n", + "yy = []\n", + "for i in test:\n", + " for j, k in zip(i[\"target_grad\"], i[\"pred_grad\"]):\n", + " for m, n in zip(j, k):\n", + " xx.append(m)\n", + " yy.append(n)\n", + "xx = np.array(xx) * factor\n", + "yy = np.array(yy) * factor\n", + "\n", + "x_bar = np.mean(xx)\n", + "baseline_mae = mean_absolute_error(\n", + " np.array(xx),\n", + " np.array([x_bar for i in range(len(xx))]),\n", + ")\n", + "print(\"Test\")\n", + "print(\"Baseline MAE: eV/A\", baseline_mae)\n", + "print(\"MAE eV/A\", mean_absolute_error(xx, yy))\n", + "plt.scatter(xx, yy, c=\"blueviolet\", s=10, alpha=0.5)\n", + "\n", + "plt.scatter(xx, yy, c=\"blueviolet\", s=10, alpha=0.5)\n", + "plt.ylabel(\"ALIGNN Force (eV/A)\")\n", + "plt.xlabel(\"DFT Force (eV/A)\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "# plt.savefig(\"val.png\")\n", + "# plt.close()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 543 + }, + "id": "BnvgA2euv6QN", + "outputId": "a624f36f-46c3-43d2-a7ac-24d7e07414fb" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Val\n", + "Baseline MAE: eV 0.29931997259457904\n", + "MAE eV 0.018439888954162598\n", + "Test\n", + "Baseline MAE: eV/A 0.574290006651727\n", + "MAE eV/A 0.10566627225786171\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "hHEZ2LEJPS50" + }, + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "wUnnHpJcPS0s" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "SCf2nmy-PSyL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "jMmF8lqJPSvm" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "6RTgAlDmO_vt" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Copper" + ], + "metadata": { + "id": "qGKqUXeGQTkt" + } + }, + { + "cell_type": "code", + "source": [ + "from jarvis.db.jsonutils import loadjson\n", + "from matplotlib.gridspec import GridSpec\n", + "import numpy as np\n", + "from sklearn.metrics import mean_absolute_error\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "out_dir = 'alff_Cu'\n", + "%matplotlib inline\n", + "# Plot training hostory for validation set\n", + "json_path = os.path.join(out_dir, \"history_val.json\")\n", + "v = loadjson(json_path)\n", + "ens = []\n", + "fs = []\n", + "for i in v:\n", + " ens.append(i[0])\n", + " fs.append(i[2])\n", + "the_grid = GridSpec(1, 2)\n", + "plt.rcParams.update({\"font.size\": 18})\n", + "plt.figure(figsize=(12, 5))\n", + "plt.subplot(the_grid[0])\n", + "plt.title(\"(a) Energy\")\n", + "plt.plot(ens)\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"eV\")\n", + "plt.subplot(the_grid[1])\n", + "plt.title(\"(b) Forces\")\n", + "plt.plot(fs)\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"eV/A\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "# plt.savefig(\"history.png\")\n", + "# plt.close()\n" + ], + "metadata": { + "id": "ojMQaKg4v6Nc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 436 + }, + "outputId": "bd23a7d7-090d-4c20-e169-51e03173d253" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "the_grid = GridSpec(1, 2)\n", + "json_path = os.path.join(out_dir, \"Val_results.json\")\n", + "test = loadjson(json_path)\n", + "plt.rcParams.update({\"font.size\": 18})\n", + "plt.figure(figsize=(12, 5))\n", + "plt.subplot(the_grid[0])\n", + "xx = []\n", + "yy = []\n", + "factor = 1\n", + "for i in test:\n", + " for j, k in zip(i[\"target_out\"], i[\"pred_out\"]):\n", + " xx.append(j)\n", + " yy.append(k)\n", + "xx = np.array(xx) * factor\n", + "yy = np.array(yy) * factor\n", + "\n", + "x_bar = np.mean(xx)\n", + "baseline_mae = mean_absolute_error(\n", + " np.array(xx),\n", + " np.array([x_bar for i in range(len(xx))]),\n", + ")\n", + "print(\"Val\")\n", + "print(\"Baseline MAE: eV\", baseline_mae)\n", + "print(\"MAE eV\", mean_absolute_error(xx, yy))\n", + "\n", + "plt.plot(xx, yy, \".\")\n", + "plt.ylabel(\"ALIGNN Energy (eV)\")\n", + "plt.xlabel(\"DFT Energy (eV)\")\n", + "plt.subplot(the_grid[1])\n", + "xx = []\n", + "yy = []\n", + "for i in test:\n", + " for j, k in zip(i[\"target_grad\"], i[\"pred_grad\"]):\n", + " for m, n in zip(j, k):\n", + " xx.append(m)\n", + " yy.append(n)\n", + "xx = np.array(xx) * factor\n", + "yy = np.array(yy) * factor\n", + "\n", + "x_bar = np.mean(xx)\n", + "baseline_mae = mean_absolute_error(\n", + " np.array(xx),\n", + " np.array([x_bar for i in range(len(xx))]),\n", + ")\n", + "print(\"Test\")\n", + "print(\"Baseline MAE: eV/A\", baseline_mae)\n", + "print(\"MAE eV/A\", mean_absolute_error(xx, yy))\n", + "plt.scatter(xx, yy, c=\"blueviolet\", s=10, alpha=0.5)\n", + "\n", + "plt.scatter(xx, yy, c=\"blueviolet\", s=10, alpha=0.5)\n", + "plt.ylabel(\"ALIGNN Force (eV/A)\")\n", + "plt.xlabel(\"DFT Force (eV/A)\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "# plt.savefig(\"val.png\")\n", + "# plt.close()\n" + ], + "metadata": { + "id": "WEd5fgdIPF-3", + "outputId": "569bf560-1fad-4dc3-de3c-c83fe7bbbe36", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 543 + } + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Val\n", + "Baseline MAE: eV 0.14882186783684634\n", + "MAE eV 0.5573384126027425\n", + "Test\n", + "Baseline MAE: eV/A 0.467293795777469\n", + "MAE eV/A 0.3451500446265101\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "from alignn.ff.ff import AlignnAtomwiseCalculator,default_path,wt10_path,alignnff_fmult,fd_path,ForceField\n", + "from ase import Atom, Atoms\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "model_path = 'alff_Cu' #default_path()\n", + "calc = AlignnAtomwiseCalculator(path=model_path)\n", + "\n", + "\n", + "lattice_params = np.linspace(3.1, 3.8)\n", + "fcc_energies = []\n", + "ready = True\n", + "for a in lattice_params:\n", + " atoms = Atoms([Atom('Cu', (0, 0, 0))],\n", + " cell=0.5 * a * np.array([[1.0, 1.0, 0.0],\n", + " [0.0, 1.0, 1.0],\n", + " [1.0, 0.0, 1.0]]),\n", + " pbc=True)\n", + "\n", + " atoms.set_tags(np.ones(len(atoms)))\n", + "\n", + " atoms.calc = calc\n", + "\n", + " e = atoms.get_potential_energy()\n", + " fcc_energies.append(e)\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "plt.plot(lattice_params, fcc_energies)\n", + "plt.title('1x1x1')\n", + "plt.xlabel('Lattice constant ($\\AA$)')\n", + "plt.ylabel('Total energy (eV)')\n", + "plt.show()" + ], + "metadata": { + "id": "XYYKSJMvQYvs", + "outputId": "a2a9e811-a68a-468b-be86-241aa4947cb9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 } }, - "execution_count": 1, + "execution_count": 16, "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content\n" - ] + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} } ] }, { "cell_type": "code", "source": [ - "\n", - "import os\n", - "os.chdir('/content')\n", - "if not os.path.exists('jarvis_leaderboard'):\n", - " !git clone https://github.com/usnistgov/jarvis_leaderboard.git\n", - "os.chdir('jarvis_leaderboard')\n", - "!pip install -e ." + "!pip install -q phonopy" ], "metadata": { - "id": "_k6ga4THb2B9", - "outputId": "64f8d488-8ab6-4892-9e8a-fa6d38af5135", + "id": "U5e5RePpQqGF", + "outputId": "18f72eed-b1a5-4001-a760-114b78357f04", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": 2, + "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Cloning into 'jarvis_leaderboard'...\n", - "remote: Enumerating objects: 43198, done.\u001b[K\n", - "remote: Counting objects: 100% (11998/11998), done.\u001b[K\n", - "remote: Compressing objects: 100% (1292/1292), done.\u001b[K\n", - "remote: Total 43198 (delta 6358), reused 11802 (delta 6317), pack-reused 31200\u001b[K\n", - "Receiving objects: 100% (43198/43198), 313.86 MiB | 22.19 MiB/s, done.\n", - "Resolving deltas: 100% (22526/22526), done.\n", - "Updating files: 100% (2568/2568), done.\n", - "Obtaining file:///content/jarvis_leaderboard\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from jarvis-leaderboard==2023.7.10) (1.22.4)\n", - "Requirement already satisfied: scipy>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from jarvis-leaderboard==2023.7.10) (1.10.1)\n", - "Collecting jarvis-tools>=2021.07.19 (from jarvis-leaderboard==2023.7.10)\n", - " Downloading jarvis_tools-2023.5.26-py2.py3-none-any.whl (974 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m974.6/974.6 kB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: scikit-learn>=0.24.1 in 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jarvis-leaderboard-2023.7.10 jarvis-tools-2023.5.26 markdown-3.2.1 mergedeep-1.3.4 mkdocs-1.4.3 mkdocs-material-9.1.19 mkdocs-material-extensions-1.1.1 multiprocess-0.70.14 pymdown-extensions-10.1 pyyaml-env-tag-0.1 responses-0.18.0 rouge-score-0.1.2 spglib-2.0.2 watchdog-3.0.0 xmltodict-0.13.0 xxhash-3.2.0\n" + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for phonopy (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" ] } ] }, - { - "cell_type": "markdown", - "source": [ - "Here are some of the exisiting contributions for Cu forces. We are going to add another contribution after a quick alignn_ff training run and comapre with existing contributions." - ], - "metadata": { - "id": "Oa6rICgdnSEu" - } - }, { "cell_type": "code", "source": [ - "from jarvis_leaderboard.rebuild import get_metric_value,get_results\n", + "from alignn.ff.ff import phonons\n", + "from jarvis.core.atoms import ase_to_atoms\n", + "ph_path=model_path\n", + "ph=phonons(model_path=ph_path,atoms=ase_to_atoms(atoms))\n", "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "names,vals=get_results(bench_name='AI-MLFF-forces-mlearn_Cu-test-multimae.csv.zip')\n", - "plt.bar(np.arange(len(vals)),vals,color=(0.2, 0.4, 0.6, 0.6),edgecolor='blue')\n", - "plt.xticks(np.arange(len(vals)),names,rotation=90)\n", - "plt.ylabel('MAE (eV/A)')" + "plt.axis('off')\n", + "plt.imshow(plt.imread(\"phonopy_bands.png\"))\n", + "plt.show()" ], "metadata": { - "id": "64aGqWzvb6yQ", - "outputId": "1b5b5a70-48a9-4178-ad49-fef58d8582ba", + "id": "JiEA_OU3QYq6", + "outputId": "d582354e-d591-4701-9103-fe0a28fb3cfe", "colab": { "base_uri": "https://localhost:8080/", - "height": 621 + "height": 285 } }, - "execution_count": 3, + "execution_count": 20, "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Text(0, 0.5, 'MAE (eV/A)')" - ] - }, - "metadata": {}, - "execution_count": 3 - }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" + "image/png": 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\n" }, "metadata": {} } ] }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "BBzZ3QtsQYoY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "tpuLTOT7PQoW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# stop" + ], + "metadata": { + "id": "XpG-QyCOv6Ko" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Here are some of the exisiting contributions for Cu forces. We are going to add another contribution after a quick alignn_ff training run and comapre with existing contributions." + ], + "metadata": { + "id": "Oa6rICgdnSEu" + } + }, + { + "cell_type": "code", + "source": [ + "# from jarvis_leaderboard.rebuild import get_metric_value,get_results\n", + "# %matplotlib inline\n", + "# import numpy as np\n", + "# import matplotlib.pyplot as plt\n", + "# names,vals=get_results(bench_name='AI-MLFF-forces-mlearn_Si-test-multimae.csv.zip')\n", + "# plt.bar(np.arange(len(vals)),vals,color=(0.2, 0.4, 0.6, 0.6),edgecolor='blue')\n", + "# plt.xticks(np.arange(len(vals)),names,rotation=90)\n", + "# plt.ylabel('MAE (eV/A)')" + ], + "metadata": { + "id": "64aGqWzvb6yQ" + }, + "execution_count": null, + "outputs": [] + }, { "cell_type": "code", "source": [ @@ -290,7 +1418,7 @@ "id": "IHoA-IATdzR_", "outputId": "b9c6019a-7976-4713-a65d-a3c32ef7bbfc" }, - "execution_count": 4, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -305,66 +1433,6 @@ } ] }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "m-7Dw7PbnND4", - "outputId": "7e146520-344f-449b-fa52-058d76fd216c" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 MB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.5/63.5 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.2/91.2 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.2/45.2 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.4/52.4 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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"\u001b[?25h Building wheel for phonopy (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "confection 0.1.0 requires pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4, but you have pydantic 1.8.1 which is incompatible.\n", - "dglgo 0.0.2 requires pydantic>=1.9.0, but you have pydantic 1.8.1 which is incompatible.\n", - "inflect 6.0.5 requires pydantic<2,>=1.9.1, but you have pydantic 1.8.1 which is incompatible.\n", - "spacy 3.5.4 requires pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4, but you have pydantic 1.8.1 which is incompatible.\n", - "thinc 8.1.10 requires pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4, but you have pydantic 1.8.1 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mCPU times: user 571 ms, sys: 72.6 ms, total: 644 ms\n", - "Wall time: 1min 29s\n" - ] - } - ], - "source": [ - "%%time\n", - "!pip install -q dgl -f https://data.dgl.ai/wheels/cu118/repo.html\n", - "!pip install -q dglgo -f https://data.dgl.ai/wheels-test/repo.html\n", - "!pip install -q alignn phonopy" - ] - }, { "cell_type": "markdown", "source": [ @@ -406,7 +1474,7 @@ "id": "2kJ-FaM4YxFH", "outputId": "b88b5932-d398-4f0c-fb34-7b08f8b6de09" }, - "execution_count": 6, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -478,7 +1546,7 @@ "id": "H99l8k5PZIdX", "outputId": "1e3649ea-9d66-4937-bc58-81a1d1089e93" }, - "execution_count": 7, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -507,7 +1575,7 @@ "id": "Yc1F5QA3ZeWq", "outputId": "125d0d73-5964-4cd7-ac25-627aab53ad5c" }, - "execution_count": 8, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -544,7 +1612,7 @@ "id": "DXS-tNyMarlw", "outputId": "54b7b6c1-9e1a-4a15-99b9-c352b97f2344" }, - "execution_count": 9, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -585,7 +1653,7 @@ "id": "zFJoL7M0bERn", "outputId": "b7038316-a348-49fa-8f53-14c11c835b11" }, - "execution_count": 10, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -605,7 +1673,7 @@ "metadata": { "id": "6rqTfjR6baV7" }, - "execution_count": 10, + "execution_count": null, "outputs": [] }, { @@ -626,7 +1694,7 @@ "id": "hFZn-PrinTX0", "outputId": "1e08fcbb-a317-4cd6-c3eb-84f1ce016f20" }, - "execution_count": 11, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -693,7 +1761,7 @@ "id": "R-y2oncab7-l", "outputId": "51a49cb4-7964-4f19-c4e4-2ca79709484c" }, - "execution_count": 12, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -949,7 +2017,7 @@ "id": "vxahSs2Tc1kB", "outputId": "e2b89513-92f6-4b81-b4e0-d9fca576a201" }, - "execution_count": 13, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -994,7 +2062,7 @@ "id": "ylnu0xCjoWjR", "outputId": "29c17cff-1beb-4fb2-c8e8-990405672c26" }, - "execution_count": 14, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1027,7 +2095,7 @@ "id": "YWcGU66vn4J2", "outputId": "4bd9e360-a923-4d63-d131-240a9f380cf3" }, - "execution_count": 15, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1052,7 +2120,7 @@ "metadata": { "id": "1yQ5rv5rsvdC" }, - "execution_count": 16, + "execution_count": null, "outputs": [] }, { @@ -1078,7 +2146,7 @@ "id": "o8tG1cAusthS", "outputId": "7b732198-47e6-4d4a-9825-394ca5461ff5" }, - "execution_count": 17, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1331,6 +2399,60 @@ "id": "jFEnqwk3ijcr" } }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "9BaHGXkzyyo8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "QuQ20q5-y36y" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "NOWF55ley34t" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "MiwMvAWcy32L" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "FynEcSoey3zm" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "A1daSd81y3w7" + }, + "execution_count": null, + "outputs": [] + }, { "cell_type": "code", "source": [ @@ -1345,7 +2467,7 @@ "metadata": { "id": "9uOOGKAdl_9m" }, - "execution_count": 18, + "execution_count": null, "outputs": [] }, { @@ -1386,7 +2508,7 @@ "id": "yxKgQvw8mHn1", "outputId": "86f7f1b1-a4c2-4f07-e9ad-47435ddf918a" }, - "execution_count": 19, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -1468,7 +2590,7 @@ "id": "XRPWGF0RmHlo", "outputId": "687092c8-ab2a-4cc7-84c1-18d6700fe252" }, - "execution_count": 20, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1563,7 +2685,7 @@ "id": "XGPaqn1cmHjG", "outputId": "8a68f2a5-c36a-49bb-9c12-ef1a25e6f895" }, - "execution_count": 21, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1600,7 +2722,7 @@ "id": "eb65G9gRnus6", "outputId": "5f9f2644-d8dc-4fc5-c581-1f5cd20247ee" }, - "execution_count": 22, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1665,7 +2787,7 @@ "id": "ediLCTlyjxjV", "outputId": "915f0406-0f16-4501-ba32-68ab29dd8293" }, - "execution_count": 23, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -1696,7 +2818,7 @@ "id": "8Fev-kbMjxgv", "outputId": "1ce9be25-1490-4cb9-89a9-3e204d5f7532" }, - "execution_count": 24, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -1722,7 +2844,7 @@ "base_uri": "https://localhost:8080/" } }, - "execution_count": 25, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1756,7 +2878,7 @@ "metadata": { "id": "s1rn9Unhmydg" }, - "execution_count": 26, + "execution_count": null, "outputs": [] }, { @@ -1771,7 +2893,7 @@ "id": "74c9fm3Crgv0", "outputId": "a68d04d7-0991-479a-c419-d4dea54f5197" }, - "execution_count": 27, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1938,7 +3060,7 @@ "id": "_cH9ME5HmybY", "outputId": "3365a211-5f16-4af7-bad7-9551efaf6c85" }, - "execution_count": 28, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -2033,7 +3155,7 @@ "id": "Q4WhUkLxsNDZ", "outputId": "e67660d1-4eeb-46b0-99aa-eb479f452988" }, - "execution_count": 29, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -2066,7 +3188,7 @@ "id": "NR6FotY5sNAf", "outputId": "7ab3a709-8edc-412f-da54-1098feb33c55" }, - "execution_count": 30, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -2089,7 +3211,7 @@ "base_uri": "https://localhost:8080/" } }, - "execution_count": 31, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -2118,7 +3240,7 @@ "metadata": { "id": "4GQo10LHl436" }, - "execution_count": 32, + "execution_count": null, "outputs": [] }, { @@ -2150,7 +3272,7 @@ "height": 771 } }, - "execution_count": 33, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -2184,7 +3306,7 @@ "metadata": { "id": "a3w4aYKelaum" }, - "execution_count": 34, + "execution_count": null, "outputs": [] }, { @@ -2215,7 +3337,7 @@ "height": 504 } }, - "execution_count": 35, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -2275,7 +3397,7 @@ "height": 504 } }, - "execution_count": 36, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -2326,7 +3448,7 @@ "height": 747 } }, - "execution_count": 37, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -2377,7 +3499,7 @@ "metadata": { "id": "FOprnsvCmVDp" }, - "execution_count": 37, + "execution_count": null, "outputs": [] }, { @@ -2386,7 +3508,7 @@ "metadata": { "id": "Cx5MVzHOmVBQ" }, - "execution_count": 37, + "execution_count": null, "outputs": [] }, { @@ -2399,7 +3521,7 @@ "metadata": { "id": "inHoFBEKmU-v" }, - "execution_count": 37, + "execution_count": null, "outputs": [] }, { @@ -2429,7 +3551,7 @@ }, "outputId": "1d55a5e5-c9a1-401a-a7f4-25d5ab488ea6" }, - "execution_count": 39, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -2486,7 +3608,7 @@ "metadata": { "id": "YL1_EAc7tXtB" }, - "execution_count": 41, + "execution_count": null, "outputs": [] }, { @@ -2498,7 +3620,7 @@ "metadata": { "id": "1QDwNl-5tXrE" }, - "execution_count": 42, + "execution_count": null, "outputs": [] }, { @@ -2520,7 +3642,7 @@ "id": "-6tTFmU3WDzb", "outputId": "cb2612d6-bd89-4e41-a750-c968d68dd7e5" }, - "execution_count": 43, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -2546,7 +3668,7 @@ "id": "7aPp-8e4pQ5E", "outputId": "919efa03-f56e-4c42-caee-a47a0b93af69" }, - "execution_count": 44, + "execution_count": null, "outputs": [ { "output_type": "stream", diff --git a/jarvis-tools-notebooks/Train_MLFF_ALLEGRO.ipynb b/jarvis-tools-notebooks/Train_MLFF_ALLEGRO.ipynb index e280fc1..4f3d1c7 100644 --- a/jarvis-tools-notebooks/Train_MLFF_ALLEGRO.ipynb +++ b/jarvis-tools-notebooks/Train_MLFF_ALLEGRO.ipynb @@ -5,7 +5,7 @@ "colab": { "provenance": [], "gpuType": "T4", - "authorship_tag": "ABX9TyMvSeVNPqXiJvMt455q7GQR", + "authorship_tag": "ABX9TyONwlc5bAwvsgmeS3YYaKie", "include_colab_link": true }, "kernelspec": { @@ -32,10 +32,10 @@ "cell_type": "code", "source": [ "# install wandb\n", - "!pip install wandb\n", + "!pip install -q wandb\n", "\n", "# install nequip\n", - "!pip install nequip==0.5.5 torch==1.11\n", + "!pip install -q nequip==0.5.5 torch==1.11 jarvis-tools\n", "\n", "# fix colab imports\n", "import site\n", @@ -43,18 +43,14 @@ "\n", "# set to allow anonymous WandB\n", "import os\n", - "os.environ[\"WANDB_ANONYMOUS\"] = \"must\"\n", - "\n", - "# install allegro\n", - "!git clone --depth 1 https://github.com/mir-group/allegro.git\n", - "!pip install allegro/" + "os.environ[\"WANDB_ANONYMOUS\"] = \"must\"\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sJl9OzZZKWh_", - "outputId": "4e38cf36-6105-4aa5-af16-d20bdddbaff7" + "outputId": "68c66f62-f97d-4c2d-8822-ee123b67f4d3" }, "execution_count": 1, "outputs": [ @@ -62,68 +58,16 @@ "output_type": "stream", "name": "stdout", "text": [ - "Collecting wandb\n", - " Downloading wandb-0.15.5-py3-none-any.whl (2.1 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: Click!=8.0.0,>=7.1 in /usr/local/lib/python3.10/dist-packages (from wandb) (8.1.6)\n", - "Collecting GitPython!=3.1.29,>=1.0.0 (from wandb)\n", - " Downloading GitPython-3.1.32-py3-none-any.whl (188 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m188.5/188.5 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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"\u001b[1;36mhint\u001b[0m: See above for details.\n", + "\u001b[1;36mhint\u001b[0m: See above for details.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!wget https://figshare.com/ndownloader/files/40357663 -O mlearn.json.zip" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ezhXOfk0jynn", + "outputId": "e0b82d85-0dd4-4575-ca6e-74b9ab1a7054" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-01-07 03:35:08-- https://figshare.com/ndownloader/files/40357663\n", + "Resolving figshare.com (figshare.com)... 54.155.30.132, 34.241.157.25, 2a05:d018:1f4:d000:fd71:4bc5:e7e5:3f50, ...\n", + "Connecting to figshare.com (figshare.com)|54.155.30.132|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240107/eu-west-1/s3/aws4_request&X-Amz-Date=20240107T033508Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=3651d78a49d54d369e0d2888bf8784072d838df22fb579416b07b3e60dcacada [following]\n", + "--2024-01-07 03:35:08-- https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240107/eu-west-1/s3/aws4_request&X-Amz-Date=20240107T033508Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=3651d78a49d54d369e0d2888bf8784072d838df22fb579416b07b3e60dcacada\n", + "Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.30.107, 52.218.25.99, 52.218.112.43, ...\n", + "Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.30.107|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 2542319 (2.4M) [application/zip]\n", + "Saving to: ‘mlearn.json.zip’\n", + "\n", + "mlearn.json.zip 100%[===================>] 2.42M 3.33MB/s in 0.7s \n", + "\n", + "2024-01-07 03:35:09 (3.33 MB/s) - ‘mlearn.json.zip’ saved [2542319/2542319]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import json,zipfile\n", + "mlearn = json.loads(\n", + " zipfile.ZipFile(\"mlearn.json.zip\").read(\n", + " \"mlearn.json\"\n", + " )\n", + " )" + ], + "metadata": { + "id": "RbUdtA3Hj3H9" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# install allegro\n", + "!git clone --depth 1 https://github.com/mir-group/allegro.git\n", + "!pip install allegro/" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3FqDN7vTJ8ds", + "outputId": "07cfc568-b480-4f82-ee8f-91bb68a69f4b" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ "Cloning into 'allegro'...\n", "remote: Enumerating objects: 44, done.\u001b[K\n", "remote: Counting objects: 100% (44/44), done.\u001b[K\n", "remote: Compressing objects: 100% (43/43), done.\u001b[K\n", - "remote: Total 44 (delta 0), reused 28 (delta 0), pack-reused 0\u001b[K\n", - 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" Created wheel for mir-allegro: filename=mir_allegro-0.2.0-py3-none-any.whl size=27439 sha256=2df5622d5361cb19a3c9e3f3e56fe5467c32948a2f78b7ae519f9bd9c82015c2\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-qa7ura5s/wheels/b4/da/7a/12e336aa57ba27cca94b3d21b0f02fa0c6c86f7e1f2b7a4195\n", + " Created wheel for mir-allegro: filename=mir_allegro-0.2.0-py3-none-any.whl size=27432 sha256=3048ae1760812b82392ccb46ab74667c8524351d9813d5cafe6899e304634806\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-xti282gr/wheels/b4/da/7a/12e336aa57ba27cca94b3d21b0f02fa0c6c86f7e1f2b7a4195\n", "Successfully built mir-allegro\n", "Installing collected packages: torch-runstats, torch, torch-ema, opt-einsum-fx, e3nn, ase, nequip, mir-allegro\n", " Attempting uninstall: torch\n", - " Found existing installation: torch 2.0.1+cu118\n", - " Uninstalling torch-2.0.1+cu118:\n", - " Successfully uninstalled torch-2.0.1+cu118\n", + " Found existing installation: torch 2.1.0+cu121\n", + " Uninstalling torch-2.1.0+cu121:\n", + " Successfully uninstalled torch-2.1.0+cu121\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "torchaudio 2.0.2+cu118 requires torch==2.0.1, but you have torch 1.12.1 which is incompatible.\n", - "torchdata 0.6.1 requires torch==2.0.1, but you have torch 1.12.1 which is incompatible.\n", - "torchtext 0.15.2 requires torch==2.0.1, but you have torch 1.12.1 which is incompatible.\n", - "torchvision 0.15.2+cu118 requires torch==2.0.1, but you have torch 1.12.1 which is incompatible.\u001b[0m\u001b[31m\n", + "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 1.12.1 which is incompatible.\n", + "torchdata 0.7.0 requires torch==2.1.0, but you have torch 1.12.1 which is incompatible.\n", + "torchtext 0.16.0 requires torch==2.1.0, but you have torch 1.12.1 which is incompatible.\n", + "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 1.12.1 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed ase-3.22.1 e3nn-0.5.1 mir-allegro-0.2.0 nequip-0.5.6 opt-einsum-fx-0.1.4 torch-1.12.1 torch-ema-0.3 torch-runstats-0.2.0\n" ] } @@ -205,257 +227,515 @@ }, { "cell_type": "code", - "source": [], - "metadata": { - "id": "ruL2JdaB5hbS" - }, - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": 2, + "source": [ + "import os\n", + "if not os.path.exists('jarvis_leaderboard'):\n", + " !git clone https://github.com/usnistgov/jarvis_leaderboard.git\n", + "os.chdir('jarvis_leaderboard')\n", + "!pip install -e ." + ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "5r1_3LmhI8y3", - "outputId": "78bbe58a-68e5-4558-df40-e923f268c5c9" + "id": "GQJl67kKkJKB", + "outputId": "93785567-8f3b-424b-8d06-824adcc20ada" }, + "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "--2023-07-22 15:55:00-- https://github.com/materialsvirtuallab/mlearn/raw/master/data/Ni/test.json\n", - "Resolving github.com (github.com)... 140.82.114.4\n", - "Connecting to 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paginate, xmltodict, watchdog, typing-extensions, spglib, rouge, pyyaml-env-tag, pymdown-extensions, pathspec, mkdocs-material-extensions, mergedeep, colorama, annotated-types, pydantic-core, ghp-import, pydantic, mkdocs, mkdocs-material, jarvis-tools, jarvis-leaderboard\n", + " Attempting uninstall: typing-extensions\n", + " Found existing installation: typing_extensions 4.5.0\n", + " Uninstalling typing_extensions-4.5.0:\n", + " Successfully uninstalled typing_extensions-4.5.0\n", + " Attempting uninstall: pydantic\n", + " Found existing installation: pydantic 1.10.13\n", + " Uninstalling pydantic-1.10.13:\n", + " Successfully uninstalled pydantic-1.10.13\n", + " Running setup.py develop for jarvis-leaderboard\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires kaleido, which is not installed.\n", + "lida 0.0.10 requires python-multipart, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\n", + "llmx 0.0.15a0 requires cohere, which is not installed.\n", + "llmx 0.0.15a0 requires openai, which is not installed.\n", + "llmx 0.0.15a0 requires tiktoken, which is not installed.\n", + "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\n", + "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 1.12.1 which is incompatible.\n", + "torchdata 0.7.0 requires torch==2.1.0, but you have torch 1.12.1 which is incompatible.\n", + "torchtext 0.16.0 requires torch==2.1.0, but you have torch 1.12.1 which is incompatible.\n", + "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 1.12.1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed annotated-types-0.6.0 colorama-0.4.6 ghp-import-2.1.0 jarvis-leaderboard-2023.12.16 jarvis-tools-2023.12.12 mergedeep-1.3.4 mkdocs-1.5.3 mkdocs-material-9.5.3 mkdocs-material-extensions-1.3.1 paginate-0.5.6 pathspec-0.12.1 pydantic-2.5.3 pydantic-core-2.14.6 pymdown-extensions-10.7 pyyaml-env-tag-0.1 rouge-1.0.1 spglib-2.2.0 typing-extensions-4.9.0 watchdog-3.0.0 xmltodict-0.13.0\n" ] } - ], - "source": [ - "# get data from mlearn\n", - "#!rm *.json\n", - "!for m in Ni Cu Mo Ge Si Li; do wget https://github.com/materialsvirtuallab/mlearn/raw/master/data/${m}/test.json; mv test.json ${m}_test.json; done;\n", - "!ls" ] }, { "cell_type": "code", "source": [ - "# get data from mlearn\n", - "#!rm *.json\n", - "!for m in Ni Cu Mo Ge Si Li; do wget https://github.com/materialsvirtuallab/mlearn/raw/master/data/${m}/training.json; mv training.json ${m}_training.json; done;\n", - "!ls" + "%%time\n", + "import os,glob,sys,yaml\n", + "import zipfile\n", + "import json\n", + "import pandas as pd\n", + "from jarvis.db.figshare import data\n", + "from jarvis.core.atoms import Atoms\n", + "import numpy as np\n", + "from nequip.data import AtomicData, Collater, dataset_from_config, register_fields, AtomicDataDict\n", + "from nequip.data.transforms import TypeMapper\n", + "import fileinput\n", + "import torch\n", + "os.chdir('/content')\n", + "#torch.cuda.is_available = lambda : False\n", + "elements = [\"Si\"] #[\"Ni\", \"Si\", \"Ge\", \"Mo\", \"Cu\", \"Li\"]\n", + "\n", + "with open('allegro/configs/tutorial.yaml','r') as f:\n", + " txt=f.read()\n", + "\n", + "tut = yaml.load(txt, Loader=yaml.Loader)\n", + "\n", + "os.environ[\"WANDB_ANONYMOUS\"] = \"must\"\n", + "cmd = \"wandb offline\"\n", + "os.system(cmd)\n", + "#mlearn = data(\"mlearn\")\n", + "\n", + "\n", + "# def replaceAll(filename,searchExp,replaceExp):\n", + "# with open(filename, \"r\") as file:\n", + "# filedata = file.read().splitlines()\n", + "# content = []\n", + "# for j in filedata:\n", + "# if searchExp in j:\n", + "# content.append(replaceExp)\n", + "# else:\n", + "# content.append(j)\n", + "# with open(filename, \"w\") as file:\n", + "# file.write(\"\\n\".join(content))\n", + "for element in elements:\n", + " os.chdir('/content')\n", + " cmd = \"rm -r Si_data\"\n", + " os.system(cmd)\n", + " folder = \"Si_data\"\n", + " if not os.path.exists(folder):\n", + " os.makedirs(folder)\n", + " benchmark_energies = (\n", + " \"jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/mlearn_\"\n", + " + element\n", + " + \"_energy.json.zip\"\n", + " )\n", + " temp_energies = benchmark_energies.split(\"/\")[-1].split(\".zip\")[0]\n", + " energies = json.loads(\n", + " zipfile.ZipFile(benchmark_energies).read(temp_energies)\n", + " )\n", + " train_ids = list(energies[\"train\"].keys())\n", + " test_ids = list(energies[\"test\"].keys())\n", + "\n", + " f = open(\"Si_data/sitraj.xyz\", \"w\")\n", + " line = \"\"\n", + " for i in mlearn:\n", + " if i[\"jid\"] in train_ids:\n", + " # print(i)\n", + " atoms = Atoms.from_dict(i[\"atoms\"])\n", + " line += str(atoms.num_atoms) + \"\\n\"\n", + " line += (\n", + " \"Lattice=\"\n", + " + '\"'\n", + " + \" \".join(map(str, (atoms.lattice_mat).flatten()))\n", + " + '\"'\n", + " + \" Properties=species:S:1:pos:R:3:forces:R:3 energy=\"\n", + " + str(i[\"energy\"])\n", + " # + ' stress=\"'\n", + " # + \" \".join(map(str, np.array(i[\"stresses\"]).flatten()))\n", + " # + '\"'\n", + " + \" free_energy=\"\n", + " + str(i[\"energy\"])\n", + " + ' pbc=\"T T T\"'\n", + " + \"\\n\"\n", + " )\n", + " for m, n, p in zip(\n", + " atoms.elements, atoms.cart_coords, i[\"forces\"]\n", + " ):\n", + " line += (\n", + " str(m)\n", + " + \" \"\n", + " + \" \".join(map(str, n))\n", + " + \" \"\n", + " + \" \".join(map(str, p))\n", + " + \"\\n\"\n", + " )\n", + " # print(line)\n", + " f.write(line)\n", + " for i in mlearn:\n", + " if i[\"jid\"] in test_ids:\n", + " # print(i)\n", + " atoms = Atoms.from_dict(i[\"atoms\"])\n", + " line += str(atoms.num_atoms) + \"\\n\"\n", + " line += (\n", + " \"Lattice=\"\n", + " + '\"'\n", + " + \" \".join(map(str, (atoms.lattice_mat).flatten()))\n", + " + '\"'\n", + " + \" Properties=species:S:1:pos:R:3:forces:R:3 energy=\"\n", + " + str(i[\"energy\"])\n", + " # + ' stress=\"'\n", + " # + \" \".join(map(str, np.array(i[\"stresses\"]).flatten()))\n", + " # + '\"'\n", + " + \" free_energy=\"\n", + " + str(i[\"energy\"])\n", + " + ' pbc=\"T T T\"'\n", + " + \"\\n\"\n", + " )\n", + " for m, n, p in zip(\n", + " atoms.elements, atoms.cart_coords, i[\"forces\"]\n", + " ):\n", + " line += (\n", + " str(m)\n", + " + \" \"\n", + " + \" \".join(map(str, n))\n", + " + \" \"\n", + " + \" \".join(map(str, p))\n", + " + \"\\n\"\n", + " )\n", + " f.write(line)\n", + " # print(line)\n", + " for i in mlearn:\n", + " if i[\"jid\"] in test_ids:\n", + " # print(i)\n", + " atoms = Atoms.from_dict(i[\"atoms\"])\n", + " line += str(atoms.num_atoms) + \"\\n\"\n", + " line += (\n", + " \"Lattice=\"\n", + " + '\"'\n", + " + \" \".join(map(str, (atoms.lattice_mat).flatten()))\n", + " + '\"'\n", + " + \" Properties=species:S:1:pos:R:3:forces:R:3 energy=\"\n", + " + str(i[\"energy\"])\n", + " # + ' stress=\"'\n", + " # + \" \".join(map(str, np.array(i[\"stresses\"]).flatten()))\n", + " # + '\"'\n", + " + \" free_energy=\"\n", + " + str(i[\"energy\"])\n", + " + ' pbc=\"T T T\"'\n", + " + \"\\n\"\n", + " )\n", + " for m, n, p in zip(\n", + " atoms.elements, atoms.cart_coords, i[\"forces\"]\n", + " ):\n", + " line += (\n", + " str(m)\n", + " + \" \"\n", + " + \" \".join(map(str, n))\n", + " + \" \"\n", + " + \" \".join(map(str, p))\n", + " + \"\\n\"\n", + " )\n", + " # print(line)\n", + " f.write(line)\n", + " f.close()\n", + " cmd = \"rm -rf ./results\"\n", + " os.system(cmd)\n", + "\n", + " yaml_f = 'allegro/configs/tutorial_'+element+'.yaml'\n", + "\n", + " cmd = 'cp allegro/configs/tutorial.yaml allegro/configs/tutorial_'+element+'.yaml'\n", + " os.system(cmd)\n", + " tmp=\" \"+element+\": 0\"\n", + " #replaceAll(yaml_f,\"Si: 0\",tmp)\n", + " tut['chemical_symbol_to_type'] ={element: 0}\n", + " tut['n_train'] = len(train_ids)\n", + " tut['shuffle'] = False\n", + " tut['n_test'] = len(test_ids)\n", + " tut['n_val'] = len(test_ids)\n", + " tut['batch_size'] = 10\n", + " with open(yaml_f, \"w+\") as fp:\n", + " yaml.dump(tut,fp)\n", + " cmd = \"nequip-train allegro/configs/tutorial_\"+element+\".yaml --equivariance-test\"\n", + " os.system(cmd)\n", + " print('FINISHED')\n", + " import torch\n", + " from nequip.utils import Config\n", + " from nequip.model import model_from_config\n", + " from nequip.data import AtomicData, ASEDataset\n", + "\n", + " device = \"cpu\"\n", + " if torch.cuda.is_available():\n", + " device = torch.device(\"cuda\")\n", + " config = Config.from_file(\n", + " \"results/silicon-tutorial/si/config.yaml\"\n", + " )\n", + "\n", + " # config[\"train_on_keys\"]=[\"forces\", \"total_energy\"]\n", + " # config[\"model_builders\"] = [\"EnergyModel\", \"PerSpeciesRescale\", \"ForceOutput\", \"RescaleEnergyEtc\"]\n", + " model = model_from_config(config, initialize=False)\n", + " d = torch.load(\n", + " \"results/silicon-tutorial/si/best_model.pth\",\n", + " map_location=device,\n", + " )\n", + " model.load_state_dict(d)\n", + "\n", + " df = pd.DataFrame(mlearn)\n", + "\n", + " def get_allegro_forces(model=[], atoms=[], cutoff=5):\n", + " ase_atoms = atoms.ase_converter()\n", + " a = AtomicData.from_ase(ase_atoms, cutoff)\n", + " data = AtomicData.to_AtomicDataDict(a)\n", + " tm = TypeMapper(\n", + " chemical_symbol_to_type=config[\"chemical_symbol_to_type\"]\n", + " )\n", + " data = tm(data)\n", + " out = model(data)\n", + " pen = (\n", + " out[\"total_energy\"]\n", + " .squeeze()\n", + " .cpu()\n", + " .detach()\n", + " .numpy()\n", + " .tolist()\n", + " )\n", + " num_atoms = atoms.num_atoms\n", + " pf = out[\"forces\"].squeeze().cpu().detach().numpy()\n", + " return pen, pf, 0\n", + "\n", + "\n", + " for i in glob.glob(\"jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/*energy*.zip\"):\n", + "\n", + " if \"mlearn\" in i and element in i:\n", + " fname_e = (\n", + " \"AI-MLFF-energy-\"\n", + " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " + \"-test-mae.csv\"\n", + " )\n", + " fname_f = (\n", + " \"AI-MLFF-forces-\"\n", + " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " + \"-test-multimae.csv\"\n", + " )\n", + " fname_s = (\n", + " \"AI-MLFF-stresses-\"\n", + " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " + \"-test-multimae.csv\"\n", + " )\n", + " f_e = open(fname_e, \"w\")\n", + " f_f = open(fname_f, \"w\")\n", + " # f_s = open(fname_s, \"w\")\n", + "\n", + " f_e.write(\"id,target,prediction\\n\")\n", + " f_f.write(\"id,target,prediction\\n\")\n", + " # f_s.write(\"id,prediction\\n\")\n", + " #\n", + " print(i)\n", + " dat = json.loads(\n", + " zipfile.ZipFile(i).read(\n", + " i.split(\"/\")[-1].split(\".zip\")[0]\n", + " )\n", + " )\n", + " print(dat[\"test\"])\n", + " for key, val in dat[\"test\"].items():\n", + " entry = df[df[\"jid\"] == key]\n", + " atoms = Atoms.from_dict(entry.atoms.values[0])\n", + " # print(key,val,df[df['jid']==key],atoms)\n", + " # energy,forces=get_alignn_forces(atoms)\n", + " energy, forces, stress = get_allegro_forces(\n", + " model=model, atoms=atoms\n", + " )\n", + " print(key, val, energy, atoms.num_atoms)\n", + " line = (\n", + " key\n", + " + \",\"\n", + " + str(entry.energy.values[0])\n", + " + \",\"\n", + " + str(energy)\n", + " + \"\\n\"\n", + " )\n", + " f_e.write(line)\n", + " line = (\n", + " key\n", + " + \",\"\n", + " + str(\n", + " \";\".join(\n", + " map(\n", + " str,\n", + " np.array(\n", + " entry.forces.values[0]\n", + " ).flatten(),\n", + " )\n", + " )\n", + " )\n", + " + \",\"\n", + " + str(\n", + " \";\".join(map(str, np.array(forces).flatten()))\n", + " )\n", + " + \"\\n\"\n", + " )\n", + " f_f.write(line)\n", + " # line = (\n", + " # key\n", + " # + \",\"\n", + " # + str(\";\".join(map(str, np.array(stress).flatten())))\n", + " # + \"\\n\"\n", + " # )\n", + " # f_s.write(line)\n", + " f_e.close()\n", + " f_f.close()\n", + " # f_s.close()\n", + " zname = fname_e + \".zip\"\n", + " with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " myzip.write(fname_e)\n", + "\n", + " zname = fname_f + \".zip\"\n", + " with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " myzip.write(fname_f)\n", + "\n", + " # zname = fname_s + \".zip\"\n", + " # with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " # myzip.write(fname_s)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "soGwt8gCJUx4", - "outputId": "69069007-65af-4c12-fff4-bbae0fa538f5" + "id": "cKjZaxI0Vh-K", + "outputId": "7538aa77-4e50-4c68-a7d3-e0e676ac643f" }, - "execution_count": 3, + "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", 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-297.62773938, 'Si-216': -295.77170067, 'Si-217': -291.28958206, 'Si-218': -296.24088456, 'Si-219': -294.41361742, 'Si-220': -334.75283939, 'Si-221': -334.69215136, 'Si-222': -184.71808052, 'Si-223': -121.41180043, 'Si-224': -338.93899696, 'Si-225': -338.83557056, 'Si-226': -335.68901422, 'Si-227': -333.7064957, 'Si-228': -344.85564046, 'Si-229': -344.81108268, 'Si-230': -298.83222646, 'Si-231': -298.96501782, 'Si-232': -295.20943762, 'Si-233': -291.86293882, 'Si-234': -344.74080048, 'Si-235': -344.74080047, 'Si-236': -344.74080046, 'Si-237': -341.22165747, 'Si-238': -341.22165734, 'Si-239': -341.22165747}\n", + "Si-215 -297.62773938 -338.2159729003906 63\n", + "Si-216 -295.77170067 -334.56494140625 63\n", + "Si-217 -291.28958206 -335.72833251953125 63\n", + "Si-218 -296.24088456 -333.9977111816406 63\n", + "Si-219 -294.41361742 -334.2371520996094 63\n", + "Si-220 -334.75283939 -375.88885498046875 63\n", + "Si-221 -334.69215136 -376.1081848144531 63\n", + "Si-222 -184.71808052 -213.72637939453125 36\n", + "Si-223 -121.41180043 -141.29652404785156 24\n", + "Si-224 -338.93899696 -376.8706970214844 64\n", + "Si-225 -338.83557056 -376.7699279785156 64\n", + "Si-226 -335.68901422 -373.9237060546875 64\n", + "Si-227 -333.7064957 -371.8525390625 64\n", + "Si-228 -344.85564046 -382.75616455078125 64\n", + "Si-229 -344.81108268 -382.6435852050781 64\n", + "Si-230 -298.83222646 -343.4004211425781 64\n", + "Si-231 -298.96501782 -344.3780212402344 64\n", + "Si-232 -295.20943762 -339.88250732421875 64\n", + "Si-233 -291.86293882 -336.7851867675781 64\n", + "Si-234 -344.74080048 -377.7457580566406 64\n", + "Si-235 -344.74080047 -377.7456970214844 64\n", + "Si-236 -344.74080046 -377.74578857421875 64\n", + "Si-237 -341.22165747 -377.0001220703125 64\n", + "Si-238 -341.22165734 -377.0001220703125 64\n", + "Si-239 -341.22165747 -377.0000915527344 64\n", + "CPU times: user 15.5 s, sys: 3.88 s, total: 19.4 s\n", + "Wall time: 20min 6s\n" ] } ] @@ -463,86 +743,36 @@ { 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latexcodec-2.0.1 monty-2023.5.8 mp-api-0.33.3 pybtex-0.24.0 pymatgen-2023.7.20 ruamel.yaml-0.17.32 ruamel.yaml.clib-0.2.7 spglib-2.0.2 uncertainties-3.1.7\n" + "total 2728\n", + "-rw-r--r-- 1 root root 2542319 Apr 29 2023 mlearn.json.zip\n", + "drwxr-xr-x 4 root root 4096 Jan 4 14:20 .config\n", + "drwxr-xr-x 1 root root 4096 Jan 4 14:21 sample_data\n", + "drwxr-xr-x 1 root root 4096 Jan 7 03:30 ..\n", + "drwxr-xr-x 8 root root 4096 Jan 7 03:35 allegro\n", + "drwxr-xr-x 8 root root 4096 Jan 7 03:37 jarvis_leaderboard\n", + "drwxr-xr-x 2 root root 4096 Jan 7 05:22 Si_data\n", + "drwxr-xr-x 5 root root 4096 Jan 7 05:22 wandb\n", + "drwxr-xr-x 3 root root 4096 Jan 7 05:22 results\n", + "-rw-r--r-- 1 root root 98929 Jan 7 05:42 AI-MLFF-forces-mlearn_Si-test-multimae.csv\n", + "-rw-r--r-- 1 root root 1194 Jan 7 05:42 AI-MLFF-energy-mlearn_Si-test-mae.csv.zip\n", + "-rw-r--r-- 1 root root 1022 Jan 7 05:42 AI-MLFF-energy-mlearn_Si-test-mae.csv\n", + "drwxr-xr-x 1 root root 4096 Jan 7 05:42 .\n", + "-rw-r--r-- 1 root root 99111 Jan 7 05:42 AI-MLFF-forces-mlearn_Si-test-multimae.csv.zip\n" ] } ] @@ -550,3479 +780,99 @@ { "cell_type": "code", "source": [ - "# install allegro\n", - "!git clone --depth 1 https://github.com/mir-group/allegro.git\n", - "!pip install allegro/" + "# #Test example material\n", + "# from jarvis.core.atoms import Atoms\n", + "# from jarvis.db.figshare import get_jid_data\n", + "# atoms = Atoms.from_dict(get_jid_data(jid='JVASP-1002',dataset='dft_3d')['atoms'])\n", + "# ase_atoms = atoms.ase_converter()\n", + "# a = ASEDataset.from_atoms_list([ase_atoms,ase_atoms],extra_fixed_fields={\"r_max\": 5.0})" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3FqDN7vTJ8ds", - "outputId": "5aa47aa0-a999-4a1f-e46b-bdd748456e2b" + "id": "rxCCG4xx5jOK" }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "fatal: destination path 'allegro' already exists and is not an empty 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"cell_type": "code", "source": [ - "pip install jarvis-tools" + "en_df = pd.read_csv('AI-MLFF-energy-mlearn_Si-test-mae.csv.zip')\n", + "from sklearn.metrics import mean_absolute_error\n", + "print(mean_absolute_error(en_df['target'],en_df['prediction']))\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(en_df['target'],en_df['prediction'],'.')\n", + "plt.xlabel('DFT energy(eV)')\n", + "plt.ylabel('FF energy(eV)')\n" ], "metadata": { + "id": "FU4jyfg-Rtjl", "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 485 }, - "id": "f_vtPWpNY7Tl", - "outputId": "980baeb3-d122-44a2-9648-2f81d314bd09" + "outputId": "66065fbd-a380-449a-a56a-b7dd13ded8f9" }, - "execution_count": 6, + "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Collecting jarvis-tools\n", - " Downloading jarvis_tools-2023.5.26-py2.py3-none-any.whl (974 kB)\n", - "\u001b[2K 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/usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (3.4)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->jarvis-tools) (1.16.0)\n", - "Installing collected packages: xmltodict, jarvis-tools\n", - "Successfully installed jarvis-tools-2023.5.26 xmltodict-0.13.0\n" + "37.870087930323436\n" ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'FF energy(eV)')" + ] + }, + "metadata": {}, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} } ] }, { "cell_type": "code", "source": [ - "import numpy as np\n", - "from jarvis.core.atoms import pmg_to_atoms\n", - "from jarvis.db.jsonutils import dumpjson\n", - "from jarvis.db.jsonutils import loadjson\n", - "from monty.serialization import loadfn, MontyEncoder, MontyDecoder\n", - "from ase.stress import voigt_6_to_full_3x3_stress\n", - "# Ref: https://github.com/materialsvirtuallab/mlearn\n", - "data = loadfn(\n", - " \"Si_training.json\",\n", - " cls=MontyDecoder,\n", - ")\n", - "train_structures = [d[\"structure\"] for d in data]\n", - "train_energies = [d[\"outputs\"][\"energy\"] for d in data]\n", - "train_forces = [d[\"outputs\"][\"forces\"] for d in data]\n", - "train_stresses = [d[\"outputs\"][\"virial_stress\"] for d in data]\n", - "\n", - "\n", - "data = loadfn(\n", - " \"Si_test.json\",\n", - " cls=MontyDecoder,\n", - ")\n", - "test_structures = [d[\"structure\"] for d in data]\n", - "test_energies = [d[\"outputs\"][\"energy\"] for d in data]\n", - "test_forces = [d[\"outputs\"][\"forces\"] for d in data]\n", - "test_stresses = [d[\"outputs\"][\"virial_stress\"] for d in data]\n", - "\n" - ], - "metadata": { - "id": "uQcYsDbgJIiC" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import os\n", - "# fix colab imports\n", - "import site\n", - "site.main()\n", - "\n", - "# set to allow anonymous WandB\n", - "os.environ[\"WANDB_ANONYMOUS\"] = \"must\"\n", - "os.makedirs('Si_data')" - ], - "metadata": { - "id": "huyOqxYmY5G7" - }, - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "f = open(\"Si_data/sitraj.xyz\", \"w\")\n", - "mem = []\n", - "count = 0\n", - "line = \"\"\n", - "for i, j, k, l in zip(\n", - " train_structures, train_energies, train_forces, train_stresses\n", - "):\n", - " info = {}\n", - " atoms = pmg_to_atoms(i)\n", - " #print(atoms)\n", - " count += 1\n", - " info[\"jid\"] = str(count)\n", - " info[\"atoms\"] = atoms.to_dict()\n", - " info[\"total_energy\"] = j / atoms.num_atoms\n", - " info[\"forces\"] = k\n", - " info[\"stresses\"] = voigt_6_to_full_3x3_stress(l).tolist()\n", - " mem.append(info)\n", - " line += str(atoms.num_atoms) + \"\\n\"\n", - " line += (\n", - " \"Lattice=\"\n", - " + '\"'\n", - " + \" \".join(map(str, (atoms.lattice_mat).flatten()))\n", - " + '\"'\n", - " + \" Properties=species:S:1:pos:R:3:forces:R:3 energy=\"\n", - " + str(j)\n", - " + ' stress=\"'\n", - " + \" \".join(map(str, np.array(info[\"stresses\"]).flatten()))\n", - " + '\"'\n", - " + \" free_energy=\"\n", - " + str(j)\n", - " + ' pbc=\"T T T\"'\n", - " + \"\\n\"\n", - " )\n", - " for m, n, p in zip(atoms.elements, atoms.cart_coords, k):\n", - " line += (\n", - " str(m)\n", - " + \" \"\n", - " + \" \".join(map(str, n))\n", - " + \" \"\n", - " + \" \".join(map(str, p))\n", - " + \"\\n\"\n", - " )\n", - " # print(line)\n", - " f.write(line)\n", - "for i, j, k, l in zip(\n", - " test_structures, test_energies, test_forces, train_stresses\n", - "):\n", - " info = {}\n", - " count += 1\n", - " info[\"jid\"] = str(count)\n", - " atoms = pmg_to_atoms(i)\n", - " info[\"atoms\"] = atoms.to_dict()\n", - " info[\"total_energy\"] = j / atoms.num_atoms\n", - " info[\"forces\"] = k\n", - " #info[\"stresses\"] = l\n", - " #info[\"stresses\"] = voigt_6_to_full_3x3_stress(l)\n", - " info[\"stresses\"] = voigt_6_to_full_3x3_stress(l).tolist()\n", - " mem.append(info)\n", - " line += str(atoms.num_atoms) + \"\\n\"\n", - " line += (\n", - " \"Lattice=\"\n", - " + '\"'\n", - " + \" \".join(map(str, (atoms.lattice_mat).flatten()))\n", - " + '\"'\n", - " + \" Properties=species:S:1:pos:R:3:forces:R:3 energy=\"\n", - " + str(j)\n", - " + ' stress=\"'\n", - " #+ \" \".join(map(str, np.array(l).flatten()))\n", - " + \" \".join(map(str, np.array(info[\"stresses\"]).flatten()))\n", - " + '\"'\n", - " + \" free_energy=\"\n", - " + str(j)\n", - " + ' pbc=\"T T T\"'\n", - " + \"\\n\"\n", - " )\n", - " for m, n, p in zip(atoms.elements, atoms.cart_coords, k):\n", - " line += (\n", - " str(m)\n", - " + \" \"\n", - " + \" \".join(map(str, n))\n", - " + \" \"\n", - " + \" \".join(map(str, p))\n", - " + \"\\n\"\n", - " )\n", - " # print(line)\n", - " f.write(line)\n", - "for i, j, k, l in zip(\n", - " test_structures, test_energies, test_forces, train_stresses\n", - "):\n", - " info = {}\n", - " count += 1\n", - " info[\"jid\"] = str(count)\n", - " atoms = pmg_to_atoms(i)\n", - " info[\"atoms\"] = atoms.to_dict()\n", - " info[\"total_energy\"] = j / atoms.num_atoms\n", - " info[\"forces\"] = k\n", - " #info[\"stresses\"] = l\n", - " #info[\"stresses\"] = voigt_6_to_full_3x3_stress(l)\n", - " info[\"stresses\"] = voigt_6_to_full_3x3_stress(l).tolist()\n", - " mem.append(info)\n", - " line += str(atoms.num_atoms) + \"\\n\"\n", - " line += (\n", - " \"Lattice=\"\n", - " + '\"'\n", - " + \" \".join(map(str, (atoms.lattice_mat).flatten()))\n", - " + '\"'\n", - " + \" Properties=species:S:1:pos:R:3:forces:R:3 energy=\"\n", - " + str(j)\n", - " + ' stress=\"'\n", - " #+ \" \".join(map(str, np.array(l).flatten()))\n", - " + \" \".join(map(str, np.array(info[\"stresses\"]).flatten()))\n", - " + '\"'\n", - " + \" free_energy=\"\n", - " + str(j)\n", - " + ' pbc=\"T T T\"'\n", - " + \"\\n\"\n", - " )\n", - " for m, n, p in zip(atoms.elements, atoms.cart_coords, k):\n", - " line += (\n", - " str(m)\n", - " + \" \"\n", - " + \" \".join(map(str, n))\n", - " + \" \"\n", - " + \" \".join(map(str, p))\n", - " + \"\\n\"\n", - " )\n", - " # print(line)\n", - " f.write(line)\n", - "f.close()\n", - "dumpjson(data=mem, filename=\"Si_data/id_prop.json\")\n" - ], - "metadata": { - "id": "e3Vk8wEOZOdg" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "%%time\n", - "!rm -rf ./results\n", - "!nequip-train allegro/configs/tutorial.yaml --equivariance-test" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "eiIbyRqIZtIW", - "outputId": "e473d447-dc45-49bc-8562-2f66e2f277e4" - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.15.5\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/content/wandb/run-20230722_155605-8gf60lsk\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33msi\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/anony-mouse-121328701729598718/allegro-tutorial?apiKey=a3119b2aa4383bcda016425f70e052266828d485\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/anony-mouse-121328701729598718/allegro-tutorial/runs/8gf60lsk?apiKey=a3119b2aa4383bcda016425f70e052266828d485\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Do NOT share these links with anyone. They can be used to claim your runs.\n", - "Torch device: cuda\n", - "Processing dataset...\n", - "Loaded data: Batch(atomic_numbers=[2139211, 1], batch=[2139211], cell=[34980, 3, 3], edge_cell_shift=[57841530, 3], edge_index=[2, 57841530], forces=[2139211, 3], free_energy=[34980], pbc=[34980, 3], pos=[2139211, 3], ptr=[34981], stress=[34980, 3, 3], total_energy=[34980, 1])\n", - " processed data size: ~1629.18 MB\n", - "Cached processed data to disk\n", - "Done!\n", - "Successfully loaded the data set of type ASEDataset(34980)...\n", - "Replace string dataset_forces_rms to 0.9137988090515137\n", - "Replace string dataset_per_atom_total_energy_mean to -5.076940059661865\n", - "Atomic outputs are scaled by: [Si: 0.913799], shifted by [Si: -5.076940].\n", - "Replace string dataset_forces_rms to 0.9137988090515137\n", - "Initially outputs are globally scaled by: 0.9137988090515137, total_energy are globally shifted by None.\n", - "Successfully built the network...\n", - "/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py:276: UserWarning: operator() profile_node %884 : int[] = prim::profile_ivalue(%882)\n", - " does not have profile information (Triggered internally at ../torch/csrc/jit/codegen/cuda/graph_fuser.cpp:108.)\n", - " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", - "Equivariance test passed; equivariance errors:\n", - " Errors are in real units, where relevant.\n", - " Please note that the large scale of the typical\n", - " shifts to the (atomic) energy can cause\n", - " catastrophic cancellation and give incorrectly\n", - " the equivariance error as zero for those fields.\n", - " node permutation equivariance of field pos -> max error=0.000e+00\n", - " edge & node permutation invariance for field free_energy -> max error=0.000e+00\n", - " edge & node permutation invariance for field stress -> max error=0.000e+00\n", - " edge & node permutation invariance for field total_energy -> max error=6.104e-05\n", - " edge permutation equivariance of field edge_cell_shift -> max error=0.000e+00\n", - " edge & node permutation invariance for field pbc -> max error=0.000e+00\n", - " node permutation equivariance of field forces -> max error=1.974e-07\n", - " edge & node permutation invariance for field cell -> max error=0.000e+00\n", - " node permutation equivariance of field atom_types -> max error=0.000e+00\n", - " node permutation equivariance of field node_attrs -> max error=0.000e+00\n", - " node permutation equivariance of field node_features -> max error=0.000e+00\n", - " edge permutation equivariance of field edge_vectors -> max error=0.000e+00\n", - " edge permutation equivariance of field edge_lengths -> max error=0.000e+00\n", - " edge permutation equivariance of field edge_embedding -> max error=2.831e-07\n", - " edge permutation equivariance of field edge_attrs -> max error=0.000e+00\n", - " edge permutation equivariance of field edge_features -> max error=1.192e-07\n", - " edge permutation equivariance of field edge_energy -> max error=1.043e-07\n", - " node permutation equivariance of field atomic_energy -> max error=4.768e-07\n", - " node permutation equivariance of field batch -> max error=0.000e+00\n", - " (parity_k=0, did_translate=False, field=pos ) -> max error=4.678e-07\n", - " (parity_k=0, did_translate=False, field=edge_index ) -> max error=0.000e+00\n", - " (parity_k=0, did_translate=False, field=node_attrs ) -> max error=0.000e+00\n", - " (parity_k=0, did_translate=False, field=node_features ) -> max error=0.000e+00\n", - " (parity_k=0, did_translate=False, field=edge_embedding ) -> max error=7.477e-06\n", - " (parity_k=0, did_translate=False, field=edge_attrs ) -> max error=2.147e-06\n", - " (parity_k=0, did_translate=False, field=edge_features ) -> max error=4.686e-06\n", - " (parity_k=0, did_translate=False, field=edge_energy ) -> max error=6.557e-07\n", - " (parity_k=0, did_translate=False, field=atomic_energy ) -> max error=4.768e-07\n", - " (parity_k=0, did_translate=False, field=total_energy ) -> max error=0.000e+00\n", - " (parity_k=0, did_translate=False, field=forces ) -> max error=1.570e-06\n", - " (parity_k=0, did_translate=True , field=pos ) -> max error=9.413e-07\n", - " (parity_k=0, did_translate=True , field=edge_index ) -> max error=0.000e+00\n", - " (parity_k=0, did_translate=True , field=node_attrs ) -> max error=0.000e+00\n", - " (parity_k=0, did_translate=True , field=node_features ) -> max error=0.000e+00\n", - " (parity_k=0, did_translate=True , field=edge_embedding ) -> max error=7.423e-06\n", - " (parity_k=0, did_translate=True , field=edge_attrs ) -> max error=3.855e-06\n", - " (parity_k=0, did_translate=True , field=edge_features ) -> max error=6.437e-06\n", - " (parity_k=0, did_translate=True , field=edge_energy ) -> max error=7.451e-07\n", - " (parity_k=0, did_translate=True , field=atomic_energy ) -> max error=4.768e-07\n", - " (parity_k=0, did_translate=True , field=total_energy ) -> max error=0.000e+00\n", - " (parity_k=0, did_translate=True , field=forces ) -> max error=2.282e-06\n", - " (parity_k=1, did_translate=False, field=pos ) -> max error=4.721e-07\n", - " (parity_k=1, did_translate=False, field=edge_index ) -> max error=0.000e+00\n", - " (parity_k=1, did_translate=False, field=node_attrs ) -> max error=0.000e+00\n", - " (parity_k=1, did_translate=False, field=node_features ) -> max error=0.000e+00\n", - " (parity_k=1, did_translate=False, field=edge_embedding ) -> max error=7.423e-06\n", - " (parity_k=1, did_translate=False, field=edge_attrs ) -> max error=2.839e-06\n", - " (parity_k=1, did_translate=False, field=edge_features ) -> max error=4.590e-06\n", - " (parity_k=1, did_translate=False, field=edge_energy ) -> max error=5.066e-07\n", - " (parity_k=1, did_translate=False, field=atomic_energy ) -> max error=4.768e-07\n", - " (parity_k=1, did_translate=False, field=total_energy ) -> max error=0.000e+00\n", - " (parity_k=1, did_translate=False, field=forces ) -> max error=1.730e-06\n", - " (parity_k=1, did_translate=True , field=pos ) -> max error=9.385e-07\n", - " (parity_k=1, did_translate=True , field=edge_index ) -> max error=0.000e+00\n", - " (parity_k=1, did_translate=True , field=node_attrs ) -> max error=0.000e+00\n", - " (parity_k=1, did_translate=True , field=node_features ) -> max error=0.000e+00\n", - " (parity_k=1, did_translate=True , field=edge_embedding ) -> max error=8.649e-06\n", - " (parity_k=1, did_translate=True , field=edge_attrs ) -> max error=2.132e-06\n", - " (parity_k=1, did_translate=True , field=edge_features ) -> max error=4.590e-06\n", - " (parity_k=1, did_translate=True , field=edge_energy ) -> max error=4.768e-07\n", - " (parity_k=1, did_translate=True , field=atomic_energy ) -> max error=4.768e-07\n", - " (parity_k=1, did_translate=True , field=total_energy ) -> max error=0.000e+00\n", - " (parity_k=1, did_translate=True , field=forces ) -> max error=1.300e-06\n", - "Number of weights: 37352\n", - "Number of trainable weights: 37352\n", - "! Starting training ...\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 0 2 1.09 0.836 0.251 0.54 0.835 26.2 0.414\n", - "\n", - "\n", - " Initialization # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Initial Validation 0 6.205 0.002 0.82 0.212 1.03 0.539 0.828 22.9 0.36\n", - "Wall time: 6.206524298000204\n", - "! Best model 0 1.032\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 1 10 0.383 0.265 0.118 0.37 0.471 20.1 0.314\n", - " 1 20 0.275 0.202 0.0727 0.329 0.411 15.5 0.246\n", - " 1 30 0.337 0.274 0.0631 0.381 0.478 14.7 0.229\n", - " 1 40 0.189 0.11 0.0798 0.239 0.302 16.3 0.258\n", - " 1 50 0.496 0.419 0.0774 0.481 0.591 16 0.254\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 1 2 0.366 0.274 0.092 0.327 0.478 17.4 0.274\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 1 16.098 0.002 0.429 0.133 0.561 0.399 0.605 18.7 0.297\n", - "! Validation 1 16.098 0.002 0.263 0.0874 0.351 0.314 0.469 16.7 0.262\n", - "Wall time: 16.099436577999995\n", - "! Best model 1 0.351\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 2 10 0.0777 0.0703 0.00735 0.194 0.242 4.94 0.0784\n", - " 2 20 0.759 0.522 0.237 0.47 0.66 28.5 0.445\n", - " 2 30 0.365 0.322 0.0425 0.41 0.519 11.9 0.188\n", - " 2 40 0.818 0.621 0.197 0.544 0.72 25.9 0.405\n", - " 2 50 0.331 0.145 0.186 0.274 0.348 25.2 0.394\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 2 2 0.276 0.213 0.0625 0.289 0.422 14 0.22\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 2 17.593 0.002 0.223 0.0815 0.305 0.292 0.436 14.8 0.234\n", - "! Validation 2 17.593 0.002 0.2 0.0632 0.264 0.276 0.409 14.2 0.223\n", - "Wall time: 17.59445267199999\n", - "! Best model 2 0.264\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 3 10 0.0842 0.0627 0.0215 0.183 0.229 8.43 0.134\n", - " 3 20 0.159 0.0583 0.1 0.174 0.221 18.2 0.289\n", - " 3 30 0.19 0.187 0.00277 0.312 0.396 3.08 0.0481\n", - " 3 40 0.0748 0.0525 0.0224 0.167 0.209 8.61 0.137\n", - " 3 50 0.319 0.221 0.0988 0.248 0.429 18.4 0.287\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 3 2 0.234 0.168 0.065 0.261 0.375 14.4 0.226\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 3 19.391 0.002 0.179 0.0707 0.25 0.268 0.391 13.3 0.212\n", - "! Validation 3 19.391 0.002 0.158 0.0613 0.219 0.249 0.363 13.7 0.216\n", - "Wall time: 19.39341814600016\n", - "! Best model 3 0.219\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 4 10 0.0661 0.0512 0.0149 0.164 0.207 7.03 0.112\n", - " 4 20 0.37 0.212 0.158 0.328 0.421 22.9 0.364\n", - " 4 30 0.253 0.25 0.00283 0.353 0.457 3.06 0.0486\n", - " 4 40 0.227 0.168 0.0593 0.298 0.375 14.2 0.223\n", - " 4 50 0.125 0.119 0.00565 0.245 0.315 4.4 0.0687\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 4 2 0.19 0.133 0.0565 0.235 0.334 13.4 0.211\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 4 21.368 0.002 0.145 0.0494 0.194 0.245 0.351 10.8 0.173\n", - "! Validation 4 21.368 0.002 0.131 0.0537 0.184 0.227 0.33 12.9 0.202\n", - "Wall time: 21.370358600000145\n", - "! Best model 4 0.184\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 5 10 0.0644 0.0466 0.0178 0.158 0.197 7.69 0.122\n", - " 5 20 0.0228 0.0115 0.0114 0.0373 0.0979 1.17 0.0975\n", - " 5 30 0.258 0.129 0.13 0.261 0.328 20.7 0.329\n", - " 5 40 0.233 0.168 0.0653 0.298 0.374 14.7 0.234\n", - " 5 50 0.171 6.44e-11 0.171 4.51e-06 7.33e-06 24.2 0.377\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 5 2 0.159 0.123 0.0361 0.225 0.321 10.7 0.169\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 5 23.340 0.002 0.126 0.0426 0.169 0.232 0.328 9.94 0.158\n", - "! Validation 5 23.340 0.002 0.119 0.0359 0.155 0.218 0.315 10.7 0.167\n", - "Wall time: 23.34174522000012\n", - "! Best model 5 0.155\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 6 10 0.0373 0.0312 0.00611 0.133 0.161 4.57 0.0714\n", - " 6 20 0.151 0.11 0.0404 0.245 0.304 11.8 0.184\n", - " 6 30 0.297 0.197 0.1 0.333 0.405 18.5 0.289\n", - " 6 40 0.0658 0.0532 0.0126 0.172 0.211 6.47 0.103\n", - " 6 50 0.0921 0.0771 0.015 0.147 0.254 7.15 0.112\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 6 2 0.15 0.119 0.0314 0.221 0.315 9.89 0.156\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 6 24.829 0.002 0.114 0.0305 0.145 0.222 0.311 8.88 0.14\n", - "! Validation 6 24.829 0.002 0.116 0.0308 0.147 0.215 0.312 9.59 0.15\n", - "Wall time: 24.82983593600011\n", - "! Best model 6 0.147\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 7 10 0.0119 0.00907 0.00287 0.0368 0.087 0.587 0.0489\n", - " 7 20 0.23 0.188 0.0414 0.325 0.396 11.9 0.186\n", - " 7 30 0.0414 0.04 0.00138 0.144 0.183 2.14 0.0339\n", - " 7 40 0.263 0.244 0.0186 0.361 0.451 7.98 0.125\n", - " 7 50 0.113 0.082 0.0309 0.211 0.262 10.3 0.161\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 7 2 0.151 0.13 0.0213 0.229 0.329 7.99 0.126\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 7 26.154 0.002 0.119 0.0197 0.139 0.225 0.318 6.53 0.105\n", - "! Validation 7 26.154 0.002 0.123 0.0213 0.144 0.219 0.32 7.8 0.122\n", - "Wall time: 26.15526405500009\n", - "! Best model 7 0.144\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 8 10 0.169 0.144 0.0241 0.275 0.347 8.94 0.142\n", - " 8 20 0.0409 0.0374 0.00345 0.142 0.177 3.38 0.0537\n", - " 8 30 0.259 0.187 0.0717 0.311 0.396 15.7 0.245\n", - " 8 40 0.0587 0.0516 0.00712 0.162 0.207 4.86 0.0771\n", - " 8 50 0.0501 0.0433 0.00673 0.15 0.19 4.72 0.075\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 8 2 0.142 0.12 0.0222 0.221 0.317 8.14 0.128\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 8 27.469 0.002 0.109 0.0229 0.132 0.218 0.304 7.3 0.116\n", - "! Validation 8 27.469 0.002 0.116 0.0222 0.138 0.214 0.311 7.93 0.124\n", - "Wall time: 27.47042564200001\n", - "! Best model 8 0.138\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 9 10 0.152 0.1 0.0517 0.167 0.289 13.3 0.208\n", - " 9 20 0.138 0.113 0.0259 0.244 0.307 9.26 0.147\n", - " 9 30 0.121 0.115 0.0062 0.25 0.309 4.61 0.072\n", - " 9 40 0.00977 0.00757 0.00221 0.0328 0.0795 0.515 0.0429\n", - " 9 50 0.0504 0.0304 0.02 0.124 0.159 8.26 0.129\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 9 2 0.136 0.117 0.0192 0.218 0.312 7.56 0.119\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 9 28.784 0.002 0.104 0.022 0.126 0.214 0.297 7.31 0.116\n", - "! Validation 9 28.784 0.002 0.113 0.0197 0.133 0.211 0.307 7.5 0.118\n", - "Wall time: 28.78449959300019\n", - "! Best model 9 0.133\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 10 10 0.0417 0.0408 0.000837 0.146 0.185 1.67 0.0264\n", - " 10 20 0.0658 0.0338 0.0321 0.134 0.168 10.3 0.164\n", - " 10 30 0.159 0.159 0.000403 0.296 0.364 1.17 0.0183\n", - " 10 40 0.211 0.168 0.0431 0.3 0.374 12.1 0.19\n", - " 10 50 0.138 0.137 0.000344 0.266 0.338 1.07 0.0169\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 10 2 0.133 0.115 0.0181 0.217 0.31 7.22 0.114\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 10 30.092 0.002 0.104 0.0218 0.126 0.214 0.298 6.58 0.107\n", - "! Validation 10 30.092 0.002 0.111 0.0186 0.13 0.209 0.305 7.13 0.112\n", - "Wall time: 30.092846285000178\n", - "! Best model 10 0.130\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 11 10 0.113 0.101 0.0113 0.232 0.291 6.2 0.0969\n", - " 11 20 0.274 0.163 0.11 0.29 0.369 19.4 0.303\n", - " 11 30 0.132 0.132 3.01e-05 0.258 0.332 0.316 0.00501\n", - " 11 40 0.278 0.197 0.0813 0.318 0.406 16.7 0.26\n", - " 11 50 0.313 0.218 0.0944 0.336 0.427 18 0.281\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 11 2 0.128 0.111 0.0177 0.214 0.304 7.13 0.112\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 11 31.398 0.002 0.0986 0.0216 0.12 0.209 0.29 6.79 0.108\n", - "! Validation 11 31.398 0.002 0.108 0.0184 0.126 0.207 0.3 7.09 0.111\n", - "Wall time: 31.399047548999988\n", - "! Best model 11 0.126\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 12 10 0.0613 0.0605 0.000861 0.179 0.225 1.69 0.0268\n", - " 12 20 0.0675 0.0266 0.0409 0.12 0.149 11.8 0.185\n", - " 12 30 0.23 0.178 0.0526 0.312 0.385 13.4 0.21\n", - " 12 40 0.0315 0.031 0.000483 0.125 0.161 1.26 0.0201\n", - " 12 50 0.063 0.0334 0.0296 0.133 0.167 10.1 0.157\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 12 2 0.126 0.111 0.0146 0.214 0.305 6.36 0.1\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 12 32.720 0.002 0.0971 0.0162 0.113 0.206 0.287 6.14 0.0987\n", - "! Validation 12 32.720 0.002 0.107 0.0156 0.123 0.206 0.299 6.43 0.101\n", - "Wall time: 32.720739902000105\n", - "! Best model 12 0.123\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 13 10 0.176 0.123 0.0528 0.255 0.32 13.2 0.21\n", - " 13 20 0.0518 0.0418 0.00999 0.145 0.187 5.75 0.0913\n", - " 13 30 0.161 0.15 0.0109 0.281 0.354 6.01 0.0953\n", - " 13 40 0.15 0.148 0.00159 0.274 0.352 2.29 0.0364\n", - " 13 50 0.234 0.207 0.0277 0.325 0.415 9.73 0.152\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 13 2 0.125 0.112 0.0133 0.215 0.305 5.94 0.0934\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 13 34.096 0.002 0.107 0.0153 0.122 0.215 0.301 5.83 0.0943\n", - "! Validation 13 34.096 0.002 0.107 0.0144 0.121 0.205 0.299 6.02 0.0943\n", - "Wall time: 34.09757367700013\n", - "! Best model 13 0.121\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 14 10 0.2 0.185 0.0154 0.316 0.393 7.13 0.113\n", - " 14 20 0.129 0.112 0.0173 0.245 0.306 7.68 0.12\n", - " 14 30 0.0857 0.0817 0.004 0.2 0.261 3.7 0.0578\n", - " 14 40 0.202 0.178 0.0239 0.304 0.385 8.89 0.141\n", - " 14 50 0.101 0.0952 0.00591 0.225 0.282 4.5 0.0703\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 14 2 0.12 0.107 0.013 0.211 0.299 5.83 0.0917\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 14 35.877 0.002 0.0974 0.0203 0.118 0.207 0.287 6.8 0.108\n", - "! Validation 14 35.877 0.002 0.104 0.0142 0.118 0.202 0.295 5.97 0.0935\n", - "Wall time: 35.87804695\n", - "! Best model 14 0.118\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 15 10 0.104 0.0825 0.0211 0.202 0.262 8.5 0.133\n", - " 15 20 0.0774 0.0457 0.0317 0.154 0.195 10.3 0.163\n", - " 15 30 0.163 0.155 0.00743 0.284 0.36 4.96 0.0788\n", - " 15 40 0.121 0.117 0.00412 0.246 0.312 3.75 0.0586\n", - " 15 50 0.143 0.138 0.00515 0.282 0.339 4.13 0.0656\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 15 2 0.119 0.105 0.0134 0.209 0.296 5.84 0.0918\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 15 37.863 0.002 0.0905 0.0191 0.11 0.202 0.277 6.32 0.102\n", - "! Validation 15 37.863 0.002 0.103 0.0147 0.117 0.201 0.293 5.92 0.0928\n", - "Wall time: 37.86461178200011\n", - "! Best model 15 0.117\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 16 10 0.116 0.0722 0.0436 0.142 0.246 12.2 0.191\n", - " 16 20 0.0469 0.0467 0.000247 0.114 0.197 0.918 0.0143\n", - " 16 30 0.0429 0.0328 0.0101 0.129 0.165 5.79 0.0919\n", - " 16 40 0.151 0.147 0.00412 0.276 0.35 3.7 0.0587\n", - " 16 50 0.216 0.183 0.0326 0.318 0.391 10.6 0.165\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 16 2 0.117 0.105 0.0119 0.209 0.296 5.4 0.0848\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 16 39.816 0.002 0.0927 0.0124 0.105 0.202 0.28 5.23 0.0847\n", - "! Validation 16 39.816 0.002 0.102 0.0134 0.115 0.201 0.292 5.63 0.0882\n", - "Wall time: 39.817879889000096\n", - "! Best model 16 0.115\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 17 10 0.129 0.128 0.00129 0.264 0.327 2.06 0.0328\n", - " 17 20 0.0435 0.0412 0.00228 0.146 0.185 2.75 0.0437\n", - " 17 30 0.0346 0.0328 0.00183 0.132 0.165 2.46 0.039\n", - " 17 40 0.239 0.19 0.0489 0.317 0.398 12.9 0.202\n", - " 17 50 0.152 0.124 0.0278 0.256 0.322 9.76 0.152\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 17 2 0.113 0.101 0.012 0.205 0.291 5.43 0.0854\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 17 41.437 0.002 0.0934 0.0177 0.111 0.202 0.282 6.49 0.104\n", - "! Validation 17 41.437 0.002 0.0992 0.0136 0.113 0.198 0.288 5.7 0.0894\n", - "Wall time: 41.437974334000046\n", - "! Best model 17 0.113\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 18 10 0.121 0.12 8.69e-05 0.254 0.317 0.545 0.00852\n", - " 18 20 0.015 0.014 0.00105 0.0428 0.108 0.355 0.0296\n", - " 18 30 0.0541 0.0367 0.0174 0.143 0.175 7.59 0.121\n", - " 18 40 0.193 0.137 0.0563 0.269 0.338 13.9 0.217\n", - " 18 50 0.0968 0.0731 0.0238 0.197 0.247 9.02 0.141\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 18 2 0.112 0.101 0.0117 0.205 0.29 5.28 0.083\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 18 42.755 0.002 0.0893 0.0163 0.106 0.198 0.275 6.11 0.0973\n", - "! Validation 18 42.755 0.002 0.0985 0.0134 0.112 0.197 0.287 5.63 0.0882\n", - "Wall time: 42.75604814400003\n", - "! Best model 18 0.112\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 19 10 0.122 0.0938 0.0281 0.222 0.28 9.8 0.153\n", - " 19 20 0.157 0.115 0.0414 0.25 0.31 11.9 0.186\n", - " 19 30 0.103 0.103 0.000308 0.169 0.293 1.03 0.016\n", - " 19 40 0.157 0.14 0.0163 0.275 0.342 7.35 0.117\n", - " 19 50 0.0303 0.0301 0.000151 0.129 0.159 0.719 0.0112\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 19 2 0.11 0.0986 0.0117 0.202 0.287 5.22 0.0819\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 19 44.052 0.002 0.0869 0.0134 0.1 0.196 0.271 5.55 0.0899\n", - "! Validation 19 44.052 0.002 0.0966 0.0136 0.11 0.195 0.284 5.64 0.0884\n", - "Wall time: 44.052739125000016\n", - "! Best model 19 0.110\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 20 10 0.142 0.132 0.01 0.266 0.332 5.86 0.0916\n", - " 20 20 0.14 0.138 0.00125 0.278 0.34 2.03 0.0323\n", - " 20 30 0.0462 0.0353 0.0109 0.134 0.172 6.01 0.0954\n", - " 20 40 0.0867 0.0866 0.000157 0.209 0.269 0.732 0.0114\n", - " 20 50 0.197 0.152 0.0441 0.284 0.357 12.3 0.192\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 20 2 0.109 0.0985 0.0106 0.202 0.287 4.84 0.076\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 20 45.382 0.002 0.089 0.0159 0.105 0.198 0.274 5.62 0.0908\n", - "! Validation 20 45.382 0.002 0.0963 0.0125 0.109 0.194 0.284 5.39 0.0845\n", - "Wall time: 45.383323692000204\n", - "! Best model 20 0.109\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 21 10 0.00958 0.00958 5.25e-06 0.0516 0.0894 0.134 0.00209\n", - " 21 20 0.171 0.162 0.00896 0.293 0.368 5.53 0.0865\n", - " 21 30 0.202 0.131 0.0711 0.269 0.331 15.6 0.244\n", - " 21 40 0.00939 5.43e-11 0.00939 4.17e-06 6.74e-06 5.67 0.0886\n", - " 21 50 0.133 0.125 0.0083 0.257 0.323 5.24 0.0832\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 21 2 0.108 0.0962 0.0114 0.2 0.283 5.03 0.0791\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 21 46.678 0.002 0.0819 0.0165 0.0984 0.191 0.263 5.65 0.0891\n", - "! Validation 21 46.678 0.002 0.0947 0.0134 0.108 0.193 0.281 5.6 0.0877\n", - "Wall time: 46.679247928999985\n", - "! Best model 21 0.108\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 22 10 0.189 0.166 0.0231 0.215 0.373 8.88 0.139\n", - " 22 20 0.163 0.15 0.0132 0.286 0.354 6.6 0.105\n", - " 22 30 0.0829 0.0775 0.0054 0.203 0.254 4.3 0.0672\n", - " 22 40 0.0587 0.0559 0.00279 0.172 0.216 3.04 0.0483\n", - " 22 50 0.241 0.168 0.0727 0.296 0.374 15.8 0.246\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 22 2 0.106 0.0952 0.0109 0.199 0.282 4.82 0.0756\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 22 47.990 0.002 0.0876 0.0149 0.102 0.195 0.272 5.73 0.0924\n", - "! Validation 22 47.990 0.002 0.0936 0.013 0.107 0.192 0.28 5.48 0.0858\n", - "Wall time: 47.99094174900006\n", - "! Best model 22 0.107\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 23 10 0.0955 0.0943 0.00129 0.225 0.281 2.1 0.0328\n", - " 23 20 0.161 0.15 0.0117 0.278 0.354 6.24 0.099\n", - " 23 30 0.155 0.136 0.0192 0.278 0.336 8.09 0.126\n", - " 23 40 0.164 0.12 0.0437 0.253 0.317 12 0.191\n", - " 23 50 0.0764 0.0434 0.033 0.149 0.19 10.5 0.166\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 23 2 0.104 0.0933 0.0104 0.197 0.279 4.66 0.0732\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 23 49.304 0.002 0.0839 0.0163 0.1 0.192 0.266 5.87 0.095\n", - "! Validation 23 49.304 0.002 0.0922 0.0126 0.105 0.19 0.277 5.4 0.0846\n", - "Wall time: 49.30485614600002\n", - "! Best model 23 0.105\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 24 10 0.13 0.121 0.00897 0.253 0.318 5.45 0.0866\n", - " 24 20 0.0491 0.0207 0.0284 0.106 0.131 9.86 0.154\n", - " 24 30 0.0763 0.0742 0.00213 0.201 0.249 2.7 0.0422\n", - " 24 40 0.198 0.129 0.069 0.262 0.328 15.1 0.24\n", - " 24 50 0.0745 0.0357 0.0388 0.0997 0.173 11.5 0.18\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 24 2 0.103 0.0928 0.00994 0.196 0.278 4.42 0.0694\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 24 50.634 0.002 0.0816 0.0148 0.0964 0.188 0.262 5.45 0.0877\n", - "! Validation 24 50.634 0.002 0.0913 0.0122 0.103 0.189 0.276 5.26 0.0823\n", - "Wall time: 50.63548598400007\n", - "! Best model 24 0.103\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 25 10 0.203 0.143 0.0597 0.278 0.345 14.3 0.223\n", - " 25 20 0.11 0.11 5.47e-05 0.251 0.303 0.426 0.00676\n", - " 25 30 0.0319 0.03 0.00187 0.123 0.158 2.49 0.0395\n", - " 25 40 0.192 0.166 0.0258 0.298 0.373 9.4 0.147\n", - " 25 50 0.00611 6.03e-11 0.00611 4.77e-06 7.09e-06 4.57 0.0714\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 25 2 0.102 0.0921 0.00958 0.195 0.277 4.2 0.0659\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 25 52.368 0.002 0.082 0.0134 0.0954 0.19 0.263 5.19 0.0842\n", - "! Validation 25 52.368 0.002 0.0904 0.012 0.102 0.188 0.275 5.14 0.0805\n", - "Wall time: 52.36945648000005\n", - "! Best model 25 0.102\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 26 10 0.118 0.106 0.0122 0.239 0.297 6.35 0.101\n", - " 26 20 0.0522 0.022 0.0303 0.109 0.135 10.2 0.159\n", - " 26 30 0.102 0.0896 0.0124 0.158 0.273 6.51 0.102\n", - " 26 40 0.125 0.115 0.00975 0.251 0.31 5.78 0.0902\n", - " 26 50 0.134 0.127 0.00698 0.256 0.326 4.81 0.0763\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 26 2 0.1 0.0899 0.0102 0.193 0.274 4.41 0.0691\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 26 54.292 0.002 0.0802 0.0237 0.104 0.189 0.26 6.82 0.108\n", - "! Validation 26 54.292 0.002 0.0889 0.0126 0.102 0.186 0.272 5.32 0.0833\n", - "Wall time: 54.29318391400011\n", - "! Best model 26 0.102\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 27 10 0.229 0.227 0.00192 0.342 0.435 2.56 0.0401\n", - " 27 20 0.14 0.108 0.0323 0.241 0.3 10.3 0.164\n", - " 27 30 0.127 0.0906 0.0362 0.22 0.275 11.1 0.174\n", - " 27 40 0.0362 0.036 0.000178 0.134 0.173 0.768 0.0122\n", - " 27 50 0.0747 0.0747 3.01e-09 0.202 0.25 0.0032 5.01e-05\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 27 2 0.0995 0.09 0.00952 0.192 0.274 4.18 0.0655\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 27 56.301 0.002 0.0837 0.0207 0.104 0.189 0.266 6.39 0.105\n", - "! Validation 27 56.301 0.002 0.0883 0.0119 0.1 0.185 0.271 5.15 0.0807\n", - "Wall time: 56.303499583000075\n", - "! Best model 27 0.100\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 28 10 0.067 0.0226 0.0445 0.108 0.137 12.3 0.193\n", - " 28 20 0.0737 0.0712 0.00243 0.195 0.244 2.88 0.0451\n", - " 28 30 0.212 0.164 0.0479 0.292 0.37 12.8 0.2\n", - " 28 40 0.239 0.161 0.0777 0.297 0.367 16.3 0.255\n", - " 28 50 0.119 0.117 0.00225 0.252 0.313 2.73 0.0434\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 28 2 0.0983 0.0893 0.00901 0.192 0.273 4.01 0.0629\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 28 58.114 0.002 0.0793 0.0209 0.1 0.186 0.259 6.48 0.106\n", - "! Validation 28 58.114 0.002 0.0875 0.0113 0.0988 0.184 0.27 5.03 0.0787\n", - "Wall time: 58.11439637400008\n", - "! Best model 28 0.099\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 29 10 0.0135 5.91e-11 0.0135 4.66e-06 7.03e-06 6.79 0.106\n", - " 29 20 0.161 0.127 0.0342 0.26 0.325 10.8 0.169\n", - " 29 30 0.134 0.134 0.000124 0.264 0.334 0.641 0.0102\n", - " 29 40 0.103 0.0694 0.0337 0.186 0.241 10.7 0.168\n", - " 29 50 0.0551 0.051 0.00409 0.119 0.206 3.74 0.0584\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 29 2 0.0981 0.0893 0.00875 0.192 0.273 3.85 0.0604\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 29 59.429 0.002 0.0805 0.0163 0.0968 0.186 0.261 5.84 0.0948\n", - "! Validation 29 59.429 0.002 0.0871 0.0111 0.0982 0.184 0.27 4.91 0.0768\n", - "Wall time: 59.43028433000018\n", - "! Best model 29 0.098\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 30 10 0.108 0.0893 0.0186 0.22 0.273 7.97 0.125\n", - " 30 20 0.024 0.0194 0.00461 0.102 0.127 3.97 0.062\n", - " 30 30 0.0447 0.0139 0.0308 0.0867 0.108 10.3 0.16\n", - " 30 40 0.102 0.101 0.000203 0.235 0.291 0.833 0.013\n", - " 30 50 0.0363 0.033 0.00327 0.131 0.166 3.29 0.0523\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 30 2 0.0965 0.0875 0.00906 0.19 0.27 4.06 0.0636\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 30 60.725 0.002 0.0738 0.0174 0.0913 0.18 0.249 6.18 0.0992\n", - "! Validation 30 60.725 0.002 0.0856 0.0114 0.097 0.182 0.267 5.06 0.0792\n", - "Wall time: 60.72593052800016\n", - "! Best model 30 0.097\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 31 10 0.0199 0.0162 0.00367 0.0944 0.116 3.54 0.0554\n", - " 31 20 0.0548 0.0183 0.0364 0.0472 0.124 2.09 0.174\n", - " 31 30 0.16 0.119 0.0407 0.254 0.316 11.8 0.184\n", - " 31 40 0.108 0.099 0.0088 0.221 0.288 5.4 0.0857\n", - " 31 50 0.00543 5.8e-11 0.00543 4.62e-06 6.96e-06 4.31 0.0674\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 31 2 0.0945 0.0857 0.00881 0.188 0.268 4.01 0.063\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 31 62.034 0.002 0.0745 0.0129 0.0874 0.18 0.251 4.89 0.0811\n", - "! Validation 31 62.034 0.002 0.0841 0.0111 0.0952 0.181 0.265 5.02 0.0786\n", - "Wall time: 62.0345880320001\n", - "! Best model 31 0.095\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 32 10 0.124 0.106 0.0171 0.172 0.298 7.64 0.119\n", - " 32 20 0.0097 5.55e-11 0.0097 4.53e-06 6.81e-06 5.76 0.09\n", - " 32 30 0.125 0.109 0.0153 0.246 0.302 7.12 0.113\n", - " 32 40 0.07 0.0674 0.00262 0.193 0.237 2.99 0.0468\n", - " 32 50 0.0196 0.0173 0.0023 0.0946 0.12 2.8 0.0438\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 32 2 0.0928 0.0837 0.00903 0.186 0.264 4.09 0.0641\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 32 63.335 0.002 0.0725 0.0121 0.0847 0.178 0.247 5.16 0.0834\n", - "! Validation 32 63.335 0.002 0.0825 0.0114 0.0939 0.179 0.262 5.1 0.0798\n", - "Wall time: 63.33574177100013\n", - "! Best model 32 0.094\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 33 10 0.172 0.154 0.0184 0.207 0.358 7.94 0.124\n", - " 33 20 0.0416 0.0408 0.000748 0.144 0.185 1.57 0.025\n", - " 33 30 0.124 0.114 0.00973 0.242 0.309 5.68 0.0901\n", - " 33 40 0.209 0.149 0.0604 0.275 0.353 14.4 0.225\n", - " 33 50 0.0334 0.033 0.000404 0.131 0.166 1.16 0.0184\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 33 2 0.0919 0.083 0.00894 0.185 0.263 4 0.0628\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 33 64.640 0.002 0.0781 0.0165 0.0947 0.182 0.256 5.63 0.0927\n", - "! Validation 33 64.640 0.002 0.0818 0.0113 0.0931 0.179 0.261 5.04 0.0789\n", - "Wall time: 64.64077707500019\n", - "! Best model 33 0.093\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 34 10 0.129 0.117 0.0124 0.252 0.312 6.51 0.102\n", - " 34 20 0.0526 0.0171 0.0355 0.0964 0.12 11 0.172\n", - " 34 30 0.127 0.114 0.0131 0.249 0.308 6.58 0.105\n", - " 34 40 0.17 0.116 0.0538 0.18 0.312 13.6 0.212\n", - " 34 50 0.0785 0.0785 6.71e-06 0.148 0.256 0.151 0.00237\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 34 2 0.0897 0.081 0.00867 0.183 0.26 4 0.0628\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 34 65.999 0.002 0.0728 0.0175 0.0903 0.177 0.247 6.01 0.095\n", - "! Validation 34 65.999 0.002 0.08 0.011 0.091 0.177 0.258 5.01 0.0784\n", - "Wall time: 66.00008639900011\n", - "! Best model 34 0.091\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 35 10 0.12 0.101 0.0184 0.232 0.291 7.8 0.124\n", - " 35 20 0.00786 5.94e-11 0.00786 4.79e-06 7.05e-06 5.19 0.081\n", - " 35 30 0.0928 0.0602 0.0326 0.179 0.224 10.6 0.165\n", - " 35 40 0.117 0.108 0.00984 0.243 0.3 5.8 0.0906\n", - " 35 50 0.000189 7.73e-11 0.000189 5.09e-06 8.03e-06 0.804 0.0126\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 35 2 0.0882 0.0797 0.00852 0.181 0.258 3.97 0.0623\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 35 67.329 0.002 0.0706 0.0179 0.0884 0.174 0.244 5.94 0.0966\n", - "! Validation 35 67.329 0.002 0.0787 0.0108 0.0895 0.175 0.256 4.98 0.0779\n", - "Wall time: 67.32975756100018\n", - "! Best model 35 0.089\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 36 10 0.225 0.195 0.0304 0.308 0.403 10.2 0.159\n", - " 36 20 0.0361 0.0357 0.000431 0.131 0.173 1.2 0.019\n", - " 36 30 0.122 0.105 0.0161 0.238 0.297 7.31 0.116\n", - " 36 40 0.0257 0.0253 0.000448 0.112 0.145 1.22 0.0193\n", - " 36 50 0.122 0.114 0.0078 0.243 0.309 5.08 0.0807\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 36 2 0.0874 0.0795 0.00796 0.181 0.258 3.72 0.0583\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 36 68.995 0.002 0.073 0.012 0.0849 0.177 0.248 5 0.0816\n", - "! Validation 36 68.995 0.002 0.0781 0.0102 0.0883 0.175 0.255 4.79 0.075\n", - "Wall time: 68.9977623650002\n", - "! Best model 36 0.088\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 37 10 0.0442 0.0437 0.000519 0.151 0.191 1.33 0.0208\n", - " 37 20 0.0498 0.0381 0.0117 0.138 0.178 6.32 0.0988\n", - " 37 30 0.0281 0.0273 0.000792 0.117 0.151 1.62 0.0257\n", - " 37 40 0.0277 0.0249 0.00278 0.111 0.144 3.04 0.0482\n", - " 37 50 0.0806 0.0599 0.0207 0.18 0.224 8.42 0.132\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 37 2 0.086 0.0786 0.0074 0.18 0.256 3.53 0.0553\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 37 70.866 0.002 0.0718 0.0108 0.0826 0.174 0.245 4.61 0.074\n", - "! Validation 37 70.866 0.002 0.0771 0.00955 0.0867 0.174 0.254 4.64 0.0727\n", - "Wall time: 70.86770910500013\n", - "! Best model 37 0.087\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 38 10 0.0481 0.0481 3.06e-05 0.156 0.2 0.324 0.00506\n", - " 38 20 0.119 0.113 0.00519 0.25 0.308 4.21 0.0658\n", - " 38 30 0.0567 0.045 0.0117 0.112 0.194 6.33 0.0989\n", - " 38 40 0.115 0.109 0.0057 0.241 0.302 4.35 0.069\n", - " 38 50 0.0256 0.0254 0.000155 0.11 0.146 0.717 0.0114\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 38 2 0.0837 0.0758 0.00784 0.177 0.252 3.72 0.0584\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 38 72.863 0.002 0.0661 0.0139 0.08 0.169 0.236 5.31 0.0858\n", - "! Validation 38 72.863 0.002 0.075 0.0101 0.085 0.172 0.25 4.8 0.0751\n", - "Wall time: 72.86528628100018\n", - "! Best model 38 0.085\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 39 10 0.0708 0.0706 0.000185 0.196 0.243 0.795 0.0124\n", - " 39 20 0.0442 0.0295 0.0147 0.127 0.157 6.97 0.111\n", - " 39 30 0.193 0.153 0.0392 0.274 0.358 11.6 0.181\n", - " 39 40 0.0391 0.0304 0.00868 0.122 0.159 5.36 0.0851\n", - " 39 50 0.12 0.108 0.012 0.241 0.3 6.3 0.1\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 39 2 0.0819 0.0746 0.00731 0.175 0.25 3.54 0.0554\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 39 74.862 0.002 0.0677 0.0129 0.0806 0.17 0.238 5.42 0.087\n", - "! Validation 39 74.862 0.002 0.0738 0.00949 0.0833 0.171 0.248 4.67 0.073\n", - "Wall time: 74.86277496700018\n", - "! Best model 39 0.083\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 40 10 0.0194 0.0151 0.00429 0.0896 0.112 3.83 0.0598\n", - " 40 20 0.0306 0.0255 0.00502 0.111 0.146 4.08 0.0648\n", - " 40 30 0.0813 0.0813 4e-05 0.203 0.261 0.364 0.00578\n", - " 40 40 0.103 0.0996 0.0039 0.166 0.288 3.65 0.0571\n", - " 40 50 0.14 0.123 0.0173 0.263 0.32 7.7 0.12\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 40 2 0.0805 0.0731 0.00735 0.174 0.247 3.53 0.0553\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 40 76.173 0.002 0.0667 0.0131 0.0798 0.169 0.237 4.88 0.0805\n", - "! Validation 40 76.173 0.002 0.0724 0.00957 0.082 0.169 0.246 4.66 0.0728\n", - "Wall time: 76.1743991960002\n", - "! Best model 40 0.082\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 41 10 0.0295 0.0291 0.000388 0.124 0.156 1.13 0.018\n", - " 41 20 0.0824 0.0677 0.0146 0.192 0.238 7.07 0.111\n", - " 41 30 0.0955 0.0938 0.00163 0.22 0.28 2.33 0.0369\n", - " 41 40 0.0584 0.0163 0.0421 0.092 0.117 12 0.187\n", - " 41 50 0.181 0.117 0.0645 0.254 0.312 14.8 0.232\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 41 2 0.0799 0.0728 0.00708 0.173 0.247 3.42 0.0536\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 41 77.481 0.002 0.0672 0.0128 0.08 0.17 0.237 5.1 0.0819\n", - "! Validation 41 77.481 0.002 0.072 0.00926 0.0812 0.169 0.245 4.56 0.0714\n", - "Wall time: 77.48225752799999\n", - "! Best model 41 0.081\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 42 10 0.123 0.118 0.00508 0.25 0.314 4.1 0.0651\n", - " 42 20 0.0931 0.0781 0.015 0.199 0.255 7.04 0.112\n", - " 42 30 0.028 0.0142 0.0139 0.0859 0.109 6.89 0.108\n", - " 42 40 3.37e-05 7.69e-11 3.37e-05 5.44e-06 8.01e-06 0.339 0.0053\n", - " 42 50 0.128 0.121 0.00654 0.184 0.318 4.73 0.0739\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 42 2 0.0779 0.0713 0.00655 0.172 0.244 3.25 0.051\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 42 78.796 0.002 0.0681 0.00945 0.0776 0.169 0.239 4.69 0.0771\n", - "! Validation 42 78.796 0.002 0.0704 0.00867 0.0791 0.167 0.242 4.43 0.0693\n", - "Wall time: 78.7971429690001\n", - "! Best model 42 0.079\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 43 10 0.114 0.111 0.00356 0.236 0.304 3.44 0.0545\n", - " 43 20 0.059 0.0565 0.00249 0.176 0.217 2.92 0.0456\n", - " 43 30 0.0872 0.0871 0.000131 0.212 0.27 0.659 0.0105\n", - " 43 40 0.193 0.116 0.0773 0.241 0.311 16.3 0.254\n", - " 43 50 0.142 0.114 0.0282 0.178 0.309 9.82 0.153\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 43 2 0.0768 0.0704 0.00637 0.171 0.243 3.24 0.0507\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 43 80.132 0.002 0.0683 0.0136 0.0819 0.169 0.24 5.12 0.0832\n", - "! Validation 43 80.132 0.002 0.0698 0.00841 0.0782 0.166 0.241 4.39 0.0687\n", - "Wall time: 80.1334141100001\n", - "! Best model 43 0.078\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 44 10 0.0162 0.0141 0.00211 0.0876 0.109 2.68 0.0419\n", - " 44 20 0.029 0.0234 0.00553 0.108 0.14 4.28 0.068\n", - " 44 30 0.0396 0.0389 0.000698 0.139 0.18 1.54 0.0241\n", - " 44 40 0.127 0.122 0.00487 0.252 0.32 4.08 0.0638\n", - " 44 50 0.0359 0.0348 0.00112 0.132 0.17 1.93 0.0306\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 44 2 0.077 0.0709 0.00612 0.171 0.243 3.08 0.0482\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 44 81.476 0.002 0.0658 0.00955 0.0753 0.167 0.235 4.39 0.0723\n", - "! Validation 44 81.476 0.002 0.0699 0.00817 0.0781 0.167 0.242 4.27 0.0668\n", - "Wall time: 81.47654692600008\n", - "! Best model 44 0.078\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 45 10 0.018 0.0109 0.00712 0.0754 0.0954 4.94 0.0771\n", - " 45 20 0.118 0.11 0.00819 0.175 0.303 5.29 0.0827\n", - " 45 30 0.0667 0.0648 0.0019 0.187 0.233 2.55 0.0399\n", - " 45 40 0.0435 0.0325 0.011 0.134 0.165 6.03 0.0957\n", - " 45 50 0.129 0.123 0.0055 0.249 0.321 4.27 0.0678\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 45 2 0.0763 0.0702 0.00608 0.171 0.242 3.07 0.0481\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 45 82.810 0.002 0.0657 0.0127 0.0784 0.166 0.234 4.89 0.0797\n", - "! Validation 45 82.810 0.002 0.0693 0.00815 0.0774 0.166 0.24 4.27 0.0667\n", - "Wall time: 82.81088046700006\n", - "! Best model 45 0.077\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 46 10 0.1 0.0995 0.00057 0.232 0.288 1.37 0.0218\n", - " 46 20 0.0139 0.0138 0.000104 0.0864 0.107 0.596 0.00931\n", - " 46 30 0.0463 0.0343 0.012 0.13 0.169 6.29 0.0999\n", - " 46 40 0.0834 0.0694 0.014 0.139 0.241 6.91 0.108\n", - " 46 50 0.0868 0.0775 0.00932 0.205 0.254 5.65 0.0882\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 46 2 0.0751 0.0694 0.00571 0.17 0.241 2.93 0.0459\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 46 84.116 0.002 0.0666 0.0111 0.0777 0.167 0.236 4.45 0.0738\n", - "! Validation 46 84.116 0.002 0.0686 0.00767 0.0763 0.166 0.239 4.14 0.0648\n", - "Wall time: 84.11712977399998\n", - "! Best model 46 0.076\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 47 10 0.113 0.095 0.0181 0.224 0.282 7.74 0.123\n", - " 47 20 0.274 0.182 0.092 0.302 0.39 17.7 0.277\n", - " 47 30 0.0363 0.0347 0.00159 0.141 0.17 2.29 0.0364\n", - " 47 40 0.112 0.0954 0.0164 0.226 0.282 7.37 0.117\n", - " 47 50 0.153 0.128 0.0248 0.189 0.328 9.22 0.144\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 47 2 0.0736 0.0674 0.0062 0.167 0.237 3.29 0.0516\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 47 85.674 0.002 0.0667 0.0149 0.0816 0.167 0.237 5.86 0.0949\n", - "! Validation 47 85.674 0.002 0.0675 0.00812 0.0756 0.164 0.237 4.38 0.0685\n", - "Wall time: 85.67590357000017\n", - "! Best model 47 0.076\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 48 10 0.0901 0.0898 0.00027 0.214 0.274 0.945 0.015\n", - " 48 20 0.13 0.108 0.0216 0.163 0.301 4.84 0.134\n", - " 48 30 0.0306 0.0227 0.00786 0.106 0.138 5.1 0.081\n", - " 48 40 0.0518 0.0517 3.56e-05 0.163 0.208 0.343 0.00545\n", - " 48 50 0.0824 0.0807 0.00169 0.206 0.26 2.4 0.0376\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 48 2 0.0738 0.0681 0.00572 0.168 0.238 3.11 0.0487\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 48 87.526 0.002 0.0646 0.0114 0.076 0.165 0.233 5.06 0.0826\n", - "! Validation 48 87.526 0.002 0.068 0.00752 0.0755 0.165 0.238 4.19 0.0656\n", - "Wall time: 87.52810144099999\n", - "! Best model 48 0.076\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 49 10 0.0305 0.011 0.0195 0.0756 0.0957 8.16 0.128\n", - " 49 20 0.123 0.119 0.00417 0.249 0.315 3.72 0.059\n", - " 49 30 0.157 0.107 0.0499 0.238 0.299 13.1 0.204\n", - " 49 40 0.135 0.124 0.0109 0.186 0.321 6.11 0.0955\n", - " 49 50 0.0662 0.0564 0.00974 0.125 0.217 5.77 0.0902\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 49 2 0.0732 0.0677 0.00548 0.167 0.238 3.01 0.0471\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 49 89.527 0.002 0.0656 0.0137 0.0793 0.165 0.234 5.57 0.0906\n", - "! Validation 49 89.527 0.002 0.0673 0.00727 0.0746 0.164 0.237 4.1 0.0641\n", - "Wall time: 89.52865907800015\n", - "! Best model 49 0.075\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 50 10 0.117 0.117 6.55e-05 0.249 0.313 0.466 0.0074\n", - " 50 20 0.0366 0.0329 0.00365 0.134 0.166 3.48 0.0552\n", - " 50 30 0.103 0.0984 0.00427 0.231 0.287 3.76 0.0597\n", - " 50 40 0.0159 0.0115 0.00442 0.0779 0.098 3.89 0.0608\n", - " 50 50 0.0329 0.0225 0.0103 0.106 0.137 5.85 0.0929\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 50 2 0.0737 0.0684 0.00526 0.168 0.239 2.82 0.0442\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 50 91.474 0.002 0.066 0.0095 0.0755 0.166 0.235 4.23 0.0698\n", - "! Validation 50 91.474 0.002 0.0677 0.00707 0.0748 0.165 0.238 3.98 0.0622\n", - "Wall time: 91.47561553300011\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 51 10 0.078 0.0747 0.00336 0.192 0.25 3.34 0.053\n", - " 51 20 0.138 0.1 0.0373 0.229 0.289 11.3 0.177\n", - " 51 30 0.0934 0.0885 0.00492 0.217 0.272 4.04 0.0641\n", - " 51 40 0.0314 0.0309 0.000458 0.12 0.161 1.23 0.0196\n", - " 51 50 0.0121 0.0112 0.00089 0.0763 0.0968 1.74 0.0273\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 51 2 0.0725 0.0673 0.00513 0.167 0.237 2.82 0.0443\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 51 92.880 0.002 0.0648 0.0139 0.0787 0.165 0.233 5.47 0.0887\n", - "! Validation 51 92.880 0.002 0.067 0.0069 0.0739 0.164 0.237 3.97 0.0622\n", - "Wall time: 92.88123096900017\n", - "! Best model 51 0.074\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 52 10 0.145 0.125 0.0206 0.247 0.323 8.26 0.131\n", - " 52 20 0.103 0.0733 0.0296 0.197 0.247 9.9 0.157\n", - " 52 30 0.13 0.0958 0.0345 0.225 0.283 10.9 0.17\n", - " 52 40 0.22 0.151 0.0687 0.287 0.355 15.3 0.24\n", - " 52 50 0.093 0.077 0.016 0.199 0.254 7.29 0.116\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 52 2 0.0715 0.0655 0.006 0.165 0.234 3.36 0.0526\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 52 94.171 0.002 0.0644 0.0234 0.0878 0.165 0.233 7.2 0.119\n", - "! Validation 52 94.171 0.002 0.0658 0.00782 0.0736 0.162 0.234 4.36 0.0682\n", - "Wall time: 94.17162295600019\n", - "! Best model 52 0.074\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 53 10 0.0486 0.017 0.0316 0.047 0.119 1.95 0.162\n", - " 53 20 0.0568 0.0542 0.00262 0.171 0.213 2.95 0.0468\n", - " 53 30 0.0892 0.0856 0.00359 0.211 0.267 3.45 0.0547\n", - " 53 40 0.218 0.161 0.0574 0.296 0.366 14 0.219\n", - " 53 50 0.119 0.102 0.0172 0.23 0.292 7.68 0.12\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 53 2 0.0715 0.0657 0.00585 0.164 0.234 3.31 0.0519\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 53 95.476 0.002 0.0652 0.0142 0.0794 0.165 0.234 5.69 0.0924\n", - "! Validation 53 95.476 0.002 0.0661 0.00757 0.0737 0.162 0.235 4.29 0.0671\n", - "Wall time: 95.47657163000008\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 54 10 0.0907 0.0907 1.29e-05 0.22 0.275 0.21 0.00328\n", - " 54 20 0.0939 0.0829 0.011 0.152 0.263 6.14 0.0959\n", - " 54 30 0.0283 0.0276 0.000695 0.108 0.152 1.52 0.0241\n", - " 54 40 0.141 0.109 0.0321 0.239 0.301 10.5 0.164\n", - " 54 50 0.0512 0.0317 0.0195 0.122 0.163 8.04 0.128\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 54 2 0.072 0.0665 0.0055 0.165 0.236 3.12 0.0489\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 54 96.819 0.002 0.0641 0.0105 0.0747 0.163 0.232 4.68 0.0768\n", - "! Validation 54 96.819 0.002 0.0665 0.0072 0.0737 0.163 0.236 4.14 0.0648\n", - "Wall time: 96.81964709099998\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 55 10 0.132 0.0855 0.047 0.215 0.267 12.5 0.198\n", - " 55 20 0.118 0.0931 0.0249 0.226 0.279 9.09 0.144\n", - " 55 30 0.129 0.12 0.00872 0.174 0.317 3.07 0.0854\n", - " 55 40 0.167 0.152 0.0154 0.271 0.356 7.25 0.113\n", - " 55 50 0.0136 0.0113 0.00232 0.077 0.0971 2.81 0.044\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 55 2 0.0711 0.0654 0.00571 0.164 0.234 3.23 0.0506\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 55 98.136 0.002 0.0622 0.0121 0.0743 0.162 0.228 5.2 0.0845\n", - "! Validation 55 98.136 0.002 0.0656 0.0075 0.0731 0.162 0.234 4.25 0.0665\n", - "Wall time: 98.1371862750002\n", - "! Best model 55 0.073\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 56 10 0.11 0.103 0.00684 0.233 0.293 4.76 0.0756\n", - " 56 20 0.0327 0.0306 0.00205 0.12 0.16 2.61 0.0414\n", - " 56 30 0.144 0.136 0.00803 0.186 0.337 2.95 0.0819\n", - " 56 40 0.0279 0.0273 0.000596 0.115 0.151 1.41 0.0223\n", - " 56 50 0.0294 0.0294 4.34e-05 0.116 0.157 0.379 0.00602\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 56 2 0.0707 0.0651 0.00562 0.164 0.233 3.15 0.0493\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 56 99.448 0.002 0.0638 0.0112 0.075 0.164 0.231 4.78 0.0788\n", - "! Validation 56 99.448 0.002 0.0652 0.00745 0.0727 0.162 0.233 4.21 0.0658\n", - "Wall time: 99.45066124100003\n", - "! Best model 56 0.073\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 57 10 0.131 0.103 0.0282 0.24 0.293 9.83 0.154\n", - " 57 20 0.0348 0.0261 0.00877 0.11 0.148 5.39 0.0856\n", - " 57 30 0.0315 0.0312 0.000291 0.131 0.161 0.982 0.0156\n", - " 57 40 0.0335 0.0326 0.000985 0.122 0.165 1.81 0.0287\n", - " 57 50 0.198 0.147 0.0514 0.28 0.35 13.3 0.207\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 57 2 0.0705 0.0649 0.00566 0.164 0.233 3.15 0.0495\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 57 100.809 0.002 0.0647 0.00963 0.0744 0.165 0.233 4.53 0.074\n", - "! Validation 57 100.809 0.002 0.0652 0.00749 0.0727 0.161 0.233 4.21 0.0659\n", - "Wall time: 100.80991286300014\n", - "! Best model 57 0.073\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 58 10 0.0459 0.0341 0.0119 0.13 0.169 6.28 0.0996\n", - " 58 20 0.119 0.115 0.00384 0.247 0.31 3.57 0.0566\n", - " 58 30 0.0944 0.0894 0.005 0.222 0.273 4.07 0.0646\n", - " 58 40 0.0955 0.0876 0.00787 0.21 0.271 5.11 0.0811\n", - " 58 50 0.138 0.0892 0.0491 0.219 0.273 12.8 0.202\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 58 2 0.0703 0.0647 0.0056 0.163 0.233 3.08 0.0483\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 58 102.264 0.002 0.0638 0.0114 0.0752 0.163 0.231 4.74 0.0801\n", - "! Validation 58 102.264 0.002 0.0647 0.00749 0.0722 0.161 0.232 4.17 0.0653\n", - "Wall time: 102.26593335000007\n", - "! Best model 58 0.072\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 59 10 0.115 0.11 0.00504 0.249 0.303 4.15 0.0649\n", - " 59 20 0.0715 0.0695 0.00199 0.19 0.241 2.61 0.0408\n", - " 59 30 0.113 0.109 0.0039 0.251 0.302 3.65 0.0571\n", - " 59 40 0.0274 0.0264 0.001 0.108 0.148 1.82 0.0289\n", - " 59 50 0.0806 0.0792 0.00144 0.209 0.257 2.19 0.0347\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 59 2 0.0698 0.064 0.00586 0.162 0.231 3.27 0.0513\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 59 104.043 0.002 0.0627 0.0114 0.0741 0.163 0.229 5.11 0.0829\n", - "! Validation 59 104.043 0.002 0.0644 0.00774 0.0722 0.16 0.232 4.31 0.0675\n", - "Wall time: 104.0445039350002\n", - "! Best model 59 0.072\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 60 10 0.0772 0.0745 0.00265 0.2 0.249 3.01 0.047\n", - " 60 20 0.131 0.107 0.024 0.236 0.299 9.05 0.141\n", - " 60 30 0.144 0.119 0.0257 0.26 0.315 9.38 0.147\n", - " 60 40 0.0401 0.038 0.00208 0.129 0.178 2.62 0.0416\n", - " 60 50 0.0536 0.0241 0.0295 0.109 0.142 9.89 0.157\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 60 2 0.0695 0.0639 0.00561 0.162 0.231 3.2 0.0501\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 60 106.037 0.002 0.0617 0.0112 0.0729 0.162 0.228 4.68 0.0777\n", - "! Validation 60 106.037 0.002 0.0645 0.00741 0.0719 0.16 0.232 4.23 0.0662\n", - "Wall time: 106.03901392000012\n", - "! Best model 60 0.072\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 61 10 0.182 0.118 0.0637 0.244 0.314 14.8 0.231\n", - " 61 20 0.0211 0.0139 0.00719 0.0849 0.108 4.96 0.0775\n", - " 61 30 0.115 0.109 0.0055 0.242 0.302 4.27 0.0678\n", - " 61 40 0.177 0.157 0.0197 0.288 0.363 8.2 0.128\n", - " 61 50 0.0658 0.0148 0.051 0.0874 0.111 13.2 0.206\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 61 2 0.0694 0.0642 0.00517 0.162 0.232 3 0.0471\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 61 108.016 0.002 0.0641 0.0161 0.0802 0.166 0.232 6.14 0.101\n", - "! Validation 61 108.016 0.002 0.0646 0.00695 0.0716 0.161 0.232 4.08 0.0639\n", - "Wall time: 108.01846257500006\n", - "! Best model 61 0.072\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 62 10 5.34e-07 8.68e-11 5.34e-07 5.35e-06 8.51e-06 0.0427 0.000668\n", - " 62 20 0.152 0.134 0.0183 0.273 0.334 7.91 0.124\n", - " 62 30 0.0562 0.052 0.00418 0.166 0.208 3.72 0.0591\n", - " 62 40 0.0276 0.0275 8.21e-05 0.11 0.151 0.522 0.00828\n", - " 62 50 0.0571 0.055 0.0021 0.169 0.214 2.68 0.0419\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 62 2 0.0696 0.0647 0.00493 0.163 0.232 2.83 0.0444\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 62 109.609 0.002 0.0655 0.0135 0.079 0.166 0.234 4.97 0.082\n", - "! Validation 62 109.609 0.002 0.0646 0.00679 0.0713 0.161 0.232 3.99 0.0624\n", - "Wall time: 109.61030814800006\n", - "! Best model 62 0.071\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 63 10 0.0831 0.0814 0.00169 0.208 0.261 2.36 0.0375\n", - " 63 20 0.145 0.104 0.0411 0.236 0.294 11.9 0.185\n", - " 63 30 0.0159 0.0112 0.00465 0.0764 0.0968 3.99 0.0623\n", - " 63 40 0.084 0.0685 0.0155 0.194 0.239 7.27 0.114\n", - " 63 50 0.0544 0.0515 0.00295 0.12 0.207 3.17 0.0496\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 63 2 0.0691 0.0635 0.00557 0.162 0.23 3.11 0.0488\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 63 110.931 0.002 0.062 0.0114 0.0734 0.161 0.228 4.65 0.0762\n", - "! Validation 63 110.931 0.002 0.0638 0.00753 0.0713 0.16 0.231 4.21 0.0659\n", - "Wall time: 110.93184176099999\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 64 10 0.0222 0.0219 0.000301 0.103 0.135 0.998 0.0158\n", - " 64 20 0.0424 0.0264 0.016 0.109 0.148 7.27 0.115\n", - " 64 30 0.0415 0.0197 0.0218 0.0502 0.128 1.62 0.135\n", - " 64 40 0.0198 0.0137 0.0061 0.0841 0.107 4.57 0.0714\n", - " 64 50 0.15 0.142 0.00846 0.277 0.344 5.38 0.0841\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 64 2 0.0689 0.0636 0.00533 0.162 0.23 2.99 0.0469\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 64 112.234 0.002 0.0622 0.0101 0.0723 0.162 0.228 4.59 0.0755\n", - "! Validation 64 112.234 0.002 0.0639 0.00723 0.0711 0.16 0.231 4.11 0.0643\n", - "Wall time: 112.23479097900008\n", - "! Best model 64 0.071\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 65 10 0.0973 0.096 0.00129 0.219 0.283 2.07 0.0328\n", - " 65 20 0.0171 0.0151 0.002 0.09 0.112 2.62 0.0409\n", - " 65 30 0.0349 0.0254 0.00956 0.0538 0.146 1.07 0.0894\n", - " 65 40 0.184 0.183 0.000229 0.226 0.391 0.885 0.0138\n", - " 65 50 0.127 0.123 0.00385 0.265 0.321 3.63 0.0567\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 65 2 0.0679 0.0629 0.00504 0.16 0.229 2.89 0.0453\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 65 113.554 0.002 0.0627 0.00977 0.0725 0.161 0.229 4.7 0.0759\n", - "! Validation 65 113.554 0.002 0.0635 0.00684 0.0703 0.159 0.23 4.01 0.0627\n", - "Wall time: 113.55491477100009\n", - "! Best model 65 0.070\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 66 10 0.202 0.135 0.0671 0.257 0.336 15.1 0.237\n", - " 66 20 0.0291 0.0291 6.66e-06 0.109 0.156 0.149 0.00236\n", - " 66 30 0.133 0.115 0.0173 0.247 0.31 7.69 0.12\n", - " 66 40 0.102 0.0914 0.0104 0.221 0.276 5.87 0.0931\n", - " 66 50 0.0701 0.053 0.0171 0.165 0.21 7.53 0.119\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 66 2 0.0684 0.0634 0.00499 0.161 0.23 2.9 0.0454\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 66 114.892 0.002 0.0647 0.0113 0.076 0.166 0.233 4.96 0.0793\n", - "! Validation 66 114.892 0.002 0.0641 0.00676 0.0708 0.16 0.231 3.99 0.0625\n", - "Wall time: 114.8931862190002\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 67 10 0.0902 0.0777 0.0125 0.2 0.255 6.43 0.102\n", - " 67 20 0.0649 0.0398 0.0251 0.141 0.182 9.27 0.145\n", - " 67 30 0.0186 0.00985 0.00875 0.0524 0.0907 5.47 0.0855\n", - " 67 40 0.0479 0.0449 0.00307 0.112 0.194 3.24 0.0507\n", - " 67 50 0.0301 0.0298 0.000364 0.115 0.158 1.1 0.0174\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 67 2 0.0683 0.0634 0.0049 0.161 0.23 2.77 0.0434\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 67 116.229 0.002 0.0611 0.00938 0.0705 0.161 0.226 4.24 0.0708\n", - "! Validation 67 116.229 0.002 0.064 0.00667 0.0707 0.16 0.231 3.91 0.0611\n", - "Wall time: 116.23016239200001\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 68 10 0.0568 0.0417 0.0151 0.108 0.187 7.17 0.112\n", - " 68 20 0.152 0.117 0.0351 0.255 0.312 11 0.171\n", - " 68 30 0.096 0.0878 0.00821 0.217 0.271 5.22 0.0828\n", - " 68 40 0.0321 0.0321 1.09e-05 0.134 0.164 0.19 0.00302\n", - " 68 50 0.134 0.132 0.00237 0.259 0.332 2.85 0.0445\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 68 2 0.0681 0.0632 0.0049 0.16 0.23 2.76 0.0432\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 68 117.552 0.002 0.0628 0.008 0.0708 0.161 0.229 3.95 0.0651\n", - "! Validation 68 117.552 0.002 0.0637 0.00671 0.0704 0.159 0.231 3.92 0.0613\n", - "Wall time: 117.55306987500012\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 69 10 0.0325 0.0306 0.00184 0.12 0.16 2.47 0.0392\n", - " 69 20 0.0567 0.0425 0.0142 0.15 0.188 6.96 0.109\n", - " 69 30 0.179 0.142 0.0373 0.275 0.344 11.3 0.176\n", - " 69 40 0.0297 0.0225 0.00717 0.106 0.137 4.87 0.0774\n", - " 69 50 0.03 0.0258 0.00423 0.109 0.147 3.74 0.0594\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 69 2 0.0674 0.0626 0.00482 0.16 0.229 2.75 0.0432\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 69 118.924 0.002 0.0603 0.0146 0.075 0.16 0.225 5.83 0.0949\n", - "! Validation 69 118.924 0.002 0.0632 0.00663 0.0699 0.159 0.23 3.92 0.0613\n", - "Wall time: 118.92567081400011\n", - "! Best model 69 0.070\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 70 10 0.038 0.0243 0.0137 0.0822 0.142 6.86 0.107\n", - " 70 20 0.0999 0.0866 0.0133 0.211 0.269 6.65 0.106\n", - " 70 30 0.0403 0.0381 0.0022 0.139 0.178 2.74 0.0429\n", - " 70 40 0.162 0.14 0.0224 0.279 0.341 8.75 0.137\n", - " 70 50 0.0557 0.0422 0.0135 0.15 0.188 6.8 0.106\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 70 2 0.0673 0.0626 0.0047 0.16 0.229 2.73 0.0428\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 70 120.728 0.002 0.0598 0.0101 0.0699 0.159 0.224 4.72 0.0774\n", - "! Validation 70 120.728 0.002 0.0632 0.00648 0.0697 0.158 0.23 3.89 0.0608\n", - "Wall time: 120.7300484660002\n", - "! Best model 70 0.070\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 71 10 0.0546 0.0518 0.00276 0.165 0.208 3.02 0.048\n", - " 71 20 0.0627 0.0184 0.0444 0.0475 0.124 2.31 0.192\n", - " 71 30 0.0298 0.0289 0.000911 0.109 0.155 1.74 0.0276\n", - " 71 40 0.142 0.127 0.0155 0.254 0.325 7.29 0.114\n", - " 71 50 0.0758 0.0711 0.00472 0.196 0.244 3.96 0.0628\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 71 2 0.0673 0.0626 0.00472 0.16 0.229 2.76 0.0433\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 71 122.706 0.002 0.0599 0.00967 0.0696 0.159 0.224 4.35 0.0724\n", - "! Validation 71 122.706 0.002 0.0631 0.00648 0.0696 0.158 0.229 3.9 0.0609\n", - "Wall time: 122.70826236000016\n", - "! Best model 71 0.070\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 72 10 0.038 0.0372 0.000816 0.128 0.176 1.64 0.0261\n", - " 72 20 0.0165 0.0148 0.00168 0.0898 0.111 2.4 0.0375\n", - " 72 30 0.0612 0.0593 0.0019 0.175 0.223 2.55 0.0398\n", - " 72 40 0.106 0.102 0.00407 0.226 0.292 3.67 0.0583\n", - " 72 50 0.0916 0.0757 0.0158 0.2 0.251 7.25 0.115\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 72 2 0.0668 0.0619 0.0049 0.159 0.227 2.85 0.0447\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 72 124.695 0.002 0.0621 0.00817 0.0703 0.161 0.228 4.07 0.0664\n", - "! Validation 72 124.695 0.002 0.0626 0.00668 0.0693 0.158 0.229 3.96 0.0619\n", - "Wall time: 124.696597828\n", - "! Best model 72 0.069\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 73 10 0.103 0.0984 0.00434 0.165 0.287 3.85 0.0602\n", - " 73 20 0.124 0.111 0.0136 0.252 0.304 6.82 0.107\n", - " 73 30 0.134 0.109 0.0258 0.236 0.301 9.39 0.147\n", - " 73 40 0.022 0.0115 0.0105 0.0775 0.0979 6 0.0937\n", - " 73 50 0.0932 0.0891 0.0041 0.22 0.273 3.69 0.0585\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 73 2 0.0669 0.0625 0.00447 0.16 0.228 2.62 0.0411\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 73 126.399 0.002 0.0626 0.00772 0.0704 0.163 0.229 4.08 0.0673\n", - "! Validation 73 126.399 0.002 0.0629 0.00622 0.0691 0.158 0.229 3.79 0.0593\n", - "Wall time: 126.39960853800017\n", - "! Best model 73 0.069\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 74 10 0.159 0.121 0.0381 0.26 0.318 11.4 0.178\n", - " 74 20 0.102 0.102 0.000163 0.227 0.291 0.735 0.0117\n", - " 74 30 0.0351 0.0277 0.00743 0.116 0.152 4.96 0.0787\n", - " 74 40 0.0135 0.013 0.000529 0.0833 0.104 1.35 0.021\n", - " 74 50 0.0915 0.0864 0.00511 0.212 0.269 4.18 0.0653\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 74 2 0.0668 0.0623 0.00446 0.159 0.228 2.58 0.0404\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 74 127.750 0.002 0.0615 0.0157 0.0772 0.161 0.227 5.68 0.0932\n", - "! Validation 74 127.750 0.002 0.0626 0.00626 0.0689 0.158 0.229 3.77 0.059\n", - "Wall time: 127.75133718699999\n", - "! Best model 74 0.069\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 75 10 0.000262 7.31e-11 0.000262 5.38e-06 7.81e-06 0.946 0.0148\n", - " 75 20 0.0641 0.0534 0.0107 0.166 0.211 5.95 0.0944\n", - " 75 30 0.109 0.107 0.00207 0.236 0.298 2.66 0.0416\n", - " 75 40 0.115 0.0886 0.0262 0.215 0.272 9.31 0.148\n", - " 75 50 0.0278 0.0275 0.000255 0.114 0.152 0.919 0.0146\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 75 2 0.067 0.0625 0.00452 0.16 0.228 2.65 0.0416\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 75 129.051 0.002 0.0628 0.0112 0.0739 0.162 0.229 4.95 0.0799\n", - "! Validation 75 129.051 0.002 0.0628 0.00631 0.0691 0.158 0.229 3.81 0.0596\n", - "Wall time: 129.05153958200003\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 76 10 0.0351 0.0328 0.00234 0.12 0.165 2.79 0.0442\n", - " 76 20 0.0617 0.0476 0.0141 0.115 0.199 6.95 0.109\n", - " 76 30 0.119 0.109 0.01 0.24 0.302 5.86 0.0915\n", - " 76 40 0.113 0.102 0.0111 0.241 0.292 6.16 0.0963\n", - " 76 50 0.0738 0.0732 0.000631 0.195 0.247 1.47 0.023\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 76 2 0.0661 0.0612 0.00486 0.158 0.226 2.89 0.0454\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 76 130.382 0.002 0.0608 0.00787 0.0686 0.159 0.225 3.87 0.0617\n", - "! Validation 76 130.382 0.002 0.0623 0.0066 0.0689 0.157 0.228 3.98 0.0622\n", - "Wall time: 130.38238039700013\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 77 10 0.0266 0.0221 0.0045 0.0511 0.136 0.735 0.0613\n", - " 77 20 0.182 0.126 0.0552 0.249 0.325 13.7 0.215\n", - " 77 30 0.0798 0.079 0.000853 0.2 0.257 1.68 0.0267\n", - " 77 40 0.0952 0.0952 5.1e-05 0.224 0.282 0.418 0.00652\n", - " 77 50 0.0751 0.0751 8.46e-07 0.202 0.25 0.0529 0.00084\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 77 2 0.0658 0.0606 0.00516 0.157 0.225 3.03 0.0475\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 77 131.690 0.002 0.0588 0.00914 0.068 0.158 0.222 4.29 0.0691\n", - "! Validation 77 131.690 0.002 0.0621 0.00689 0.069 0.157 0.228 4.06 0.0636\n", - "Wall time: 131.69131595600015\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 78 10 0.0826 0.0821 0.000475 0.211 0.262 1.25 0.0199\n", - " 78 20 0.179 0.151 0.0279 0.271 0.355 9.77 0.153\n", - " 78 30 0.0302 0.0275 0.00276 0.113 0.151 3.02 0.048\n", - " 78 40 0.149 0.129 0.0199 0.256 0.329 8.25 0.129\n", - " 78 50 0.0717 0.02 0.0517 0.047 0.129 2.49 0.208\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 78 2 0.0658 0.0608 0.00496 0.157 0.225 2.91 0.0457\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 78 133.006 0.002 0.0597 0.00927 0.069 0.159 0.224 4.25 0.0713\n", - "! Validation 78 133.006 0.002 0.0621 0.00668 0.0688 0.157 0.228 3.98 0.0622\n", - "Wall time: 133.006712849\n", - "! Best model 78 0.069\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 79 10 0.0195 0.0073 0.0122 0.0451 0.0781 6.46 0.101\n", - " 79 20 0.0289 0.0288 0.000114 0.117 0.155 0.615 0.00976\n", - " 79 30 0.0811 0.0773 0.00378 0.205 0.254 3.54 0.0562\n", - " 79 40 0.0186 0.0126 0.006 0.0814 0.103 4.53 0.0708\n", - " 79 50 0.0228 0.0226 0.000199 0.105 0.137 0.811 0.0129\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 79 2 0.0657 0.0609 0.00481 0.157 0.226 2.83 0.0444\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 79 134.356 0.002 0.0607 0.0102 0.0709 0.16 0.226 4.57 0.0755\n", - "! Validation 79 134.356 0.002 0.0619 0.00655 0.0684 0.157 0.227 3.91 0.0612\n", - "Wall time: 134.3573408750001\n", - "! Best model 79 0.068\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 80 10 0.0514 0.0151 0.0363 0.0887 0.112 11.1 0.174\n", - " 80 20 0.017 0.0143 0.00274 0.0862 0.109 3.06 0.0479\n", - " 80 30 0.00142 8.47e-11 0.00142 5.24e-06 8.41e-06 2.2 0.0344\n", - " 80 40 0.0307 0.0137 0.017 0.0844 0.107 7.63 0.119\n", - " 80 50 0.0372 0.0291 0.0081 0.118 0.156 5.18 0.0822\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 80 2 0.0653 0.0603 0.00508 0.156 0.224 3 0.047\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 80 135.716 0.002 0.0603 0.0107 0.071 0.16 0.225 4.66 0.0772\n", - "! Validation 80 135.716 0.002 0.0615 0.00681 0.0683 0.156 0.227 4.03 0.0631\n", - "Wall time: 135.7170751770002\n", - "! Best model 80 0.068\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 81 10 0.135 0.123 0.0126 0.259 0.32 6.58 0.103\n", - " 81 20 0.0923 0.0887 0.0036 0.22 0.272 3.46 0.0549\n", - " 81 30 0.115 0.113 0.00172 0.239 0.307 2.39 0.0379\n", - " 81 40 0.102 0.102 0.000147 0.228 0.291 0.698 0.0111\n", - " 81 50 0.136 0.101 0.0348 0.23 0.291 10.9 0.17\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 81 2 0.0654 0.0606 0.00484 0.157 0.225 2.87 0.0451\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 81 137.494 0.002 0.0611 0.00928 0.0704 0.16 0.226 4.48 0.0727\n", - "! Validation 81 137.494 0.002 0.0615 0.00658 0.068 0.156 0.227 3.94 0.0617\n", - "Wall time: 137.495926005\n", - "! Best model 81 0.068\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 82 10 0.132 0.117 0.0142 0.253 0.313 6.97 0.109\n", - " 82 20 0.0902 0.0899 0.000266 0.214 0.274 0.939 0.0149\n", - " 82 30 0.0272 0.0266 0.000679 0.109 0.149 1.5 0.0238\n", - " 82 40 0.0975 0.0974 0.000122 0.224 0.285 0.635 0.0101\n", - " 82 50 0.0292 0.0271 0.00213 0.0556 0.15 0.506 0.0422\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 82 2 0.0656 0.0611 0.00456 0.157 0.226 2.65 0.0415\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 82 139.395 0.002 0.0603 0.00598 0.0663 0.16 0.224 3.31 0.0539\n", - "! Validation 82 139.395 0.002 0.0619 0.00629 0.0682 0.156 0.227 3.78 0.0592\n", - "Wall time: 139.39706425500003\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 83 10 0.143 0.094 0.049 0.222 0.28 12.9 0.202\n", - " 83 20 0.0275 0.0129 0.0146 0.0825 0.104 7.08 0.111\n", - " 83 30 0.0707 0.0663 0.00431 0.187 0.235 3.78 0.06\n", - " 83 40 0.147 0.123 0.0235 0.262 0.321 8.97 0.14\n", - " 83 50 0.123 0.108 0.015 0.238 0.301 7.17 0.112\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 83 2 0.0651 0.0604 0.00468 0.157 0.225 2.8 0.0439\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 83 141.348 0.002 0.0598 0.0131 0.073 0.158 0.224 5.14 0.0849\n", - "! Validation 83 141.348 0.002 0.0612 0.0064 0.0676 0.156 0.226 3.88 0.0608\n", - "Wall time: 141.34985558300014\n", - "! Best model 83 0.068\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 84 10 0.052 0.0479 0.00413 0.163 0.2 3.76 0.0587\n", - " 84 20 0.0353 0.0258 0.00951 0.0531 0.147 1.07 0.0891\n", - " 84 30 0.143 0.0974 0.0454 0.231 0.285 12.5 0.195\n", - " 84 40 0.0936 0.0922 0.00147 0.215 0.277 2.21 0.0351\n", - " 84 50 0.0569 0.0568 0.000118 0.171 0.218 0.635 0.00992\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 84 2 0.0651 0.0605 0.00461 0.157 0.225 2.73 0.0429\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 84 143.152 0.002 0.0599 0.00805 0.068 0.161 0.224 4.14 0.0677\n", - "! Validation 84 143.152 0.002 0.0614 0.00633 0.0677 0.156 0.226 3.84 0.06\n", - "Wall time: 143.15298648700013\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 85 10 0.0906 0.0905 0.0001 0.218 0.275 0.576 0.00915\n", - " 85 20 0.129 0.0946 0.0347 0.155 0.281 6.13 0.17\n", - " 85 30 0.1 0.099 0.00115 0.234 0.288 1.98 0.0309\n", - " 85 40 0.16 0.1 0.0592 0.232 0.289 14.2 0.222\n", - " 85 50 0.0237 0.0222 0.00141 0.104 0.136 2.16 0.0343\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 85 2 0.0652 0.0601 0.00518 0.156 0.224 3.05 0.0479\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 85 144.480 0.002 0.0585 0.0112 0.0697 0.159 0.222 4.29 0.0715\n", - "! Validation 85 144.480 0.002 0.0613 0.00691 0.0682 0.156 0.226 4.07 0.0636\n", - "Wall time: 144.481072563\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 86 10 0.00027 7.91e-11 0.00027 5.56e-06 8.13e-06 0.961 0.015\n", - " 86 20 0.119 0.108 0.0106 0.241 0.301 6.03 0.0942\n", - " 86 30 0.0317 0.0317 1.92e-05 0.121 0.163 0.252 0.00401\n", - " 86 40 0.094 0.094 1.05e-05 0.223 0.28 0.19 0.00296\n", - " 86 50 0.101 0.0996 0.00162 0.224 0.288 2.32 0.0368\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 86 2 0.0651 0.0601 0.00495 0.156 0.224 2.93 0.0459\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 86 145.817 0.002 0.0595 0.00806 0.0676 0.158 0.223 4.21 0.0686\n", - "! Validation 86 145.817 0.002 0.0612 0.00669 0.0679 0.156 0.226 3.98 0.0623\n", - "Wall time: 145.81810197000004\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 87 10 0.0578 0.0577 0.00012 0.173 0.219 0.641 0.01\n", - " 87 20 0.0429 0.0259 0.017 0.0523 0.147 1.43 0.119\n", - " 87 30 0.124 0.117 0.00712 0.246 0.313 4.86 0.0771\n", - " 87 40 0.132 0.132 8.49e-05 0.268 0.332 0.539 0.00842\n", - " 87 50 0.106 0.0769 0.0288 0.204 0.253 9.77 0.155\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 87 2 0.0651 0.0604 0.00468 0.157 0.225 2.81 0.0441\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 87 147.122 0.002 0.0667 0.011 0.0777 0.166 0.236 4.99 0.082\n", - "! Validation 87 147.122 0.002 0.0612 0.00641 0.0676 0.156 0.226 3.89 0.0609\n", - "Wall time: 147.12288241700003\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 88 10 0.119 0.103 0.0161 0.237 0.294 7.42 0.116\n", - " 88 20 0.00218 7.35e-11 0.00218 5.14e-06 7.83e-06 2.73 0.0427\n", - " 88 30 0.0911 0.0911 5.73e-08 0.226 0.276 0.0138 0.000218\n", - " 88 40 0.0289 0.0285 0.000439 0.116 0.154 1.21 0.0192\n", - " 88 50 0.0782 0.0767 0.00151 0.197 0.253 2.23 0.0355\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 88 2 0.0649 0.0604 0.00457 0.156 0.225 2.64 0.0413\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 88 148.419 0.002 0.0615 0.00877 0.0703 0.161 0.227 4.14 0.068\n", - "! Validation 88 148.419 0.002 0.0612 0.00633 0.0676 0.155 0.226 3.79 0.0593\n", - "Wall time: 148.41997922300015\n", - "! Best model 88 0.068\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 89 10 0.0823 0.0822 0.000116 0.151 0.262 0.631 0.00985\n", - " 89 20 0.0499 0.0366 0.0134 0.134 0.175 6.66 0.106\n", - " 89 30 0.152 0.12 0.0324 0.25 0.316 10.4 0.165\n", - " 89 40 0.0923 0.0911 0.00119 0.22 0.276 1.99 0.0315\n", - " 89 50 0.0268 0.0264 0.00036 0.0544 0.149 0.208 0.0173\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 89 2 0.0645 0.0597 0.00479 0.155 0.223 2.86 0.0449\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 89 149.712 0.002 0.0612 0.00959 0.0708 0.16 0.226 4.24 0.0686\n", - "! Validation 89 149.712 0.002 0.0609 0.00648 0.0674 0.155 0.226 3.93 0.0614\n", - "Wall time: 149.7135981580002\n", - "! Best model 89 0.067\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 90 10 0.0558 0.0497 0.00604 0.118 0.204 4.55 0.071\n", - " 90 20 0.0231 0.0048 0.0183 0.0366 0.0633 7.92 0.124\n", - " 90 30 0.0809 0.0788 0.0021 0.201 0.256 2.64 0.0419\n", - " 90 40 0.112 0.102 0.00999 0.16 0.292 3.29 0.0913\n", - " 90 50 0.0348 0.0333 0.00153 0.12 0.167 2.25 0.0357\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 90 2 0.0648 0.0597 0.00503 0.156 0.223 2.96 0.0465\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 90 151.036 0.002 0.0608 0.0111 0.0719 0.161 0.226 4.86 0.0793\n", - "! Validation 90 151.036 0.002 0.0611 0.00672 0.0678 0.155 0.226 3.99 0.0624\n", - "Wall time: 151.037316337\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 91 10 0.0325 0.0165 0.016 0.0921 0.118 7.4 0.116\n", - " 91 20 0.156 0.128 0.0278 0.256 0.327 9.75 0.152\n", - " 91 30 0.114 0.105 0.00935 0.243 0.296 5.66 0.0884\n", - " 91 40 0.12 0.103 0.0176 0.234 0.293 7.77 0.121\n", - " 91 50 0.0758 0.0743 0.00141 0.196 0.249 2.2 0.0344\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 91 2 0.0646 0.0597 0.00487 0.155 0.223 2.9 0.0454\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 91 152.336 0.002 0.0606 0.00785 0.0685 0.16 0.225 3.98 0.0659\n", - "! Validation 91 152.336 0.002 0.0611 0.00654 0.0676 0.155 0.226 3.93 0.0615\n", - "Wall time: 152.3370314880001\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 92 10 0.0914 0.0913 0.000171 0.217 0.276 0.754 0.012\n", - " 92 20 0.0951 0.0912 0.00387 0.216 0.276 3.58 0.0568\n", - " 92 30 0.0251 0.0232 0.00192 0.107 0.139 2.53 0.0401\n", - " 92 40 0.0152 0.014 0.00117 0.0868 0.108 2 0.0312\n", - " 92 50 0.093 0.0916 0.0014 0.221 0.277 2.19 0.0342\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 92 2 0.0647 0.0601 0.00457 0.156 0.224 2.74 0.0429\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 92 153.962 0.002 0.059 0.00934 0.0684 0.158 0.222 4.28 0.0682\n", - "! Validation 92 153.962 0.002 0.0613 0.00617 0.0675 0.156 0.226 3.8 0.0594\n", - "Wall time: 153.9634865720002\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 93 10 0.109 0.102 0.00681 0.227 0.292 4.75 0.0754\n", - " 93 20 0.0807 0.0792 0.00147 0.206 0.257 2.24 0.0351\n", - " 93 30 0.138 0.123 0.0147 0.185 0.321 7.09 0.111\n", - " 93 40 0.0112 8.15e-11 0.0112 5.23e-06 8.25e-06 6.19 0.0967\n", - " 93 50 0.119 0.101 0.0181 0.239 0.291 7.86 0.123\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 93 2 0.0644 0.0589 0.00547 0.154 0.222 3.22 0.0506\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 93 155.789 0.002 0.0607 0.00919 0.0699 0.159 0.225 4.32 0.0717\n", - "! Validation 93 155.789 0.002 0.0606 0.00711 0.0677 0.155 0.225 4.16 0.0651\n", - "Wall time: 155.79113780700004\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 94 10 0.137 0.104 0.0323 0.242 0.295 10.5 0.164\n", - " 94 20 0.1 0.0971 0.00292 0.226 0.285 3.11 0.0494\n", - " 94 30 0.0268 0.0268 4.22e-05 0.114 0.149 0.374 0.00593\n", - " 94 40 0.0123 0.0046 0.00769 0.0358 0.062 5.13 0.0801\n", - " 94 50 0.0289 0.0288 8.11e-05 0.117 0.155 0.519 0.00823\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 94 2 0.0643 0.0594 0.00492 0.155 0.223 2.95 0.0463\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 94 157.793 0.002 0.059 0.00879 0.0677 0.158 0.223 4.22 0.0702\n", - "! Validation 94 157.793 0.002 0.0608 0.00655 0.0673 0.155 0.225 3.96 0.0619\n", - "Wall time: 157.7935859470001\n", - "! Best model 94 0.067\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 95 10 0.0271 0.0269 0.000211 0.109 0.15 0.837 0.0133\n", - " 95 20 0.0394 0.0327 0.00665 0.13 0.165 4.77 0.0745\n", - " 95 30 0.0285 0.0281 0.000404 0.113 0.153 1.16 0.0184\n", - " 95 40 0.119 0.0921 0.027 0.22 0.277 9.46 0.15\n", - " 95 50 0.0863 0.0849 0.00135 0.209 0.266 2.15 0.0336\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 95 2 0.0641 0.0593 0.00487 0.155 0.222 2.92 0.0458\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 95 159.741 0.002 0.0593 0.00989 0.0692 0.16 0.223 4.64 0.0758\n", - "! Validation 95 159.741 0.002 0.0607 0.00653 0.0672 0.155 0.225 3.94 0.0617\n", - "Wall time: 159.74316792500008\n", - "! Best model 95 0.067\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 96 10 0.164 0.155 0.00863 0.276 0.36 5.43 0.0849\n", - " 96 20 0.0587 0.0583 0.000361 0.179 0.221 1.11 0.0174\n", - " 96 30 0.132 0.127 0.00553 0.178 0.325 2.45 0.068\n", - " 96 40 0.0423 0.0344 0.00786 0.133 0.17 5.18 0.081\n", - " 96 50 0.0263 0.0263 2.98e-05 0.107 0.148 0.315 0.00499\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 96 2 0.0643 0.0592 0.00512 0.155 0.222 2.94 0.0462\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 96 161.126 0.002 0.0632 0.0104 0.0736 0.162 0.23 4.24 0.0686\n", - "! Validation 96 161.126 0.002 0.0602 0.00689 0.0671 0.154 0.224 3.98 0.0624\n", - "Wall time: 161.12722070500013\n", - "! Best model 96 0.067\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 97 10 0.00231 8.23e-11 0.00231 5.26e-06 8.29e-06 2.81 0.044\n", - " 97 20 0.174 0.15 0.0237 0.273 0.354 9.01 0.141\n", - " 97 30 0.0426 0.016 0.0266 0.0925 0.116 9.53 0.149\n", - " 97 40 0.0441 0.0439 0.000179 0.157 0.192 0.783 0.0122\n", - " 97 50 0.0388 0.0388 2.12e-06 0.13 0.18 0.0837 0.00133\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 97 2 0.0637 0.0589 0.00484 0.154 0.222 2.88 0.0452\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 97 162.430 0.002 0.0601 0.00862 0.0687 0.158 0.224 4.14 0.0654\n", - "! Validation 97 162.430 0.002 0.0601 0.00652 0.0667 0.154 0.224 3.92 0.0613\n", - "Wall time: 162.43092208899998\n", - "! Best model 97 0.067\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 98 10 0.0294 0.028 0.0014 0.113 0.153 2.15 0.0341\n", - " 98 20 0.144 0.131 0.0125 0.261 0.331 6.54 0.102\n", - " 98 30 0.0127 0.0117 0.00107 0.0783 0.0987 1.91 0.0299\n", - " 98 40 0.0366 0.034 0.00263 0.122 0.169 2.95 0.0468\n", - " 98 50 0.0425 0.0414 0.00108 0.148 0.186 1.92 0.03\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 98 2 0.0643 0.0596 0.00472 0.155 0.223 2.82 0.0441\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 98 163.711 0.002 0.0595 0.00683 0.0664 0.159 0.224 3.62 0.0602\n", - "! Validation 98 163.711 0.002 0.0606 0.00639 0.067 0.155 0.225 3.86 0.0604\n", - "Wall time: 163.71217972099998\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 99 10 0.0322 0.0309 0.00125 0.127 0.161 2.03 0.0323\n", - " 99 20 0.000172 7.42e-11 0.000172 5.44e-06 7.87e-06 0.767 0.012\n", - " 99 30 0.0779 0.0578 0.0202 0.173 0.22 8.31 0.13\n", - " 99 40 0.0695 0.0694 7.68e-05 0.189 0.241 0.512 0.00801\n", - " 99 50 0.0833 0.0796 0.0037 0.203 0.258 3.5 0.0556\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 99 2 0.0638 0.0593 0.00455 0.155 0.222 2.64 0.0413\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 99 165.038 0.002 0.0608 0.00859 0.0694 0.16 0.226 4.27 0.0703\n", - "! Validation 99 165.038 0.002 0.0605 0.0062 0.0667 0.154 0.225 3.74 0.0585\n", - "Wall time: 165.039170432\n", - "\n", - "training\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 100 10 0.0311 0.0228 0.00829 0.0796 0.138 5.32 0.0832\n", - " 100 20 0.0501 0.0487 0.00141 0.161 0.202 2.2 0.0343\n", - " 100 30 0.0893 0.0846 0.00474 0.213 0.266 3.97 0.0629\n", - " 100 40 0.104 0.0825 0.0213 0.206 0.263 8.53 0.133\n", - " 100 50 0.0124 0.0123 8.89e-05 0.0798 0.101 0.551 0.00861\n", - "\n", - "validation\n", - "# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e/N_mae\n", - " 100 2 0.0635 0.0591 0.00441 0.154 0.222 2.59 0.0406\n", - "\n", - "\n", - " Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e/N_mae\n", - "! Train 100 166.365 0.002 0.0619 0.0124 0.0743 0.162 0.228 4.89 0.0806\n", - "! Validation 100 166.365 0.002 0.0606 0.00597 0.0665 0.154 0.225 3.69 0.0578\n", - "Wall time: 166.36592601100006\n", - "! Best model 100 0.067\n", - "! Stop training: max epochs\n", - "Wall time: 166.3796381510001\n", - "Cumulative wall time: 166.3796381510001\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish... \u001b[32m(success).\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: LR ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: cumulative_wall ▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇███\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: epoch ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_e/N_mae █▆▃▃▃▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▂▁▂▁▂▁▁▂▁▁▁▁▂\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_e_mae █▆▄▃▃▂▂▂▂▂▃▂▂▂▂▂▂▁▂▂▂▂▂▂▂▂▁▂▁▂▁▂▁▁▂▁▁▁▁▂\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_f_mae █▄▃▃▂▃▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_f_rmse █▄▃▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_loss █▄▂▂▂▂▂▂▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_loss_e █▅▂▂▂▂▁▂▂▂▂▂▁▂▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_loss_f █▃▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_e/N_mae █▅▄▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_e_mae █▅▄▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_f_mae █▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_f_rmse █▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_loss █▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_loss_e █▃▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_loss_f █▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: wall ▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇███\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: LR 0.002\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: cumulative_wall 166.36504\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: epoch 100\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_e/N_mae 0.08057\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_e_mae 4.89086\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_f_mae 0.16246\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_f_rmse 0.22751\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_loss 0.07428\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_loss_e 0.01237\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: training_loss_f 0.0619\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_e/N_mae 0.05778\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_e_mae 3.6947\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_f_mae 0.15438\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_f_rmse 0.22491\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_loss 0.06654\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_loss_e 0.00597\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: validation_loss_f 0.06057\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: wall 166.36504\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33msi\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/anony-mouse-121328701729598718/allegro-tutorial/runs/8gf60lsk?apiKey=a3119b2aa4383bcda016425f70e052266828d485\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20230722_155605-8gf60lsk/logs\u001b[0m\n", - "CPU times: user 8.19 s, sys: 1 s, total: 9.19 s\n", - "Wall time: 22min 27s\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "! ls results/silicon-tutorial/si #/best_model.pth" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0KRsXlG65jGH", - "outputId": "759186b5-9590-451a-8965-2e35f424fc49" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "best_model.pth\tlog\t\t\t metrics_epoch.csv\n", - "config.yaml\tmetrics_batch_train.csv metrics_initialization.csv\n", - "last_model.pth\tmetrics_batch_val.csv\t trainer.pth\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import torch\n", - "from nequip.utils import Config\n", - "from nequip.model import model_from_config\n", - "from nequip.data import AtomicData, ASEDataset\n", - "device = \"cpu\"\n", - "if torch.cuda.is_available():\n", - " device = torch.device(\"cuda\")\n", - "config = Config.from_file(\"results/silicon-tutorial/si/config.yaml\")\n", - "\n", - "#config[\"train_on_keys\"]=[\"forces\", \"total_energy\"]\n", - "#config[\"model_builders\"] = [\"EnergyModel\", \"PerSpeciesRescale\", \"ForceOutput\", \"RescaleEnergyEtc\"]\n", - "model = model_from_config(config, initialize=False)\n", - "d = torch.load('results/silicon-tutorial/si/best_model.pth',map_location=device)\n", - "model.load_state_dict(d)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "mg9svjLM5jI2", - "outputId": "a8555e69-1583-4c89-81d9-0757966ff0da" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 12 - } - ] - }, - { - "cell_type": "code", - "source": [ - "model" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "a0fb_mry5jLl", - "outputId": "22794f24-de5d-417a-e933-a1d52a1dc7eb" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "RescaleOutput(\n", - " (model): GradientOutput(\n", - " (func): SequentialGraphNetwork(\n", - " (one_hot): OneHotAtomEncoding()\n", - " (radial_basis): RadialBasisEdgeEncoding(\n", - " (basis): NormalizedBasis(\n", - " (basis): BesselBasis()\n", - " )\n", - " (cutoff): PolynomialCutoff()\n", - " )\n", - " (spharm): SphericalHarmonicEdgeAttrs(\n", - " (sh): SphericalHarmonics()\n", - " )\n", - " (allegro): Allegro_Module(\n", - " (latents): ModuleList(\n", - " (0): ScalarMLPFunction(\n", - " (_forward): RecursiveScriptModule(original_name=GraphModule)\n", - " )\n", - " )\n", - " (env_embed_mlps): ModuleList(\n", - " (0): ScalarMLPFunction(\n", - " (_forward): RecursiveScriptModule(original_name=GraphModule)\n", - " )\n", - " )\n", - " (tps): ModuleList(\n", - " (0): RecursiveScriptModule(original_name=GraphModule)\n", - " )\n", - " (linears): ModuleList(\n", - " (0): RecursiveScriptModule(original_name=GraphModule)\n", - " )\n", - " (env_linears): ModuleList(\n", - " (0): Identity()\n", - " )\n", - " (_env_weighter): MakeWeightedChannels()\n", - " (final_latent): ScalarMLPFunction(\n", - " (_forward): RecursiveScriptModule(original_name=GraphModule)\n", - " )\n", - " )\n", - " (edge_eng): ScalarMLP(\n", - " (_module): ScalarMLPFunction(\n", - " (_forward): RecursiveScriptModule(original_name=GraphModule)\n", - " )\n", - " )\n", - " (edge_eng_sum): EdgewiseEnergySum()\n", - " (per_species_rescale): PerSpeciesScaleShift()\n", - " (total_energy_sum): AtomwiseReduce()\n", - " )\n", - " )\n", - ")" - ] - }, - "metadata": {}, - "execution_count": 13 - } - ] - }, - { - "cell_type": "code", - "source": [ - "from jarvis.core.atoms import Atoms\n", - "from jarvis.db.figshare import get_jid_data\n", - "atoms = Atoms.from_dict(get_jid_data(jid='JVASP-1002',dataset='dft_3d')['atoms'])\n", - "ase_atoms = atoms.ase_converter()\n", - "a = ASEDataset.from_atoms_list([ase_atoms,ase_atoms],extra_fixed_fields={\"r_max\": 5.0})" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "rxCCG4xx5jOK", - "outputId": "ad42948d-e0aa-4854-833d-72edf6726304" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Obtaining 3D dataset 76k ...\n", - "Reference:https://www.nature.com/articles/s41524-020-00440-1\n", - "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 40.8M/40.8M [00:02<00:00, 17.0MiB/s]\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Loading the zipfile...\n", - "Loading completed.\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Processing dataset...\n", - "Done!\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from nequip.data import AtomicData, Collater, dataset_from_config, register_fields, AtomicDataDict\n", - "from nequip.data.transforms import TypeMapper\n", - "# c = Collater.for_dataset(a, exclude_keys=[])\n", - "a = AtomicData.from_ase(ase_atoms,5)\n", - "data = AtomicData.to_AtomicDataDict(a)\n", - "# tm = TypeMapper(chemical_symbol_to_type={\"Si\": 0})\n", - "tm = TypeMapper(chemical_symbol_to_type = config['chemical_symbol_to_type'])\n", - "data = tm(data)\n", - "out = model(data)\n" - ], - "metadata": { - "id": "0B5JNf8r7Ijz" - }, - "execution_count": 15, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import os\n", - "os.chdir('/content')\n", - "if not os.path.exists('jarvis_leaderboard'):\n", - " !git clone https://github.com/usnistgov/jarvis_leaderboard.git\n", - "# os.chdir('jarvis_leaderboard')\n", - "# !pip install -e .\n", - "os.chdir('/content/jarvis_leaderboard/jarvis_leaderboard/contributions/')\n", - "os.makedirs('allegro_si')\n", - "os.chdir('allegro_si')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vQaB-lCuPvwg", - "outputId": "7379d9ad-d3a3-4f13-b618-a15ec57071c5" - }, - "execution_count": 16, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Cloning into 'jarvis_leaderboard'...\n", - "remote: Enumerating objects: 44059, done.\u001b[K\n", - "remote: Counting objects: 100% (12867/12867), done.\u001b[K\n", - "remote: Compressing objects: 100% (1354/1354), done.\u001b[K\n", - "remote: Total 44059 (delta 6886), reused 12655 (delta 6839), pack-reused 31192\u001b[K\n", - 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"--2023-07-22 16:19:04-- https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20230722/eu-west-1/s3/aws4_request&X-Amz-Date=20230722T161904Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=33b3d4b3862af36a869779fc3942dbc01820e0a545a31326bcf4bba490d3830d\n", - "Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.93.163, 52.218.110.67, 52.92.32.184, ...\n", - "Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.93.163|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 2542319 (2.4M) [application/zip]\n", - "Saving to: ‘mlearn.json.zip’\n", - "\n", - "mlearn.json.zip 100%[===================>] 2.42M 3.32MB/s in 0.7s \n", - "\n", - "2023-07-22 16:19:06 (3.32 MB/s) - ‘mlearn.json.zip’ saved [2542319/2542319]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd\n", - "import json,zipfile\n", - "#This dataset should have same test split (with ids) as the mlearn dataset built above\n", - "df = pd.DataFrame(\n", - " json.loads(\n", - " zipfile.ZipFile(\"mlearn.json.zip\").read(\n", - " \"mlearn.json\"\n", - " )\n", - " )\n", - ")" - ], - "metadata": { - "id": "Jw72urwrQG_4" - }, - "execution_count": 18, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "!pwd" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "eJa4Uh62QfUC", - "outputId": "ae2251d7-26f5-40ea-94dc-388a6216754f" - }, - "execution_count": 19, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content/jarvis_leaderboard/jarvis_leaderboard/contributions/allegro_si\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "def get_allegro_forces(model=[],atoms=[],cutoff=5):\n", - " ase_atoms = atoms.ase_converter()\n", - " a = AtomicData.from_ase(ase_atoms,cutoff)\n", - " data = AtomicData.to_AtomicDataDict(a)\n", - " tm = TypeMapper(chemical_symbol_to_type = config['chemical_symbol_to_type'])\n", - " data = tm(data)\n", - " out = model(data)\n", - " pen=out['total_energy'].squeeze().cpu().detach().numpy().tolist()\n", - " num_atoms=atoms.num_atoms\n", - " pf=out['forces'].squeeze().cpu().detach().numpy()\n", - " return pen,pf,_" - ], - "metadata": { - "id": "5i1-CsYHQvGg" - }, - "execution_count": 20, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import glob\n", - "for i in glob.glob(\"../../benchmarks/AI/MLFF/*energy*.zip\"):\n", - " if \"mlearn\" in i and \"Si\" in i:\n", - " fname_e = (\n", - " \"AI-MLFF-energy-\"\n", - " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", - " + \"-test-mae.csv\"\n", - " )\n", - " fname_f = (\n", - " \"AI-MLFF-forces-\"\n", - " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", - " + \"-test-multimae.csv\"\n", - " )\n", - " fname_s = (\n", - " \"AI-MLFF-stresses-\"\n", - " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", - " + \"-test-multimae.csv\"\n", - " )\n", - " f_e = open(fname_e, \"w\")\n", - " f_f = open(fname_f, \"w\")\n", - " #f_s = open(fname_s, \"w\")\n", - "\n", - " f_e.write(\"id,target,prediction\\n\")\n", - " f_f.write(\"id,prediction\\n\")\n", - " #f_s.write(\"id,prediction\\n\")\n", - "\n", - " print(i)\n", - " dat = json.loads(\n", - " zipfile.ZipFile(i).read(i.split(\"/\")[-1].split(\".zip\")[0])\n", - " )\n", - " print(dat[\"test\"])\n", - " for key, val in dat[\"test\"].items():\n", - " entry = df[df[\"jid\"] == key]\n", - " atoms = Atoms.from_dict(entry.atoms.values[0])\n", - " # print(key,val,df[df['jid']==key],atoms)\n", - " # energy,forces=get_alignn_forces(atoms)\n", - " energy, forces, stress = get_allegro_forces(model=model,atoms=atoms)\n", - " print(key, val, energy, atoms.num_atoms)\n", - " line = key +\",\"+ str(entry.energy.values[0])+\",\" + str(energy) + \"\\n\"\n", - " f_e.write(line)\n", - " line = (\n", - " key\n", - " + \",\"\n", - " + str(\";\".join(map(str, np.array(forces).flatten())))\n", - " + \"\\n\"\n", - " )\n", - " f_f.write(line)\n", - " # line = (\n", - " # key\n", - " # + \",\"\n", - " # + str(\";\".join(map(str, np.array(stress).flatten())))\n", - " # + \"\\n\"\n", - " # )\n", - " # f_s.write(line)\n", - " f_e.close()\n", - " f_f.close()\n", - " # f_s.close()\n", - " zname = fname_e + \".zip\"\n", - " with zipfile.ZipFile(zname, \"w\") as myzip:\n", - " myzip.write(fname_e)\n", - "\n", - " zname = fname_f + \".zip\"\n", - " with zipfile.ZipFile(zname, \"w\") as myzip:\n", - " myzip.write(fname_f)\n", - "\n", - " # zname = fname_s + \".zip\"\n", - " # with zipfile.ZipFile(zname, \"w\") as myzip:\n", - " # myzip.write(fname_s)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ljZJz8BOPGYB", - "outputId": "f9f0b7cd-5580-4212-afc2-764d2d082fef" - }, - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "../../benchmarks/AI/MLFF/mlearn_Si_energy.json.zip\n", - "{'Si-215': -297.62773938, 'Si-216': -295.77170067, 'Si-217': -291.28958206, 'Si-218': -296.24088456, 'Si-219': -294.41361742, 'Si-220': -334.75283939, 'Si-221': -334.69215136, 'Si-222': -184.71808052, 'Si-223': -121.41180043, 'Si-224': -338.93899696, 'Si-225': -338.83557056, 'Si-226': -335.68901422, 'Si-227': -333.7064957, 'Si-228': -344.85564046, 'Si-229': -344.81108268, 'Si-230': -298.83222646, 'Si-231': -298.96501782, 'Si-232': -295.20943762, 'Si-233': -291.86293882, 'Si-234': -344.74080048, 'Si-235': -344.74080047, 'Si-236': -344.74080046, 'Si-237': -341.22165747, 'Si-238': -341.22165734, 'Si-239': -341.22165747}\n", - "Si-215 -297.62773938 -328.6075439453125 63\n", - "Si-216 -295.77170067 -324.45965576171875 63\n", - "Si-217 -291.28958206 -327.27740478515625 63\n", - "Si-218 -296.24088456 -322.92529296875 63\n", - "Si-219 -294.41361742 -324.0484313964844 63\n", - "Si-220 -334.75283939 -366.2948913574219 63\n", - "Si-221 -334.69215136 -366.56170654296875 63\n", - "Si-222 -184.71808052 -208.6649169921875 36\n", - "Si-223 -121.41180043 -138.18006896972656 24\n", - "Si-224 -338.93899696 -366.9707336425781 64\n", - "Si-225 -338.83557056 -366.8772888183594 64\n", - "Si-226 -335.68901422 -364.18890380859375 64\n", - "Si-227 -333.7064957 -362.1649475097656 64\n", - "Si-228 -344.85564046 -372.6488342285156 64\n", - "Si-229 -344.81108268 -372.5396423339844 64\n", - "Si-230 -298.83222646 -334.6480712890625 64\n", - "Si-231 -298.96501782 -335.65496826171875 64\n", - "Si-232 -295.20943762 -331.1341247558594 64\n", - "Si-233 -291.86293882 -328.0296325683594 64\n", - "Si-234 -344.74080048 -366.642578125 64\n", - "Si-235 -344.74080047 -366.642578125 64\n", - "Si-236 -344.74080046 -366.642578125 64\n", - "Si-237 -341.22165747 -367.0040588378906 64\n", - "Si-238 -341.22165734 -367.00408935546875 64\n", - "Si-239 -341.22165747 -367.0040588378906 64\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "entry.energy.values[0]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ya4Pak_cSBHU", - "outputId": "95f23eb3-7834-427b-c39b-f547f6f41ad2" - }, - "execution_count": 22, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "-341.22165747" - ] - }, - "metadata": {}, - "execution_count": 22 - } - ] - }, - { - "cell_type": "code", - "source": [ - "!ls -altr" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cK1rdjyGRqGD", - "outputId": "1572d077-23a0-4d62-bbbf-606baa43b0f9" - }, - "execution_count": 23, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "total 2620\n", - "-rw-r--r-- 1 root root 2542319 Apr 29 01:14 mlearn.json.zip\n", - "drwxr-xr-x 158 root root 12288 Jul 22 16:19 ..\n", - "-rw-r--r-- 1 root root 53506 Jul 22 16:19 AI-MLFF-forces-mlearn_Si-test-multimae.csv\n", - "-rw-r--r-- 1 root root 1013 Jul 22 16:19 AI-MLFF-energy-mlearn_Si-test-mae.csv\n", - "-rw-r--r-- 1 root root 1185 Jul 22 16:19 AI-MLFF-energy-mlearn_Si-test-mae.csv.zip\n", - "drwxr-xr-x 2 root root 4096 Jul 22 16:19 .\n", - "-rw-r--r-- 1 root root 53688 Jul 22 16:19 AI-MLFF-forces-mlearn_Si-test-multimae.csv.zip\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "en_df = pd.read_csv('AI-MLFF-energy-mlearn_Si-test-mae.csv.zip')" - ], - "metadata": { - "id": "FU4jyfg-Rtjl" - }, - "execution_count": 24, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "en_df" + "f_df = pd.read_csv('AI-MLFF-forces-mlearn_Si-test-multimae.csv.zip')\n", + "target = np.concatenate([np.array(i.split(';'),dtype='float') for i in f_df['target'].values])\n", + "pred= np.concatenate([np.array(i.split(';'),dtype='float') for i in f_df['prediction'].values])\n", + "plt.plot(target,pred,'.')\n", + "plt.xlabel('DFT forces(eV/A)')\n", + "plt.ylabel('FF forces(eV/A)')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 833 + "height": 467 }, "id": "rGyr7zZGRwKB", - "outputId": "2d6c70d3-5240-43b3-f2cc-70c0a2e023ce" - }, - "execution_count": 25, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " id target prediction\n", - "0 Si-215 -297.627739 -328.607544\n", - "1 Si-216 -295.771701 -324.459656\n", - "2 Si-217 -291.289582 -327.277405\n", - "3 Si-218 -296.240885 -322.925293\n", - "4 Si-219 -294.413617 -324.048431\n", - "5 Si-220 -334.752839 -366.294891\n", - "6 Si-221 -334.692151 -366.561707\n", - "7 Si-222 -184.718081 -208.664917\n", - "8 Si-223 -121.411800 -138.180069\n", - "9 Si-224 -338.938997 -366.970734\n", - "10 Si-225 -338.835571 -366.877289\n", - "11 Si-226 -335.689014 -364.188904\n", - "12 Si-227 -333.706496 -362.164948\n", - "13 Si-228 -344.855640 -372.648834\n", - "14 Si-229 -344.811083 -372.539642\n", - "15 Si-230 -298.832226 -334.648071\n", - "16 Si-231 -298.965018 -335.654968\n", - "17 Si-232 -295.209438 -331.134125\n", - "18 Si-233 -291.862939 -328.029633\n", - "19 Si-234 -344.740800 -366.642578\n", - "20 Si-235 -344.740800 -366.642578\n", - "21 Si-236 -344.740800 -366.642578\n", - "22 Si-237 -341.221657 -367.004059\n", - "23 Si-238 -341.221657 -367.004089\n", - "24 Si-239 -341.221657 -367.004059" - ], - "text/html": [ - "\n", - "\n", - "
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idtargetprediction
0Si-215-297.627739-328.607544
1Si-216-295.771701-324.459656
2Si-217-291.289582-327.277405
3Si-218-296.240885-322.925293
4Si-219-294.413617-324.048431
5Si-220-334.752839-366.294891
6Si-221-334.692151-366.561707
7Si-222-184.718081-208.664917
8Si-223-121.411800-138.180069
9Si-224-338.938997-366.970734
10Si-225-338.835571-366.877289
11Si-226-335.689014-364.188904
12Si-227-333.706496-362.164948
13Si-228-344.855640-372.648834
14Si-229-344.811083-372.539642
15Si-230-298.832226-334.648071
16Si-231-298.965018-335.654968
17Si-232-295.209438-331.134125
18Si-233-291.862939-328.029633
19Si-234-344.740800-366.642578
20Si-235-344.740800-366.642578
21Si-236-344.740800-366.642578
22Si-237-341.221657-367.004059
23Si-238-341.221657-367.004089
24Si-239-341.221657-367.004059
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\n", - "
\n" - ] - }, - "metadata": {}, - "execution_count": 25 - } - ] - }, - { - "cell_type": "code", - "source": [ - "from sklearn.metrics import mean_absolute_error\n", - "mean_absolute_error(en_df['target'],en_df['prediction'])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "zJDW6nbNSMyC", - "outputId": "56fef825-beed-43b4-fdc5-53f3432d054a" - }, - "execution_count": 26, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "28.492192422510936" - ] - }, - "metadata": {}, - "execution_count": 26 - } - ] - }, - { - "cell_type": "code", - "source": [ - "actual_en=[]\n", - "pred_en=[]\n", - "actual_forces=[]\n", - "pred_forces=[]\n", - "\n", - "for i, j, k, l in zip(\n", - " test_structures, test_energies, test_forces, train_stresses\n", - "):\n", - " atoms = pmg_to_atoms(i)\n", - " ase_atoms = atoms.ase_converter()\n", - " a = AtomicData.from_ase(ase_atoms,5)\n", - " data = AtomicData.to_AtomicDataDict(a)\n", - " tm = TypeMapper(chemical_symbol_to_type = config['chemical_symbol_to_type'])\n", - " data = tm(data)\n", - " out = model(data)\n", - " pen=out['total_energy'].squeeze().cpu().detach().numpy().tolist()\n", - " print(j,pen)\n", - " num_atoms=atoms.num_atoms\n", - " actual_en.append(j/num_atoms)\n", - " pred_en.append(pen/num_atoms)\n", - " actual_forces.append(np.array(k).flatten())\n", - " pf=out['forces'].squeeze().cpu().detach().numpy()\n", - " pred_forces.append(pf.flatten())\n", - " #break" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "HyOyUFYQLfbr", - "outputId": "ed1188e8-ab6b-4a28-971e-b412ad1eb26d" - }, - "execution_count": 27, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "-297.62773938 -328.6075439453125\n", - "-295.77170067 -324.459716796875\n", - "-291.28958206 -327.2773742675781\n", - "-296.24088456 -322.92529296875\n", - "-294.41361742 -324.0484313964844\n", - "-334.75283939 -366.2948913574219\n", - "-334.69215136 -366.56170654296875\n", - "-184.71808052 -208.6649169921875\n", - "-121.41180043 -138.18006896972656\n", - "-338.93899696 -366.9707336425781\n", - "-338.83557056 -366.8772888183594\n", - "-335.68901422 -364.18890380859375\n", - "-333.7064957 -362.1649475097656\n", - "-344.85564046 -372.6488342285156\n", - "-344.81108268 -372.5396423339844\n", - "-298.83222646 -334.6480712890625\n", - "-298.96501782 -335.65496826171875\n", - "-295.20943762 -331.1341247558594\n", - "-291.86293882 -328.0296325683594\n", - "-344.74080048 -366.642578125\n", - "-344.74080047 -366.642578125\n", - "-344.74080046 -366.642578125\n", - "-341.22165747 -367.0040588378906\n", - "-341.22165734 -367.00408935546875\n", - "-341.22165747 -367.0040588378906\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(actual_en,pred_en,'.')\n", - "plt.xlabel('DFT energy(eV/atom)')\n", - "plt.ylabel('FF energy(eV/atom)')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 468 - }, - "id": "zedmdoAcNmHU", - "outputId": "a45f325b-14f7-4eea-b29c-3a15feac3b99" - }, - "execution_count": 28, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Text(0, 0.5, 'FF energy(eV/atom)')" - ] - }, - "metadata": {}, - "execution_count": 28 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "from sklearn.metrics import mean_absolute_error\n", - "mean_absolute_error(actual_en,pred_en)#energy error MAE per atom" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ZgJemMF5N3Wd", - "outputId": "58bc56de-6f36-446a-807f-7b053e3c1f1c" - }, - "execution_count": 29, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0.47643506561811505" - ] - }, - "metadata": {}, - "execution_count": 29 - } - ] - }, - { - "cell_type": "code", - "source": [ - "actual_forces = np.concatenate(actual_forces)\n", - "pred_forces = np.concatenate(pred_forces)" - ], - "metadata": { - "id": "ms8fXomtOVsP" - }, - "execution_count": 30, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "plt.plot(actual_forces,pred_forces,'.')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - }, - "id": "R8DdStiMOlM0", - "outputId": "7c03a097-2be7-4cf6-9532-357394c67b1c" + "outputId": "e27d6056-b729-48da-899a-af54afb44576" }, - "execution_count": 31, + "execution_count": 11, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "[]" + "Text(0, 0.5, 'FF forces(eV/A)')" ] }, "metadata": {}, - "execution_count": 31 + "execution_count": 11 }, { "output_type": "display_data", @@ -4030,245 +880,20 @@ "text/plain": [ "
" ], - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {} } ] }, - { - "cell_type": "code", - "source": [ - "mean_absolute_error(actual_forces,pred_forces)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NICx2BStO1Az", - "outputId": "4b321790-80e9-4ebe-bd5b-604d2d6cbecf" - }, - "execution_count": 32, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0.16588544067946293" - ] - }, - "metadata": {}, - "execution_count": 32 - } - ] - }, - { - "cell_type": "code", - "source": [ - "out.keys()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yvbS6NaC7U0w", - "outputId": "8467c103-12cf-469b-9e58-5bca5482d7dc" - }, - "execution_count": 33, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "dict_keys(['edge_index', 'pos', 'cell', 'edge_cell_shift', 'pbc', 'atom_types', 'node_attrs', 'node_features', 'edge_vectors', 'edge_lengths', 'edge_embedding', 'edge_attrs', 'edge_features', 'edge_energy', 'atomic_energy', 'batch', 'total_energy', 'forces'])" - ] - }, - "metadata": {}, - "execution_count": 33 - } - ] - }, - { - "cell_type": "code", - "source": [ - "out['total_energy'].squeeze().cpu().detach().numpy().tolist()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "F4QLSATpJmIz", - "outputId": "dd6ca853-476a-4960-b1f7-2773c88a4982" - }, - "execution_count": 34, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "-367.0040588378906" - ] - }, - "metadata": {}, - "execution_count": 34 - } - ] - }, - { - "cell_type": "code", - "source": [ - "out['forces'].squeeze().cpu().detach().numpy() #.tolist()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kFIOTBuOJqWu", - "outputId": "f28fde98-2977-467e-9b35-022ed826660e" - }, - "execution_count": 35, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[-1.61515772e+00, 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"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->pymatgen>=2023.10.11->chgnet) (1.3.0)\n", + "Building wheels for collected packages: nvidia-ml-py3\n", + " Building wheel for nvidia-ml-py3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for nvidia-ml-py3: filename=nvidia_ml_py3-7.352.0-py3-none-any.whl size=19171 sha256=0fd963763136fa823d275c51e2ed30ce5c0dc9427ec6c43c2195b236c21175c2\n", + " Stored in directory: /root/.cache/pip/wheels/5c/d8/c0/46899f8be7a75a2ffd197a23c8797700ea858b9b34819fbf9e\n", + "Successfully built nvidia-ml-py3\n", + "Installing collected packages: nvidia-ml-py3, uncertainties, ruamel.yaml.clib, palettable, numpy, monty, latexcodec, spglib, ruamel.yaml, pybtex, pymatgen, ase, chgnet\n", + " Attempting uninstall: numpy\n", + " Found existing installation: numpy 1.23.5\n", + " Uninstalling numpy-1.23.5:\n", + " Successfully uninstalled numpy-1.23.5\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires kaleido, which is not installed.\n", + "lida 0.0.10 requires python-multipart, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed ase-3.22.1 chgnet-0.3.3 latexcodec-2.0.1 monty-2023.11.3 numpy-1.26.3 nvidia-ml-py3-7.352.0 palettable-3.3.3 pybtex-0.24.0 pymatgen-2023.12.18 ruamel.yaml-0.18.5 ruamel.yaml.clib-0.2.8 spglib-2.2.0 uncertainties-3.1.7\n" + ] + } + ], + "source": [ + "pip install chgnet" + ] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "if not os.path.exists('jarvis_leaderboard'):\n", + " !git clone https://github.com/usnistgov/jarvis_leaderboard.git\n", + "os.chdir('jarvis_leaderboard')\n", + "!pip install -e ." + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LzQ0SavgDB4h", + "outputId": "d9ac9e05-83ab-49c9-b4a0-af406a3d481b" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'jarvis_leaderboard'...\n", + "remote: Enumerating objects: 57856, done.\u001b[K\n", + "remote: Counting objects: 100% (6368/6368), done.\u001b[K\n", + "remote: Compressing objects: 100% (792/792), done.\u001b[K\n", + "remote: Total 57856 (delta 3607), reused 5829 (delta 3298), pack-reused 51488\u001b[K\n", + "Receiving objects: 100% (57856/57856), 381.00 MiB | 33.00 MiB/s, done.\n", + "Resolving deltas: 100% (30573/30573), done.\n", + "Updating files: 100% (3622/3622), done.\n", + "Obtaining file:///content/jarvis_leaderboard\n", + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from jarvis-leaderboard==2023.12.16) (1.26.3)\n", + "Requirement already satisfied: scipy>=1.6.3 in 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/usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools>=2021.07.19->jarvis-leaderboard==2023.12.16) (2.0.7)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools>=2021.07.19->jarvis-leaderboard==2023.12.16) (2023.11.17)\n", + "Building wheels for collected packages: paginate\n", + " Building wheel for paginate (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for paginate: filename=paginate-0.5.6-py3-none-any.whl size=12666 sha256=f9bd9beaa052f5821ba6a4edb0ac161a4f6fdfd08ed77b0f46317f6a3f23ed50\n", + " Stored in directory: /root/.cache/pip/wheels/47/d3/18/0b5bebc873f29bea61fedece1e92cbcbef416839dfe5bd0eef\n", + "Successfully built paginate\n", + "Installing collected packages: paginate, xmltodict, watchdog, typing-extensions, rouge, pyyaml-env-tag, pymdown-extensions, pathspec, mkdocs-material-extensions, mergedeep, colorama, annotated-types, pydantic-core, ghp-import, pydantic, mkdocs, mkdocs-material, jarvis-tools, jarvis-leaderboard\n", + " Attempting uninstall: typing-extensions\n", + " Found existing installation: typing_extensions 4.5.0\n", + " Uninstalling typing_extensions-4.5.0:\n", + " Successfully uninstalled typing_extensions-4.5.0\n", + " Attempting uninstall: pydantic\n", + " Found existing installation: pydantic 1.10.13\n", + " Uninstalling pydantic-1.10.13:\n", + " Successfully uninstalled pydantic-1.10.13\n", + " Running setup.py develop for jarvis-leaderboard\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires kaleido, which is not installed.\n", + "lida 0.0.10 requires python-multipart, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\n", + "llmx 0.0.15a0 requires cohere, which is not installed.\n", + "llmx 0.0.15a0 requires openai, which is not installed.\n", + "llmx 0.0.15a0 requires tiktoken, which is not installed.\n", + "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed annotated-types-0.6.0 colorama-0.4.6 ghp-import-2.1.0 jarvis-leaderboard-2023.12.16 jarvis-tools-2023.12.12 mergedeep-1.3.4 mkdocs-1.5.3 mkdocs-material-9.5.3 mkdocs-material-extensions-1.3.1 paginate-0.5.6 pathspec-0.12.1 pydantic-2.5.3 pydantic-core-2.14.6 pymdown-extensions-10.7 pyyaml-env-tag-0.1 rouge-1.0.1 typing-extensions-4.9.0 watchdog-3.0.0 xmltodict-0.13.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!wget https://figshare.com/ndownloader/files/40357663 -O mlearn.json.zip" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dPvYBqtaDXsG", + "outputId": "09d5956e-8e95-45a0-da16-529a89f187dc" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-01-07 22:35:32-- https://figshare.com/ndownloader/files/40357663\n", + "Resolving figshare.com (figshare.com)... 34.241.157.25, 54.155.30.132, 2a05:d018:1f4:d003:b62a:af42:316b:d9dd, ...\n", + "Connecting to figshare.com (figshare.com)|34.241.157.25|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240107/eu-west-1/s3/aws4_request&X-Amz-Date=20240107T223532Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=d203065f4d0f64ec0e8280cf05785360863531102cae0c2094ed3c64aebb40a5 [following]\n", + "--2024-01-07 22:35:32-- https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240107/eu-west-1/s3/aws4_request&X-Amz-Date=20240107T223532Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=d203065f4d0f64ec0e8280cf05785360863531102cae0c2094ed3c64aebb40a5\n", + "Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.98.131, 52.92.2.8, 52.218.30.67, ...\n", + "Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.98.131|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 2542319 (2.4M) [application/zip]\n", + "Saving to: ‘mlearn.json.zip’\n", + "\n", + "mlearn.json.zip 100%[===================>] 2.42M 3.60MB/s in 0.7s \n", + "\n", + "2024-01-07 22:35:34 (3.60 MB/s) - ‘mlearn.json.zip’ saved [2542319/2542319]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import json,zipfile\n", + "mlearn = json.loads(\n", + " zipfile.ZipFile(\"mlearn.json.zip\").read(\n", + " \"mlearn.json\"\n", + " )\n", + " )" + ], + "metadata": { + "id": "iREgc4gSDaNz" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "from jarvis.core.atoms import Atoms\n", + "os.chdir('/content')\n", + "elements = ['Si']\n", + "for element in elements:\n", + "\n", + " benchmark_energies = (\n", + " \"jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/mlearn_\"\n", + " + element\n", + " + \"_energy.json.zip\"\n", + " )\n", + " temp_energies = benchmark_energies.split(\"/\")[-1].split(\".zip\")[0]\n", + " energies = json.loads(\n", + " zipfile.ZipFile(benchmark_energies).read(temp_energies)\n", + " )\n", + " train_ids = list(energies[\"train\"].keys())\n", + " test_ids = list(energies[\"test\"].keys())\n", + "\n", + " train_energies=[]\n", + " train_forces=[]\n", + " train_stresses=[]\n", + " train_structures=[]\n", + " for i in mlearn:\n", + " if i[\"jid\"] in train_ids:\n", + " # print(i)\n", + " train_energies.append(i[\"energy\"])\n", + " train_forces.append(i[\"forces\"])\n", + " train_stresses.append(i['stresses'])\n", + " atoms = Atoms.from_dict(i[\"atoms\"])\n", + " train_structures.append(atoms.pymatgen_converter())\n", + "\n", + "" + ], + "metadata": { + "id": "K6Smsh6_Dc_o" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "pip install numpy==1.23.5" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "62xDbVEIEHxX", + "outputId": "ba4e21a0-2811-487f-87d3-61d7f0a66dff" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting numpy==1.23.5\n", + " Downloading numpy-1.23.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.1/17.1 MB\u001b[0m \u001b[31m37.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: numpy\n", + " Attempting uninstall: numpy\n", + " Found existing installation: numpy 1.26.3\n", + " Uninstalling numpy-1.26.3:\n", + " Successfully uninstalled numpy-1.26.3\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires kaleido, which is not installed.\n", + "lida 0.0.10 requires python-multipart, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\n", + "pymatgen 2023.12.18 requires numpy>=1.25.0, but you have numpy 1.23.5 which is incompatible.\n", + "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed numpy-1.23.5\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "numpy" + ] + } + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "pip install -U ase" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "C3MQJcUxEX7k", + "outputId": "02850395-de0d-4482-fe6d-e52847c2ea66" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: ase in /usr/local/lib/python3.10/dist-packages (3.22.1)\n", + "Requirement already satisfied: matplotlib>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from ase) (3.7.1)\n", + "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from ase) (1.23.5)\n", + "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from ase) (1.11.4)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.0->ase) (1.2.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.0->ase) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.0->ase) (4.47.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.0->ase) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.0->ase) (23.2)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.0->ase) (9.4.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.0->ase) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.1.0->ase) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.1.0->ase) (1.16.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from chgnet.data.dataset import StructureData, get_train_val_test_loader\n", + "from chgnet.trainer import Trainer\n", + "from chgnet.model.model import CHGNet\n", + "dataset = StructureData(\n", + " structures=train_structures,\n", + " energies=train_energies,\n", + " forces=train_forces,\n", + " stresses=train_stresses,\n", + " #magmoms=list_of_magmoms,\n", + ")\n", + "train_loader, val_loader, test_loader = get_train_val_test_loader(\n", + " dataset, batch_size=32, train_ratio=0.9, val_ratio=0.05\n", + ")\n", + "trainer = Trainer(\n", + " model=CHGNet(),\n", + " targets=\"efsm\",\n", + " optimizer=\"Adam\",\n", + " criterion=\"MSE\",\n", + " learning_rate=1e-2,\n", + " epochs=50,\n", + " use_device=\"cuda\",\n", + ")\n", + "\n", + "trainer.train(train_loader, val_loader, test_loader)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 545 + }, + "id": "B9s5midXDyxY", + "outputId": "7823d813-04e6-480c-be70-36f1ce5f375f" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "error", + "ename": "ModuleNotFoundError", + "evalue": "No module named 'ase.filters'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mchgnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStructureData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_train_val_test_loader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mchgnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTrainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mchgnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCHGNet\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m dataset = StructureData(\n\u001b[1;32m 5\u001b[0m \u001b[0mstructures\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_structures\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chgnet/trainer/__init__.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mannotations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mchgnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTrainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0m__all__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Trainer\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chgnet/trainer/trainer.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 19\u001b[0m )\n\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mchgnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;32mimport\u001b[0m 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"open_url", + "actionText": "Open Examples", + "url": "/notebooks/snippets/importing_libraries.ipynb" + } + ] + } + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "X3GCZaehD40E" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/jarvis-tools-notebooks/monolayer_heterostructure1.ipynb b/jarvis-tools-notebooks/monolayer_heterostructure1.ipynb new file mode 100644 index 0000000..32578ae --- /dev/null +++ b/jarvis-tools-notebooks/monolayer_heterostructure1.ipynb @@ -0,0 +1,849 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyM8dg7wgjvXiSBRwMFIeHLC", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Reference: https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.7.014009" + ], + "metadata": { + "id": "EC6bfloHH8_v" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "renv0hp83Kee", + "outputId": "d3658053-9baf-403f-f4bc-eb3d4ea2c3f5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.1/2.2 MB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.7/2.2 MB\u001b[0m \u001b[31m10.5 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awaiting response... 302 Found\n", + "Location: https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/39756997/monolayer_data.json?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20231116/eu-west-1/s3/aws4_request&X-Amz-Date=20231116T171559Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=a95fed43f1af4052200c24827160d06ae1192a20642c6053c9ce5225d8e82671 [following]\n", + "--2023-11-16 17:15:59-- https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/39756997/monolayer_data.json?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20231116/eu-west-1/s3/aws4_request&X-Amz-Date=20231116T171559Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=a95fed43f1af4052200c24827160d06ae1192a20642c6053c9ce5225d8e82671\n", + "Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.92.17.192, 52.218.92.67, 52.218.96.234, ...\n", + "Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.92.17.192|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1007631 (984K) [application/json]\n", + "Saving to: ‘monolayer_data.json’\n", + "\n", + "monolayer_data.json 100%[===================>] 984.01K 1.31MB/s in 0.7s \n", + "\n", + "2023-11-16 17:16:00 (1.31 MB/s) - ‘monolayer_data.json’ saved [1007631/1007631]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from jarvis.db.jsonutils import loadjson\n", + "\n", + "\n", + "\n", + "jdata = loadjson(\"monolayer_data.json\")" + ], + "metadata": { + "id": "bzq1o_rG3t8Y" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "len(jdata)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zjx1Peek4TMN", + "outputId": "6e58175c-6f51-4327-8097-59fd08f812cc" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1105" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from jarvis.core.atoms import Atoms\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from math import floor\n", + "%matplotlib inline\n", + "\n", + "#Select 2 JARVIS-DFT IDs for monolayers\n", + "jid1='JVASP-664'\n", + "jid2='JVASP-652'\n", + "def band_alignment_diagram(vbms=[], cbms=[], labels=[]):\n", + " x = np.arange(len(vbms)) + 0.5\n", + " emin = floor(min(vbms)) - 1.0\n", + "\n", + " fig = plt.figure(figsize=(10, 8))\n", + "\n", + " plt.rcParams.update({\"font.size\": 22})\n", + " plt.bar(x, np.array(vbms) - emin, bottom=emin, width=0.2)\n", + " plt.bar(x, -np.array(cbms), bottom=cbms, width=0.2)\n", + " plt.xlim(0, len(labels))\n", + " plt.ylim(emin, 0)\n", + " plt.xticks(x, labels)\n", + " # plt.labels(labels, rotation=90)\n", + " plt.axhline(y=-4.5, linestyle=\"-.\")\n", + " plt.axhline(y=-5.73, linestyle=\"-.\")\n", + " plt.text(1, -4, \"${H^+}/{H_2}$\")\n", + " plt.text(1, -6.2, \"${O_2}/{H_2O}$\")\n", + " # plt.title(\"2D: Positions of VBM and CBM\")\n", + " plt.ylabel(\"Energy relative to vacuum [eV]\")\n", + " plt.show()\n", + "\n", + "\n", + "def get_hetero_type(A={}, B={}):\n", + " stack = \"na\"\n", + " int_type = \"na\"\n", + " vbm_a1 = A[\"scf_vbm\"] - A[\"avg_max\"]\n", + " vbm_b1 = B[\"scf_vbm\"] - B[\"avg_max\"]\n", + " cbm_a1 = A[\"scf_cbm\"] - A[\"avg_max\"]\n", + " cbm_b1 = B[\"scf_cbm\"] - B[\"avg_max\"]\n", + " try:\n", + " # if A['phi']>B['phi']:\n", + " if A[\"scf_vbm\"] - A[\"avg_max\"] < B[\"scf_vbm\"] - B[\"avg_max\"]:\n", + " stack = \"BA\"\n", + " print(\"NOT-SWAPPED\")\n", + " else:\n", + " C = A\n", + " D = B\n", + " A = D\n", + " B = C\n", + " stack = \"AB\"\n", + " print(\"SWAPPED\")\n", + " # tmp=B\n", + " # B=A\n", + " # A=tmp\n", + " vbm_a = A[\"scf_vbm\"] - A[\"avg_max\"]\n", + " vbm_b = B[\"scf_vbm\"] - B[\"avg_max\"]\n", + " cbm_a = A[\"scf_cbm\"] - A[\"avg_max\"]\n", + " cbm_b = B[\"scf_cbm\"] - B[\"avg_max\"]\n", + " # print ('vbm_a,vbm_b,cbm_b,cbm_a',vbm_a,vbm_b,cbm_b,cbm_a)\n", + " if vbm_a < vbm_b and vbm_b < cbm_b and cbm_b < cbm_a:\n", + " int_type = \"I\"\n", + " elif vbm_a < vbm_b and vbm_b < cbm_a and cbm_a < cbm_b:\n", + " int_type = \"II\"\n", + " elif vbm_a < cbm_a and cbm_a < vbm_b and vbm_b < cbm_b:\n", + " int_type = \"III\"\n", + " except:\n", + " pass\n", + " return int_type, stack, vbm_a1, vbm_b1, cbm_a1, cbm_b1\n", + "\n", + "\n", + "\n", + "for i in jdata:\n", + " if i[\"jid\"] == jid1:\n", + " strt1 = Atoms.from_dict(i[\"atoms\"])\n", + " ase_strt1 = strt1.ase_converter()\n", + " phiA = i[\"phi\"]\n", + " break\n", + "for i in jdata:\n", + " if i[\"jid\"] == jid2:\n", + " strt2 = Atoms.from_dict(i[\"atoms\"])\n", + " ase_strt2 = strt2.ase_converter()\n", + " phiB = i[\"phi\"]\n", + " break\n", + "if strt1 != None and strt2 != None:\n", + " print('Formula',strt1.composition.reduced_formula,strt2.composition.reduced_formula)\n", + " (\n", + " int_type,\n", + " stack,\n", + " vbm_a,\n", + " vbm_b,\n", + " cbm_a,\n", + " cbm_b,\n", + " ) = get_hetero_type(A=phiA, B=phiB)\n", + " vbms = [vbm_b, vbm_a]\n", + " cbms = [cbm_b, cbm_a]\n", + "\n", + " labels = [\n", + " strt2.composition.reduced_formula,\n", + " strt1.composition.reduced_formula,\n", + " ]\n", + " label2=strt2.composition.reduced_formula+str(\"(\")+str(jid2)+str(\")\")\n", + " label1=strt1.composition.reduced_formula+str(\"(\")+str(jid1)+str(\")\")\n", + " plot_url = band_alignment_diagram(\n", + " vbms=vbms, cbms=cbms, labels=labels\n", + " )\n" + ], + "metadata": { + "id": "6KqiOAYfGiXl", + "outputId": "0da39a80-554f-42ab-a157-f5985e706ea6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 738 + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Formula MoS2 WSe2\n", + "NOT-SWAPPED\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "jdata[0]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3wdzu2oK30sc", + "outputId": "275067b3-725f-44aa-c6f4-c590e90b47e4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'phi': {'nelect': 48,\n", + " 'phi': 4.73414095269429,\n", + " 'scf_vbm': -1.6519,\n", + " 'scf_cbm': -1.6472,\n", + " 'Ef': -1.97822464,\n", + " 'scf_gap': 0.0,\n", + " 'avg_max': 2.7559163126942896,\n", + " 'scf_dir': False},\n", + " 'atoms': {'lattice_mat': [[3.353617811446221, 0.0, 0.0],\n", + " [0.0, 6.273423021773385, 0.0],\n", + " [0.0, 0.0, 33.313518]],\n", + " 'coords': [[0.0, 0.6004615456553349, 0.2002745956304112],\n", + " [0.5, 0.9651745521229684, 0.205389889617873],\n", + " [0.0, 0.8600702878546368, 0.2642586539633663],\n", + " [0.5, 0.3508476059345406, 0.2471701152668883],\n", + " [0.5, 0.7055702442093444, 0.1414059027438078],\n", + " [0.0, 0.2147957642231673, 0.1584938427776558]],\n", + " 'elements': ['Mo', 'Mo', 'Te', 'Te', 'Te', 'Te'],\n", + " 'abc': [3.35362, 6.27342, 33.31352],\n", + " 'angles': [90.0, 90.0, 90.0],\n", + " 'cartesian': False,\n", + " 'props': ['', '', '', '', '', '']},\n", + " 'jid': 'JVASP-677'}" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "jdata[10]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2WRPYjtb3-5Y", + "outputId": "f0b910ba-0ac0-45bf-af4b-941c1775eb31" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'phi': {'nelect': 14,\n", + " 'phi': 4.512538625611761,\n", + " 'scf_vbm': -4.6633,\n", + " 'scf_cbm': -2.7558,\n", + " 'Ef': -3.79276598,\n", + " 'scf_gap': 1.9075,\n", + " 'avg_max': 0.7197726456117611,\n", + " 'scf_dir': False},\n", + " 'atoms': {'lattice_mat': [[4.753244353820853, 0.0, 0.0],\n", + " [-2.3766221769104265, 4.11643028031666, 0.0],\n", + " [0.0, 0.0, 23.870235]],\n", + " 'coords': [[0.3333330000000032, 0.6666669999999968, 0.0614810000000006],\n", + " [0.6666669999999968, 0.3333330000000032, 0.0614810000000006]],\n", + " 'elements': ['Yb', 'Se'],\n", + " 'abc': [4.75324, 4.753243, 23.87024],\n", + " 'angles': [90.0, 90.0, 120.0],\n", + " 'cartesian': False,\n", + " 'props': ['', '']},\n", + " 'jid': 'JVASP-6844'}" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from jarvis.db.figshare import data\n", + "\n", + "dft_2d = data('dft_2d')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SObWnh9R4K55", + "outputId": "ba8288a0-505b-4f86-cd84-3bcbce193dd9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Obtaining 2D dataset 1.1k ...\n", + "Reference:https://www.nature.com/articles/s41524-020-00440-1\n", + "Other versions:https://doi.org/10.6084/m9.figshare.6815705\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 8.39M/8.39M [00:01<00:00, 7.44MiB/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loading the zipfile...\n", + "Loading completed.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "\n", + "df1=pd.DataFrame(dft_2d)\n", + "df2=pd.DataFrame(jdata)" + ], + "metadata": { + "id": "tNNiK7Ng4f9a" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df3=pd.merge(df1,df2,on='jid')" + ], + "metadata": { + "id": "oHhL6zXU4icy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df3[['jid','formula','exfoliation_energy','phi']]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "mh1N09l74wKj", + "outputId": "674e1f01-30d8-44c6-d682-1afc41da93e0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " jid formula exfoliation_energy \\\n", + "0 JVASP-14441 CoO2 66.6 \n", + "1 JVASP-76308 UTe3 na \n", + "2 JVASP-76515 BiI3 na \n", + "3 JVASP-13526 YBr3 77.18 \n", + "4 JVASP-27901 ScHCl na \n", + "... ... ... ... \n", + "1066 JVASP-13608 Nb3GeTe6 94.87 \n", + "1067 JVASP-20029 AuCN 135.73 \n", + "1068 JVASP-28027 Bi4Te3S8 64.69 \n", + "1069 JVASP-28028 NbIO2 35.37 \n", + "1070 JVASP-28046 ScIO 39.21 \n", + "\n", + " phi \n", + "0 {'nelect': 21, 'phi': 8.069307004960171, 'scf_... \n", + "1 {'nelect': 64, 'phi': 4.806983682774846, 'scf_... \n", + "2 {'nelect': 52, 'phi': 5.882297010864416, 'scf_... \n", + "3 {'nelect': 128, 'phi': 6.909346629389352, 'scf... \n", + "4 {'nelect': 38, 'phi': 4.172323923531389, 'scf_... \n", + "... ... \n", + "1066 {'nelect': 166, 'phi': 5.114709300683647, 'scf... \n", + "1067 {'nelect': 20, 'phi': 5.60298582241378, 'scf_v... \n", + "1068 {'nelect': 196, 'phi': 5.260797581934564, 'scf... \n", + "1069 {'nelect': 60, 'phi': 5.572614384079687, 'scf_... \n", + "1070 {'nelect': 96, 'phi': 5.217805301198548, 'scf_... \n", + "\n", + "[1071 rows x 4 columns]" + ], + "text/html": [ + "\n", + "
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jidformulaexfoliation_energyphi
0JVASP-14441CoO266.6{'nelect': 21, 'phi': 8.069307004960171, 'scf_...
1JVASP-76308UTe3na{'nelect': 64, 'phi': 4.806983682774846, 'scf_...
2JVASP-76515BiI3na{'nelect': 52, 'phi': 5.882297010864416, 'scf_...
3JVASP-13526YBr377.18{'nelect': 128, 'phi': 6.909346629389352, 'scf...
4JVASP-27901ScHClna{'nelect': 38, 'phi': 4.172323923531389, 'scf_...
...............
1066JVASP-13608Nb3GeTe694.87{'nelect': 166, 'phi': 5.114709300683647, 'scf...
1067JVASP-20029AuCN135.73{'nelect': 20, 'phi': 5.60298582241378, 'scf_v...
1068JVASP-28027Bi4Te3S864.69{'nelect': 196, 'phi': 5.260797581934564, 'scf...
1069JVASP-28028NbIO235.37{'nelect': 60, 'phi': 5.572614384079687, 'scf_...
1070JVASP-28046ScIO39.21{'nelect': 96, 'phi': 5.217805301198548, 'scf_...
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\n" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.hist(df3[df3['exfoliation_energy']!='na']['exfoliation_energy'])\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 445 + }, + "id": "InMA8riR43w5", + "outputId": "88d26d9a-c259-4210-dfb7-17b655a652f2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "ESG4J1AW5HLD" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/jarvis-tools-notebooks/snap_mlearn.ipynb b/jarvis-tools-notebooks/snap_mlearn.ipynb new file mode 100644 index 0000000..3468b48 --- /dev/null +++ b/jarvis-tools-notebooks/snap_mlearn.ipynb @@ -0,0 +1,1947 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyPQBmC/SkC2mk+0UNwEWAXe", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "os.chdir('/content')\n", + "if not os.path.exists('jarvis_leaderboard'):\n", + " !git clone https://github.com/usnistgov/jarvis_leaderboard.git\n", + "os.chdir('jarvis_leaderboard')\n", + "!pip install -e .\n", + "os.chdir('/content')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "w9uK3-bwqXyv", + "outputId": "8ddf66d1-ea4f-4226-a205-c33ec0b0a75c" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'jarvis_leaderboard'...\n", + "remote: Enumerating objects: 57856, done.\u001b[K\n", + "remote: Counting objects: 100% (6368/6368), done.\u001b[K\n", + "remote: Compressing objects: 100% (792/792), done.\u001b[K\n", + "remote: Total 57856 (delta 3607), reused 5829 (delta 3298), pack-reused 51488\u001b[K\n", + "Receiving objects: 100% (57856/57856), 381.00 MiB | 17.39 MiB/s, done.\n", + "Resolving deltas: 100% (30573/30573), done.\n", + "Updating files: 100% (3622/3622), done.\n", + "Obtaining 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This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires kaleido, which is not installed.\n", + "lida 0.0.10 requires python-multipart, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\n", + "llmx 0.0.15a0 requires cohere, which is not installed.\n", + "llmx 0.0.15a0 requires openai, which is not installed.\n", + "llmx 0.0.15a0 requires tiktoken, which is not installed.\n", + "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed annotated-types-0.6.0 colorama-0.4.6 ghp-import-2.1.0 jarvis-leaderboard-2023.12.16 jarvis-tools-2023.12.12 mergedeep-1.3.4 mkdocs-1.5.3 mkdocs-material-9.5.3 mkdocs-material-extensions-1.3.1 paginate-0.5.6 pathspec-0.12.1 pydantic-2.5.3 pydantic-core-2.14.6 pymdown-extensions-10.7 pyyaml-env-tag-0.1 rouge-1.0.1 spglib-2.2.0 typing-extensions-4.9.0 watchdog-3.0.0 xmltodict-0.13.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "pip install maml numpy==1.23.5" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pHGwIwyUoDJN", + "outputId": "16d79e22-10f8-4135-ee00-7d0ffb5b439f" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting maml\n", + " Downloading maml-2023.9.9-py3-none-any.whl (4.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.3/4.3 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: numpy==1.23.5 in /usr/local/lib/python3.10/dist-packages (1.23.5)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from maml) (1.11.4)\n", + "Collecting monty (from maml)\n", + " Downloading monty-2023.11.3-py3-none-any.whl (42 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.7/42.7 kB\u001b[0m \u001b[31m953.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from maml) (1.2.2)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from maml) (1.5.3)\n", + "Collecting pymatgen (from maml)\n", + " Downloading pymatgen-2023.12.18-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.0 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from maml) (4.66.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->maml) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->maml) (2023.3.post1)\n", + "Requirement already satisfied: matplotlib>=1.5 in /usr/local/lib/python3.10/dist-packages (from pymatgen->maml) (3.7.1)\n", + "Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.10/dist-packages (from pymatgen->maml) (3.2.1)\n", + "INFO: pip is looking at multiple versions of pymatgen to determine which version is compatible with other requirements. 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}, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ews0Li7alSti", + "outputId": "69839305-676a-433c-94ad-e3891ab13812" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\r0% [Working]\r \rGet:1 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease [3,626 B]\n", + "\r0% [Connecting to archive.ubuntu.com (91.189.91.83)] [Connecting to security.ubuntu.com (91.189.91.8\r0% [Connecting to archive.ubuntu.com (91.189.91.83)] [Connecting to security.ubuntu.com (91.189.91.8\r \rHit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "\r \r0% [Connecting to archive.ubuntu.com (91.189.91.83)] [Waiting for headers] [Waiting for headers]\r \rHit:3 https://ppa.launchpadcontent.net/c2d4u.team/c2d4u4.0+/ubuntu jammy InRelease\n", + "\r \r0% [Waiting for headers] [Waiting for headers] [Waiting for headers]\r \rHit:4 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Done\n", + "build-essential is already the newest version (12.9ubuntu3).\n", + "libopenmpi-dev is already the newest version (4.1.2-2ubuntu1).\n", + "openmpi-bin is already the newest version (4.1.2-2ubuntu1).\n", + "openmpi-bin set to manually installed.\n", + "cmake is already the newest version (3.22.1-1ubuntu1.22.04.1).\n", + "git is already the newest version (1:2.34.1-1ubuntu1.10).\n", + "The following additional packages will be installed:\n", + " libhiredis0.14 python3-pip-whl python3-setuptools-whl\n", + "Suggested packages:\n", + " distcc | icecc\n", + "The following NEW packages will be installed:\n", + " ccache libhiredis0.14 python3-pip-whl python3-setuptools-whl python3.10-venv\n", + "0 upgraded, 5 newly installed, 0 to remove and 24 not upgraded.\n", + "Need to get 3,001 kB of archives.\n", + "After this operation, 4,353 kB of additional disk space will be used.\n", + "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libhiredis0.14 amd64 0.14.1-2 [32.8 kB]\n", 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m/content\n", + "Cloning into 'lammps'...\n", + "remote: Enumerating objects: 378653, done.\u001b[K\n", + "remote: Counting objects: 100% (3500/3500), done.\u001b[K\n", + "remote: Compressing objects: 100% (1303/1303), done.\u001b[K\n", + "remote: Total 378653 (delta 2429), reused 3201 (delta 2189), pack-reused 375153\u001b[K\n", + "Receiving objects: 100% (378653/378653), 700.37 MiB | 18.12 MiB/s, done.\n", + "Resolving deltas: 100% (317242/317242), done.\n", + "Updating files: 100% (12984/12984), done.\n", + "/content/lammps\n", + "/content/lammps/build\n", + "-- The CXX compiler identification is GNU 11.4.0\n", + "-- Detecting CXX compiler ABI info\n", + "-- Detecting CXX compiler ABI info - done\n", + "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n", + "-- Detecting CXX compile features\n", + "-- Detecting CXX compile features - done\n", + "-- Found Git: /usr/bin/git (found version \"2.34.1\") \n", + "-- Appending /usr/local/cuda/lib64/stubs to CMAKE_LIBRARY_PATH: /usr/local/cuda/lib64/stubs\n", + "-- Running check for auto-generated files from make-based build system\n", + "-- Found MPI_CXX: /usr/lib/x86_64-linux-gnu/openmpi/lib/libmpi_cxx.so (found version \"3.1\") \n", + "-- Found MPI: TRUE (found version \"3.1\") \n", + "-- Looking for C++ include omp.h\n", + "-- Looking for C++ include omp.h - found\n", + "-- Found OpenMP_CXX: -fopenmp (found version \"4.5\") \n", + "-- Found OpenMP: TRUE (found version \"4.5\") found components: CXX \n", + "-- Found JPEG: /usr/lib/x86_64-linux-gnu/libjpeg.so (found version \"80\") \n", + "-- Found PNG: /usr/lib/x86_64-linux-gnu/libpng.so (found version \"1.6.37\") \n", + "-- Found ZLIB: /usr/lib/x86_64-linux-gnu/libz.so (found version \"1.2.11\") \n", + "-- Found GZIP: /usr/bin/gzip \n", + "-- Found FFMPEG: /usr/bin/ffmpeg \n", + "-- Found Python: /usr/local/bin/python (found version \"3.10.12\") found components: Interpreter \n", + "-- Found Python: /usr/local/bin/python (found version \"3.10.12\") found components: Interpreter Development Development.Module Development.Embed \n", + "-- Found Cythonize: /usr/local/bin/cythonize \n", + "-- Found Python: /usr/include/python3.10 (found version \"3.10.12\") found components: NumPy Interpreter Development.Module \n", + "-- Downloading https://github.com/ICAMS/lammps-user-pace/archive/refs/tags/v.2023.11.25.fix.tar.gz\n", + "\u001b[0mCMake Deprecation Warning at /content/lammps/build/lammps-user-pace-v.2023.11.25.fix/CMakeLists.txt:12 (cmake_policy):\n", + " The OLD behavior for policy CMP0109 will be removed from a future version\n", + " of CMake.\n", + "\n", + " The cmake-policies(7) manual explains that the OLD behaviors of all\n", + " policies are deprecated and that a policy should be set to OLD only under\n", + " specific short-term circumstances. Projects should be ported to the NEW\n", + " behavior and not rely on setting a policy to OLD.\n", + "\n", + "\u001b[0m\n", + "-- Looking for C++ include cmath\n", + "-- Looking for C++ include cmath - found\n", + "-- Generating style headers...\n", + "-- Generating package headers...\n", + "-- Generating lmpinstalledpkgs.h...\n", + "-- Found Python: /usr/local/bin/python (found version \"3.10.12\") found components: Interpreter \n", + "-- Found Python3: /usr/local/bin/python (found version \"3.10.12\") found components: Interpreter \n", + "-- Could NOT find ClangFormat (missing: ClangFormat_EXECUTABLE) (Required is at least version \"8.0\")\n", + "-- The following tools and libraries have been found and configured:\n", + " * Git\n", + " * MPI\n", + " * OpenMP\n", + " * JPEG\n", + " * PNG\n", + " * ZLIB\n", + " * Cythonize\n", + " * Python\n", + " * Python3\n", + "\n", + "-- <<< Build configuration >>>\n", + " LAMMPS Version: 20231121 patch_21Nov2023-452-g1e9eb933be\n", + " Operating System: Linux Ubuntu\" 22.04\n", + " CMake Version: 3.27.9\n", + " Build type: RelWithDebInfo\n", + " Install path: /root/.local\n", + " Generator: Unix Makefiles using /usr/bin/gmake\n", + "-- Enabled packages: ML-IAP;ML-PACE;ML-SNAP;PYTHON;SPIN\n", + "-- <<< Compilers and Flags: >>>\n", + "-- C++ Compiler: /usr/bin/c++\n", + " Type: GNU\n", + " Version: 11.4.0\n", + " C++ Flags: -O2 -g -DNDEBUG\n", + " Defines: LAMMPS_SMALLBIG;LAMMPS_MEMALIGN=64;LAMMPS_OMP_COMPAT=4;LAMMPS_JPEG;LAMMPS_PNG;LAMMPS_GZIP;LAMMPS_FFMPEG;LMP_PYTHON;MLIAP_PYTHON\n", + "-- <<< Linker flags: >>>\n", + "-- Executable name: lmp\n", + "-- Shared library flags: \n", + "-- <<< MPI flags >>>\n", + "-- MPI_defines: MPICH_SKIP_MPICXX;OMPI_SKIP_MPICXX;_MPICC_H\n", + "-- MPI includes: /usr/lib/x86_64-linux-gnu/openmpi/include;/usr/lib/x86_64-linux-gnu/openmpi/include/openmpi\n", + "-- MPI libraries: /usr/lib/x86_64-linux-gnu/openmpi/lib/libmpi_cxx.so;/usr/lib/x86_64-linux-gnu/openmpi/lib/libmpi.so;\n", + "-- Configuring 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/content/lammps/build/cython/mliap_model_python_couple.pyx because it changed.\n", + "[1/1] Cythonizing /content/lammps/build/cython/mliap_model_python_couple.pyx\n", + "Compiling /content/lammps/build/cython/mliap_unified_couple.pyx because it changed.\n", + "[1/1] Cythonizing /content/lammps/build/cython/mliap_unified_couple.pyx\n", + "performance hint: mliap_model_python_couple.pyx:91:5: Exception check on 'MLIAPPY_unload_model' will always require the GIL to be acquired.\n", + "Possible solutions:\n", + "\t1. Declare 'MLIAPPY_unload_model' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n", + "\t2. Use an 'int' return type on 'MLIAPPY_unload_model' to allow an error code to be returned.\n", + "performance hint: mliap_model_python_couple.pyx:103:5: Exception check on 'MLIAPPY_compute_gradients' will always require the GIL to be acquired.\n", + "Possible solutions:\n", + "\t1. Declare 'MLIAPPY_compute_gradients' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n", + "\t2. Use an 'int' return type on 'MLIAPPY_compute_gradients' to allow an error code to be returned.\n", + "performance hint: mliap_unified_couple.pyx:337:5: Exception check on 'compute_gradients_python' will always require the GIL to be acquired.\n", + "Possible solutions:\n", + "\t1. Declare 'compute_gradients_python' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n", + "\t2. Use an 'int' return type on 'compute_gradients_python' to allow an error code to be returned.\n", + "performance hint: mliap_unified_couple.pyx:343:5: Exception check on 'compute_descriptors_python' will always require the GIL to be acquired.\n", + "Possible solutions:\n", + "\t1. Declare 'compute_descriptors_python' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n", + "\t2. Use an 'int' return type on 'compute_descriptors_python' to allow an error code to be returned.\n", + "performance hint: mliap_unified_couple.pyx:349:5: Exception check on 'compute_forces_python' will always require the GIL to be acquired.\n", + "Possible solutions:\n", + "\t1. 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lammps.egg-info/dependency_links.txt\n", + "writing top-level names to lammps.egg-info/top_level.txt\n", + "writing manifest file 'lammps.egg-info/SOURCES.txt'\n", + "reading manifest file 'lammps.egg-info/SOURCES.txt'\n", + "writing manifest file 'lammps.egg-info/SOURCES.txt'\n", + "\u001b[1m* Building wheel...\u001b[0m\n", + "running bdist_wheel\n", + "running build\n", + "running build_py\n", + "creating build\n", + "creating build/lib.linux-x86_64-cpython-310\n", + "creating build/lib.linux-x86_64-cpython-310/lammps\n", + "copying lammps/pylammps.py -> build/lib.linux-x86_64-cpython-310/lammps\n", + "copying lammps/constants.py -> build/lib.linux-x86_64-cpython-310/lammps\n", + "copying lammps/numpy_wrapper.py -> build/lib.linux-x86_64-cpython-310/lammps\n", + "copying lammps/data.py -> build/lib.linux-x86_64-cpython-310/lammps\n", + "copying lammps/core.py -> build/lib.linux-x86_64-cpython-310/lammps\n", + "copying lammps/__init__.py -> build/lib.linux-x86_64-cpython-310/lammps\n", + "copying lammps/formats.py -> build/lib.linux-x86_64-cpython-310/lammps\n", + "creating build/lib.linux-x86_64-cpython-310/lammps/mliap\n", + "copying lammps/mliap/pytorch.py -> build/lib.linux-x86_64-cpython-310/lammps/mliap\n", + "copying lammps/mliap/mliap_unified_lj.py -> build/lib.linux-x86_64-cpython-310/lammps/mliap\n", + "copying lammps/mliap/__init__.py -> build/lib.linux-x86_64-cpython-310/lammps/mliap\n", + "copying lammps/mliap/mliap_unified_abc.py -> build/lib.linux-x86_64-cpython-310/lammps/mliap\n", + "copying lammps/mliap/loader.py -> build/lib.linux-x86_64-cpython-310/lammps/mliap\n", + "copying lammps/liblammps.so -> build/lib.linux-x86_64-cpython-310/lammps\n", + "running build_ext\n", + "installing to build/bdist.linux-x86_64/wheel\n", + "running install\n", + "running install_lib\n", + "creating build/bdist.linux-x86_64\n", + "creating build/bdist.linux-x86_64/wheel\n", + "creating build/bdist.linux-x86_64/wheel/lammps\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/pylammps.py -> build/bdist.linux-x86_64/wheel/lammps\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/constants.py -> build/bdist.linux-x86_64/wheel/lammps\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/numpy_wrapper.py -> build/bdist.linux-x86_64/wheel/lammps\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/data.py -> build/bdist.linux-x86_64/wheel/lammps\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/core.py -> build/bdist.linux-x86_64/wheel/lammps\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/__init__.py -> build/bdist.linux-x86_64/wheel/lammps\n", + "creating build/bdist.linux-x86_64/wheel/lammps/mliap\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/mliap/pytorch.py -> build/bdist.linux-x86_64/wheel/lammps/mliap\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/mliap/mliap_unified_lj.py -> build/bdist.linux-x86_64/wheel/lammps/mliap\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/mliap/__init__.py -> build/bdist.linux-x86_64/wheel/lammps/mliap\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/mliap/mliap_unified_abc.py -> build/bdist.linux-x86_64/wheel/lammps/mliap\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/mliap/loader.py -> build/bdist.linux-x86_64/wheel/lammps/mliap\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/liblammps.so -> build/bdist.linux-x86_64/wheel/lammps\n", + "copying build/lib.linux-x86_64-cpython-310/lammps/formats.py -> build/bdist.linux-x86_64/wheel/lammps\n", + "running install_egg_info\n", + "running egg_info\n", + "writing lammps.egg-info/PKG-INFO\n", + "writing dependency_links to lammps.egg-info/dependency_links.txt\n", + "writing top-level names to lammps.egg-info/top_level.txt\n", + "reading manifest file 'lammps.egg-info/SOURCES.txt'\n", + "writing manifest file 'lammps.egg-info/SOURCES.txt'\n", + "Copying lammps.egg-info to build/bdist.linux-x86_64/wheel/lammps-2023.11.21-py3.10.egg-info\n", + "running install_scripts\n", + "creating build/bdist.linux-x86_64/wheel/lammps-2023.11.21.dist-info/WHEEL\n", + "creating '/content/lammps/build/build-python/.tmp-a2kl1v3l/lammps-2023.11.21-cp310-cp310-linux_x86_64.whl' and adding 'build/bdist.linux-x86_64/wheel' to it\n", + "adding 'lammps/__init__.py'\n", + "adding 'lammps/constants.py'\n", + "adding 'lammps/core.py'\n", + "adding 'lammps/data.py'\n", + "adding 'lammps/formats.py'\n", + "adding 'lammps/liblammps.so'\n", + "adding 'lammps/numpy_wrapper.py'\n", + "adding 'lammps/pylammps.py'\n", + "adding 'lammps/mliap/__init__.py'\n", + "adding 'lammps/mliap/loader.py'\n", + "adding 'lammps/mliap/mliap_unified_abc.py'\n", + "adding 'lammps/mliap/mliap_unified_lj.py'\n", + "adding 'lammps/mliap/pytorch.py'\n", + "adding 'lammps-2023.11.21.dist-info/METADATA'\n", + "adding 'lammps-2023.11.21.dist-info/WHEEL'\n", + "adding 'lammps-2023.11.21.dist-info/top_level.txt'\n", + "adding 'lammps-2023.11.21.dist-info/RECORD'\n", + "removing build/bdist.linux-x86_64/wheel\n", + "\u001b[1m\u001b[92mSuccessfully built \u001b[4mlammps-2023.11.21-cp310-cp310-linux_x86_64.whl\u001b[0m\u001b[1m\u001b[92m\u001b[0m\n", + "Installing wheel into system site-packages folder\n", + "Processing ./lammps-2023.11.21-cp310-cp310-linux_x86_64.whl\n", + "Installing collected packages: lammps\n", + "Successfully installed lammps-2023.11.21\n", + "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n", + "\n", + "Built target install-python\n", + "CPU times: user 7.3 s, sys: 965 ms, total: 8.27 s\n", + "Wall time: 14min 22s\n" + ] + } + ], + "source": [ + "%%time\n", + "# Install LAMMPS with Python interface.\n", + "!apt-get update\n", + "!apt install -y cmake build-essential git ccache openmpi-bin libopenmpi-dev python3.10-venv\n", + "!pip install --upgrade pip\n", + "!pip install numpy torch scipy virtualenv psutil pandas tabulate mpi4py Cython sklearn\n", + "!pip install ase\n", + "# !pip install fitsnap3\n", + "%cd /content\n", + "!rm -rf lammps\n", + "!git clone https://github.com/lammps/lammps.git lammps\n", + "%cd /content/lammps\n", + "!rm -rf build\n", + "!mkdir build\n", + "%cd build\n", + "!cmake ../cmake -DLAMMPS_EXCEPTIONS=yes \\\n", + " -DBUILD_SHARED_LIBS=yes \\\n", + " -DMLIAP_ENABLE_PYTHON=yes \\\n", + " -DPKG_PYTHON=yes \\\n", + " -DPKG_ML-SNAP=yes \\\n", + " -DPKG_ML-IAP=yes \\\n", + " -DPKG_ML-PACE=yes \\\n", + " -DPKG_SPIN=yes \\\n", + " -DPYTHON_EXECUTABLE:FILEPATH=`which python`\n", + "!make -j 2\n", + "!make install-python\n", + "\n", + "\n", + "# # Install FitSNAP.\n", + "\n", + "# %cd /content\n", + "# !rm -rf FitSNAP\n", + "# !git clone https://github.com/FitSNAP/FitSNAP\n", + "# #!git clone -b hackathon https://github.com/rohskopf/FitSNAP\n", + "\n", + "# # Set environment variables.\n", + "\n", + "# !$PYTHONPATH\n", + "# %env PYTHONPATH=/env/python:/bin/bash:\n", + "# %env LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/content/lammps/build\n", + "\n", + "# # Move into FitSNAP directory\n", + "# %cd FitSNAP" + ] + }, + { + "cell_type": "code", + "source": [ + "!cp /content/lammps/build/lmp /content/lammps/build/lmp_serial\n", + "!chmod 755 /content/lammps/build/lmp_serial\n" + ], + "metadata": { + "id": "2w6KHCjszsv9" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "path = %env PATH\n", + "%env PATH=/content/lammps/build/:$path\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uG763i6K7Wyp", + "outputId": "7f1bd436-d5d8-47da-fb16-b2507cd62e99" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "env: PATH=/content/lammps/build/:/opt/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!which lmp_serial" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9E8bYr3Y5ZWm", + "outputId": "87cd5120-87ca-4e8b-9f7e-fe3fd2d44322" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/lammps/build//lmp_serial\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import maml" + ], + "metadata": { + "id": "hB5hOGg5ng6O" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from maml.utils import pool_from, convert_docs\n", + "\n" + ], + "metadata": { + "id": "QkgcEQYxljC8" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "os.chdir('/content')\n", + "!wget https://figshare.com/ndownloader/files/40357663 -O mlearn.json.zip" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CmSrW1gts9Nv", + "outputId": "c559d57c-7d0e-46d5-8cbc-6d3610048143" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-01-08 22:05:54-- https://figshare.com/ndownloader/files/40357663\n", + "Resolving figshare.com (figshare.com)... 54.154.80.150, 54.247.128.131, 2a05:d018:1f4:d000:90e1:2601:4ef8:5a18, ...\n", + "Connecting to figshare.com (figshare.com)|54.154.80.150|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240108/eu-west-1/s3/aws4_request&X-Amz-Date=20240108T220554Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=42603698cef570fe835fe72e58597b740759c1021b3924f1ad5869d0b95c5897 [following]\n", + "--2024-01-08 22:05:54-- https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240108/eu-west-1/s3/aws4_request&X-Amz-Date=20240108T220554Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=42603698cef570fe835fe72e58597b740759c1021b3924f1ad5869d0b95c5897\n", + "Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.28.99, 52.218.28.179, 52.218.106.147, ...\n", + "Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.28.99|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 2542319 (2.4M) [application/zip]\n", + "Saving to: ‘mlearn.json.zip’\n", + "\n", + "mlearn.json.zip 100%[===================>] 2.42M --.-KB/s in 0.1s \n", + "\n", + "2024-01-08 22:05:54 (17.3 MB/s) - ‘mlearn.json.zip’ saved [2542319/2542319]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "import os,json,torch\n", + "from jarvis.core.atoms import Atoms\n", + "from jarvis.db.jsonutils import loadjson, dumpjson\n", + "import json,zipfile\n", + "import zipfile\n", + "import json\n", + "import glob\n", + "import pandas as pd\n", + "import numpy as np\n", + "from jarvis.core.atoms import Atoms\n", + "import os\n", + "import torch\n", + "from jarvis.db.figshare import data\n", + "import subprocess\n", + "from subprocess import Popen, PIPE\n", + "from maml.base import SKLModel\n", + "from maml.describers import BispectrumCoefficients\n", + "from sklearn.linear_model import LinearRegression\n", + "from maml.apps.pes import SNAPotential\n", + "\n", + "mlearn = json.loads(\n", + " zipfile.ZipFile(\"/content/mlearn.json.zip\").read(\n", + " \"mlearn.json\"\n", + " )\n", + " )\n", + "\n", + "run_dir='/content'\n", + "elements = [\"Si\"] #,\"Ni\",\"Ge\",\"Mo\",\"Li\"]\n", + "mem = []\n", + "for element in elements:\n", + " os.chdir(run_dir)\n", + " dir_name = \"maml_snap_\" + element\n", + " cmd='rm -rf '+dir_name\n", + " os.system(cmd)\n", + " if not os.path.exists(dir_name):\n", + " os.makedirs(dir_name)\n", + " benchmark_energies = (\n", + " \"jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/mlearn_\"\n", + " + element\n", + " + \"_energy.json.zip\"\n", + " )\n", + "\n", + " temp_energies = benchmark_energies.split(\"/\")[-1].split(\".zip\")[0]\n", + " energies = json.loads(\n", + " zipfile.ZipFile(benchmark_energies).read(temp_energies)\n", + " )\n", + " train_ids = list(energies[\"train\"].keys())\n", + " test_ids = list(energies[\"test\"].keys())\n", + " train_energies = []\n", + " train_forces = []\n", + " train_stresses = []\n", + " train_structures = []\n", + " for i in mlearn:\n", + " if i[\"jid\"] in train_ids:\n", + " # print(i)\n", + " train_energies.append(i[\"energy\"])\n", + " train_forces.append(i[\"forces\"])\n", + " train_stresses.append(i[\"stresses\"])\n", + " atoms = Atoms.from_dict(i[\"atoms\"])\n", + " train_structures.append(atoms.pymatgen_converter())\n", + " # Val same as test\n", + " test_energies = []\n", + " test_forces = []\n", + " test_stresses = []\n", + " test_structures = []\n", + " tids = []\n", + " for i in mlearn:\n", + " if i[\"jid\"] in test_ids:\n", + " # print(i)\n", + " test_energies.append(i[\"energy\"])\n", + " test_forces.append(i[\"forces\"])\n", + " test_stresses.append(i[\"stresses\"])\n", + " atoms = Atoms.from_dict(i[\"atoms\"])\n", + " test_structures.append(atoms.pymatgen_converter())\n", + " tids.append(i['jid'])\n", + " train_pool = pool_from(train_structures, train_energies, train_forces)\n", + "\n", + " _, df = convert_docs(train_pool)\n", + "\n", + " weights = np.ones(len(df['dtype']), )\n", + "\n", + " weights[df['dtype'] == 'energy'] = 100\n", + "\n", + " element_profile = {element: {'r': 5.0, 'w': 1}}\n", + " describer = BispectrumCoefficients(rcutfac=0.5, twojmax=6, element_profile=element_profile,\n", + " quadratic=False, pot_fit=True)\n", + " model = SKLModel(describer=describer, model=LinearRegression())\n", + " snap = SNAPotential(model=model)\n", + " snap.train(train_structures, train_energies, train_forces, sample_weight=weights)\n", + " ########################################\n", + " def get_snap_forces(snap_model='',test_structure=''):\n", + " test_energy=-9999\n", + " test_force=np.zeros((atoms.num_atoms,3))\n", + " df_orig, df_predict = snap_model.evaluate(test_structures=[test_structure],\n", + " test_energies=[test_energy],\n", + " test_forces=[test_force], test_stresses=None\n", + " )\n", + " return df_predict[df_predict['dtype']=='energy']['y_orig'].values[0],df_predict[df_predict['dtype']=='force']['y_orig'].values,0\n", + "\n", + " # df = pd.DataFrame(mdata)\n", + " df = pd.DataFrame(\n", + " json.loads(\n", + " zipfile.ZipFile(\"mlearn.json.zip\").read(\n", + " \"mlearn.json\"\n", + " )\n", + " )\n", + " )\n", + " print(df)\n", + " #for i in glob.glob(\"../../benchmarks/AI/MLFF/*energy*.zip\"):\n", + " for i in glob.glob(\"/content/jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/*energy*.zip\"):\n", + " if \"mlearn\" in i and element in i:\n", + " fname_e = (\n", + " \"AI-MLFF-energy-\"\n", + " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " + \"-test-mae.csv\"\n", + " )\n", + " fname_f = (\n", + " \"AI-MLFF-forces-\"\n", + " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " + \"-test-multimae.csv\"\n", + " )\n", + " # fname_s = (\n", + " # \"AI-MLFF-stresses-\"\n", + " # + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", + " # + \"-test-multimae.csv\"\n", + " # )\n", + " f_e = open(fname_e, \"w\")\n", + " f_f = open(fname_f, \"w\")\n", + " # f_s = open(fname_s, \"w\")\n", + "\n", + " f_e.write(\"id,prediction\\n\")\n", + " f_f.write(\"id,prediction\\n\")\n", + " # f_s.write(\"id,prediction\\n\")\n", + "\n", + " print(i)\n", + " dat = json.loads(\n", + " zipfile.ZipFile(i).read(i.split(\"/\")[-1].split(\".zip\")[0])\n", + " )\n", + " print(dat[\"test\"])\n", + " for key, val in dat[\"test\"].items():\n", + " entry = df[df[\"jid\"] == key]\n", + " atoms = Atoms.from_dict(entry.atoms.values[0])\n", + " # print(key,val,df[df['jid']==key],atoms)\n", + " # energy,forces=get_alignn_forces(atoms)\n", + " energy, forces, stress = get_snap_forces(snap_model=snap,test_structure=atoms.pymatgen_converter())\n", + " print(key, val, energy, atoms.num_atoms)\n", + " line = key + \",\" + str(energy) + \"\\n\"\n", + " f_e.write(line)\n", + " line = (\n", + " key\n", + " + \",\"\n", + " + str(\";\".join(map(str, np.array(forces).flatten())))\n", + " + \"\\n\"\n", + " )\n", + " f_f.write(line)\n", + " # line = (\n", + " # key\n", + " # + \",\"\n", + " # + str(\";\".join(map(str, np.array(stress).flatten())))\n", + " # + \"\\n\"\n", + " # )\n", + " # f_s.write(line)\n", + " f_e.close()\n", + " f_f.close()\n", + " # f_s.close()\n", + " zname = fname_e + \".zip\"\n", + " with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " myzip.write(fname_e)\n", + "\n", + " zname = fname_f + \".zip\"\n", + " with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " myzip.write(fname_f)\n", + "\n", + " # zname = fname_s + \".zip\"\n", + " # with zipfile.ZipFile(zname, \"w\") as myzip:\n", + " # myzip.write(fname_s)\n", + "\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "O4U9GGbgmviP", + "outputId": "fbe291e1-a24b-4d9b-a7b3-f3682941349a" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "INFO:maml.apps.pes._lammps:Setting Lammps executable to /content/lammps/build/lmp_serial\n", + "WARNING:maml.apps.pes._snap:Triclinic structures will be rotated to lammps format. Please be sure to rotate forces and stresses to get the correct mapping for ensuring correct mapping. You may use`maml.utils.check_structures_forces_stresses` to do the correct rotations.\n", + "INFO:maml.utils._lammps:Structure index 0 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 1 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 2 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 3 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 4 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 5 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 6 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 7 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 8 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 9 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 10 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 11 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 12 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 13 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 14 is 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rotated.\n", + "INFO:maml.utils._lammps:Structure index 47 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 48 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 49 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 50 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 51 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 52 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 53 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 54 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 55 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 56 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 57 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 58 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 59 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 60 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 61 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 65 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 67 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 68 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 186 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 187 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 188 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 189 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 190 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 191 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 192 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 193 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 194 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 195 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 196 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 197 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 198 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 199 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 200 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 201 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 202 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 203 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 204 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 205 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 206 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 207 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 208 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 209 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 210 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 211 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 212 is rotated.\n", + "INFO:maml.utils._lammps:Structure index 0 is rotated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " jid atoms energy \\\n", + "0 Ni-1 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -604.262250 \n", + "1 Ni-2 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -603.933146 \n", + "2 Ni-3 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -603.735818 \n", + "3 Ni-4 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -604.967429 \n", + "4 Ni-5 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -602.935586 \n", + "... ... ... ... \n", + "1561 Li-266 {'lattice_mat': [[4.8442, 0.0, 0.0], [2.4221, ... -21.410502 \n", + "1562 Li-267 {'lattice_mat': [[3.429887, 0.0, 0.0], [-1.714... -17.848979 \n", + "1563 Li-268 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797287 \n", + "1564 Li-269 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797589 \n", + "1565 Li-270 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797713 \n", + "\n", + " forces \\\n", + "0 [[-0.1928178, -1.87931786, -0.66374007], [-0.0... \n", + "1 [[-0.71128299, -1.10528691, -2.20081632], [0.9... \n", + "2 [[-1.04325671, 0.32574515, 0.30692968], [-0.75... \n", + "3 [[-1.136544, 1.56868294, -1.09210495], [-1.274... \n", + "4 [[3.11483946, -0.55002862, 0.11103391], [1.460... \n", + "... ... \n", + "1561 [[-0.0, -0.0, -0.00808912], [-0.0, -0.0, -0.01... \n", + "1562 [[0.0, 0.00387929, 0.00568886], [0.0, 0.002831... \n", + "1563 [[-0.0, 0.0, 0.0], [0.0, -0.0, -0.0]] \n", + "1564 [[0.0, -0.0, -0.0], [-0.0, 0.0, 0.0]] \n", + "1565 [[0.0, 0.0, 0.0], [0.0, -0.0, -0.0]] \n", + "\n", + " stresses \n", + "0 [41.40636025, 41.1876322, 51.06529127, 1.04252... \n", + "1 [44.88288149, 44.70823804, 44.73856806, 1.6211... \n", + "2 [52.07802628, 45.27925996, 47.31874643, -4.461... \n", + "3 [38.03742565, 44.72767184, 39.73179484, -5.093... \n", + "4 [47.1482229, 51.19066271, 46.54196562, 4.22313... \n", + "... ... \n", + "1561 [3.17420482, 3.62499364, 2.54337296, -0.926102... \n", + "1562 [2.38724801, 3.61190439, 2.1257323, 0.83494626... \n", + "1563 [3.65117155, 4.28869211, 3.70795776, -0.817898... \n", + "1564 [2.79230607, 2.46389956, 3.81491553, 1.9486931... \n", + "1565 [2.74849324, 3.62957339, 3.05891903, -2.404687... \n", + "\n", + "[1566 rows x 5 columns]\n", + "/content/jarvis_leaderboard/jarvis_leaderboard/benchmarks/AI/MLFF/mlearn_Si_energy.json.zip\n", + "{'Si-215': -297.62773938, 'Si-216': -295.77170067, 'Si-217': -291.28958206, 'Si-218': -296.24088456, 'Si-219': -294.41361742, 'Si-220': -334.75283939, 'Si-221': -334.69215136, 'Si-222': -184.71808052, 'Si-223': -121.41180043, 'Si-224': -338.93899696, 'Si-225': -338.83557056, 'Si-226': -335.68901422, 'Si-227': -333.7064957, 'Si-228': -344.85564046, 'Si-229': -344.81108268, 'Si-230': -298.83222646, 'Si-231': -298.96501782, 'Si-232': -295.20943762, 'Si-233': -291.86293882, 'Si-234': -344.74080048, 'Si-235': -344.74080047, 'Si-236': -344.74080046, 'Si-237': -341.22165747, 'Si-238': -341.22165734, 'Si-239': -341.22165747}\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "INFO:maml.utils._lammps:Structure index 0 is rotated.\n" + ] + 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] +} \ No newline at end of file