diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f4a298b..8faef69 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,7 +11,7 @@ repos: - repo: https://github.com/psf/black rev: 22.3.0 hooks: - - id: black + - id: black-jupyter - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.1.0 @@ -46,3 +46,8 @@ repos: rev: v0.960 hooks: - id: mypy + + - repo: https://github.com/nbQA-dev/nbQA + rev: 1.3.1 + hooks: + - id: nbqa-isort diff --git a/CHANGES.md b/CHANGES.md index 8d3813b..edae5ea 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,12 +1,16 @@ -# v0.0.4 -- Fix model download +# Changelog + +## v0.0.4 + +- Fix model download - More documentation - Bug fixes -# v0.0.2 +## v0.0.2 + - Fix edge cases for triple bonds - Enable model weights download within code -# v0.0.1 +## v0.0.1 -- Initial release. \ No newline at end of file +- Initial release. diff --git a/README.md b/README.md index 76b6765..842ecc4 100644 --- a/README.md +++ b/README.md @@ -5,38 +5,40 @@ # M3GNet -M3GNet is a new materials graph neural network architecture that incorporates 3-body interactions. A key difference +M3GNet is a new materials graph neural network architecture that incorporates 3-body interactions. A key difference with prior materials graph implementations such as [MEGNet](https://github.com/materialsvirtuallab/megnet) is the addition of the coordinates for atoms and the 3×3 lattice matrix in crystals, which are necessary for obtaining -tensorial quantities such as forces and stresses via auto-differentiation. +tensorial quantities such as forces and stresses via auto-differentiation. As a framework, M3GNet has diverse applications, including: -- **Interatomic potential development.** With the same training data, M3GNet performs similarly to state-of-the-art + +- **Interatomic potential development.** With the same training data, M3GNet performs similarly to state-of-the-art machine learning interatomic potentials (ML-IAPs). However, a key feature of a graph representation is its flexibility to scale to diverse chemical spaces. One of the key accomplishments of M3GNet is the development of a *universal IAP* that can work across the entire periodic table of the elements by training on relaxations performed in the [Materials Project](http://materialsproject.org). - **Surrogate models for property predictions.** Like the previous MEGNet architecture, M3GNet can be used to develop - surrogate models for property predictions, achieving in many cases accuracies that better or similar to other + surrogate models for property predictions, achieving in many cases accuracies that better or similar to other state-of-the-art ML models. -For detailed performance benchmarks, please refer to the publication in the [References](#references) section. The +For detailed performance benchmarks, please refer to the publication in the [References](#references) section. The API documentation is available via the [Github Page](http://materialsvirtuallab.github.io/m3gnet). # Table of Contents -* [System requirements](#system-requirements) -* [Installation](#installation) -* [Change Log](#change-log) -* [Usage](#usage) -* [Model training](#model-training) -* [Datasets](#datasets) -* [References](#references) + +- [System requirements](#system-requirements) +- [Installation](#installation) +- [Change Log](#change-log) +- [Usage](#usage) +- [Model training](#model-training) +- [Datasets](#datasets) +- [References](#references) # System requirements -Inferences using the pre-trained models can be ran on any standard computer. For model training, the GPU memory needs -to be > 18 Gb for a batch size of 32 using the crystal training data. In our work, we used a single RTX 3090 -GPU for model training. +Inferences using the pre-trained models can be ran on any standard computer. For model training, the GPU memory needs +to be > 18 Gb for a batch size of 32 using the crystal training data. In our work, we used a single RTX 3090 +GPU for model training. # Installation @@ -56,28 +58,38 @@ the installation of tensorflow and other dependencies. Here are the recommended 1. Ensure that you already have XCode and CLI installed. 2. Install Miniconda or Anaconda. 3. Create a Python 3.9 environment. -```bash -conda create --name m3gnet python=3.9 -conda activate m3gnet -``` + + ```bash + conda create --name m3gnet python=3.9 + conda activate m3gnet + ``` + 4. First install tensorflow and its dependencies for Apple Silicon. -```bash -conda install -c apple tensorflow-deps -pip install tensorflow-macos -``` -5. If you wish, you can install `tensorflow-metal`, which helps speed up training. If you encounter strange tensorflow + + ```bash + conda install -c apple tensorflow-deps + pip install tensorflow-macos + ``` + +5. If you wish, you can install `tensorflow-metal`, which helps speed up training. If you encounter strange tensorflow errors, you should uninstall `tensorflow-metal` and see if it fixes the errors first. -``` -pip install tensorflow-metal -``` + + ```bash + pip install tensorflow-metal + ``` + 6. Install m3gnet but ignore dependencies (otherwise, pip will look for tensorflow). -```bash -pip install --no-deps m3gnet -``` + + ```bash + pip install --no-deps m3gnet + ``` + 7. Install other dependencies like pymatgen, etc. manually. -```bash -pip install protobuf==3.20.0 pymatgen ase cython -``` + + ```bash + pip install protobuf==3.20.0 pymatgen ase cython + ``` + 8. Once you are done, you can try running `pytest m3gnet` to see if all tests pass. # Change Log @@ -109,117 +121,119 @@ print(f"Relaxed lattice parameter is {final_structure.lattice.abc[0]: .3f} Å") print(f"Final energy is {final_energy.item(): .3f} eV/atom") ``` -The output is as follows: +The output is as follows: + ``` Relaxed lattice parameter is 3.169 Å Final energy is -10.859 eV/atom ``` + The initial lattice parameter of 3.3 Å was successfully relaxed to 3.169 Å, close to the DFT value of 3.168 Å. The final energy -10.859 eV/atom is also close to Materials Project DFT value of [-10.8456 eV/atom](http://materialsproject.org/materials/mp-129/). The relaxation takes less than 20 seconds on a single laptop. -The table below provides more comprehensive benchmarks for cubic crystals based on -[exp data on Wikipedia](http://en.wikipedia.org/wiki/Lattice_constant) and MP DFT data. The +The table below provides more comprehensive benchmarks for cubic crystals based on +[exp data on Wikipedia](http://en.wikipedia.org/wiki/Lattice_constant) and MP DFT data. The [Jupyter notebook](/examples/Cubic%20Crystal%20Test.ipynb) is in the [examples](/examples) folder. This benchmark is limited to cubic crystals for ease of comparison since there is only one lattice parameter. However, M3GNet is not limited to cubic systems of course (see [LiFePO4 example](/examples/Relaxation%20of%20LiFePO4.ipynb)). -| Material | Crystal structure | a | MP a | Predicted a | % error vs Expt | % error vs MP | -|:------------|:--------------------|--------:|--------:|--------------:|:------------------|:--------------| -| Ac | FCC | 5.31 | 5.66226 | 5.6646 | 6.68% | 0.04% | -| Ag | FCC | 4.079 | 4.16055 | 4.16702 | 2.16% | 0.16% | -| Al | FCC | 4.046 | 4.03893 | 4.04108 | -0.12% | 0.05% | -| AlAs | Zinc blende (FCC) | 5.6605 | 5.73376 | 5.73027 | 1.23% | -0.06% | -| AlP | Zinc blende (FCC) | 5.451 | 5.50711 | 5.50346 | 0.96% | -0.07% | -| AlSb | Zinc blende (FCC) | 6.1355 | 6.23376 | 6.22817 | 1.51% | -0.09% | -| Ar | FCC | 5.26 | 5.64077 | 5.62745 | 6.99% | -0.24% | -| Au | FCC | 4.065 | 4.17129 | 4.17431 | 2.69% | 0.07% | -| BN | Zinc blende (FCC) | 3.615 | 3.626 | 3.62485 | 0.27% | -0.03% | -| BP | Zinc blende (FCC) | 4.538 | 4.54682 | 4.54711 | 0.20% | 0.01% | -| Ba | BCC | 5.02 | 5.0303 | 5.03454 | 0.29% | 0.08% | -| C (diamond) | Diamond (FCC) | 3.567 | 3.57371 | 3.5718 | 0.13% | -0.05% | -| Ca | FCC | 5.58 | 5.50737 | 5.52597 | -0.97% | 0.34% | -| CaVO3 | Cubic perovskite | 3.767 | 3.83041 | 3.83451 | 1.79% | 0.11% | -| CdS | Zinc blende (FCC) | 5.832 | 5.94083 | 5.9419 | 1.88% | 0.02% | -| CdSe | Zinc blende (FCC) | 6.05 | 6.21283 | 6.20987 | 2.64% | -0.05% | -| CdTe | Zinc blende (FCC) | 6.482 | 6.62905 | 6.62619 | 2.22% | -0.04% | -| Ce | FCC | 5.16 | 4.72044 | 4.71921 | -8.54% | -0.03% | -| Cr | BCC | 2.88 | 2.87403 | 2.84993 | -1.04% | -0.84% | -| CrN | Halite | 4.149 | - | 4.16068 | 0.28% | - | -| Cs | BCC | 6.05 | 6.11004 | 5.27123 | -12.87% | -13.73% | -| CsCl | Caesium chloride | 4.123 | 4.20906 | 4.20308 | 1.94% | -0.14% | -| CsF | Halite | 6.02 | 6.11801 | 6.1265 | 1.77% | 0.14% | -| CsI | Caesium chloride | 4.567 | 4.66521 | 4.90767 | 7.46% | 5.20% | -| Cu | FCC | 3.597 | 3.62126 | 3.61199 | 0.42% | -0.26% | -| Eu | BCC | 4.61 | 4.63903 | 4.34783 | -5.69% | -6.28% | -| EuTiO3 | Cubic perovskite | 7.81 | 3.96119 | 3.92943 | -49.69% | -0.80% | -| Fe | BCC | 2.856 | 2.84005 | 2.85237 | -0.13% | 0.43% | -| GaAs | Zinc blende (FCC) | 5.653 | 5.75018 | 5.75055 | 1.73% | 0.01% | -| GaP | Zinc blende (FCC) | 5.4505 | 5.5063 | 5.5054 | 1.01% | -0.02% | -| GaSb | Zinc blende (FCC) | 6.0959 | 6.21906 | 6.21939 | 2.03% | 0.01% | -| Ge | Diamond (FCC) | 5.658 | 5.76286 | 5.7698 | 1.98% | 0.12% | -| HfC0.99 | Halite | 4.64 | 4.65131 | 4.65023 | 0.22% | -0.02% | -| HfN | Halite | 4.392 | 4.53774 | 4.53838 | 3.33% | 0.01% | -| InAs | Zinc blende (FCC) | 6.0583 | 6.18148 | 6.25374 | 3.23% | 1.17% | -| InP | Zinc blende (FCC) | 5.869 | 5.95673 | 5.9679 | 1.69% | 0.19% | -| InSb | Zinc blende (FCC) | 6.479 | 6.63322 | 6.63863 | 2.46% | 0.08% | -| Ir | FCC | 3.84 | 3.87573 | 3.87716 | 0.97% | 0.04% | -| K | BCC | 5.23 | 5.26212 | 5.4993 | 5.15% | 4.51% | -| KBr | Halite | 6.6 | 6.70308 | 6.70797 | 1.64% | 0.07% | -| KCl | Halite | 6.29 | 6.38359 | 6.39634 | 1.69% | 0.20% | -| KF | Halite | 5.34 | 5.42398 | 5.41971 | 1.49% | -0.08% | -| KI | Halite | 7.07 | 7.18534 | 7.18309 | 1.60% | -0.03% | -| KTaO3 | Cubic perovskite | 3.9885 | 4.03084 | 4.03265 | 1.11% | 0.05% | -| Kr | FCC | 5.72 | 6.49646 | 6.25924 | 9.43% | -3.65% | -| Li | BCC | 3.49 | 3.42682 | 3.41891 | -2.04% | -0.23% | -| LiBr | Halite | 5.5 | 5.51343 | 5.51076 | 0.20% | -0.05% | -| LiCl | Halite | 5.14 | 5.15275 | 5.14745 | 0.15% | -0.10% | -| LiF | Halite | 4.03 | 4.08343 | 4.08531 | 1.37% | 0.05% | -| LiI | Halite | 6.01 | 6.0257 | 6.02709 | 0.28% | 0.02% | -| MgO | Halite (FCC) | 4.212 | 4.25648 | 4.2567 | 1.06% | 0.01% | -| Mo | BCC | 3.142 | 3.16762 | 3.16937 | 0.87% | 0.06% | -| Na | BCC | 4.23 | 4.17262 | 4.19684 | -0.78% | 0.58% | -| NaBr | Halite | 5.97 | 6.0276 | 6.01922 | 0.82% | -0.14% | -| NaCl | Halite | 5.64 | 5.69169 | 5.69497 | 0.97% | 0.06% | -| NaF | Halite | 4.63 | 4.69625 | 4.69553 | 1.42% | -0.02% | -| NaI | Halite | 6.47 | 6.532 | 6.52739 | 0.89% | -0.07% | -| Nb | BCC | 3.3008 | 3.32052 | 3.32221 | 0.65% | 0.05% | -| NbN | Halite | 4.392 | 4.45247 | 4.45474 | 1.43% | 0.05% | -| Ne | FCC | 4.43 | 4.30383 | 6.95744 | 57.05% | 61.66% | -| Ni | FCC | 3.499 | 3.5058 | 3.5086 | 0.27% | 0.08% | -| Pb | FCC | 4.92 | 5.05053 | 5.02849 | 2.21% | -0.44% | -| PbS | Halite (FCC) | 5.9362 | 6.00645 | 6.01752 | 1.37% | 0.18% | -| PbTe | Halite (FCC) | 6.462 | 6.56567 | 6.56111 | 1.53% | -0.07% | -| Pd | FCC | 3.859 | 3.95707 | 3.95466 | 2.48% | -0.06% | -| Pt | FCC | 3.912 | 3.97677 | 3.97714 | 1.67% | 0.01% | -| Rb | BCC | 5.59 | 5.64416 | 5.63235 | 0.76% | -0.21% | -| RbBr | Halite | 6.89 | 7.02793 | 6.98219 | 1.34% | -0.65% | -| RbCl | Halite | 6.59 | 6.69873 | 6.67994 | 1.36% | -0.28% | -| RbF | Halite | 5.65 | 5.73892 | 5.76843 | 2.10% | 0.51% | -| RbI | Halite | 7.35 | 7.48785 | 7.61756 | 3.64% | 1.73% | -| Rh | FCC | 3.8 | 3.8439 | 3.84935 | 1.30% | 0.14% | -| ScN | Halite | 4.52 | 4.51831 | 4.51797 | -0.04% | -0.01% | -| Si | Diamond (FCC) | 5.43102 | 5.46873 | 5.45002 | 0.35% | -0.34% | -| Sr | FCC | 6.08 | 6.02253 | 6.04449 | -0.58% | 0.36% | -| SrTiO3 | Cubic perovskite | 3.98805 | 3.94513 | 3.94481 | -1.08% | -0.01% | -| SrVO3 | Cubic perovskite | 3.838 | 3.90089 | 3.90604 | 1.77% | 0.13% | -| Ta | BCC | 3.3058 | 3.32229 | 3.31741 | 0.35% | -0.15% | -| TaC0.99 | Halite | 4.456 | 4.48208 | 4.48225 | 0.59% | 0.00% | -| Th | FCC | 5.08 | 5.04122 | 5.04483 | -0.69% | 0.07% | -| TiC | Halite | 4.328 | 4.33565 | 4.33493 | 0.16% | -0.02% | -| TiN | Halite | 4.249 | 4.25353 | 4.25254 | 0.08% | -0.02% | -| V | BCC | 3.0399 | 2.99254 | 2.99346 | -1.53% | 0.03% | -| VC0.97 | Halite | 4.166 | 4.16195 | 4.16476 | -0.03% | 0.07% | -| VN | Halite | 4.136 | 4.12493 | 4.1281 | -0.19% | 0.08% | -| W | BCC | 3.155 | 3.18741 | 3.18826 | 1.05% | 0.03% | -| Xe | FCC | 6.2 | 6.66148 | 7.06991 | 14.03% | 6.13% | -| Yb | FCC | 5.49 | 5.44925 | 5.45807 | -0.58% | 0.16% | -| ZnO | Halite (FCC) | 4.58 | 4.33888 | 4.33424 | -5.37% | -0.11% | -| ZnS | Zinc blende (FCC) | 5.42 | 5.45027 | 5.45297 | 0.61% | 0.05% | -| ZrC0.97 | Halite | 4.698 | 4.72434 | 4.72451 | 0.56% | 0.00% | -| ZrN | Halite | 4.577 | 4.61762 | 4.61602 | 0.85% | -0.03% | +| Material | Crystal structure | a | MP a | Predicted a | % error vs Expt | % error vs MP | +| :---------- | :---------------- | ------: | ------: | ----------: | :-------------- | :------------ | +| Ac | FCC | 5.31 | 5.66226 | 5.6646 | 6.68% | 0.04% | +| Ag | FCC | 4.079 | 4.16055 | 4.16702 | 2.16% | 0.16% | +| Al | FCC | 4.046 | 4.03893 | 4.04108 | -0.12% | 0.05% | +| AlAs | Zinc blende (FCC) | 5.6605 | 5.73376 | 5.73027 | 1.23% | -0.06% | +| AlP | Zinc blende (FCC) | 5.451 | 5.50711 | 5.50346 | 0.96% | -0.07% | +| AlSb | Zinc blende (FCC) | 6.1355 | 6.23376 | 6.22817 | 1.51% | -0.09% | +| Ar | FCC | 5.26 | 5.64077 | 5.62745 | 6.99% | -0.24% | +| Au | FCC | 4.065 | 4.17129 | 4.17431 | 2.69% | 0.07% | +| BN | Zinc blende (FCC) | 3.615 | 3.626 | 3.62485 | 0.27% | -0.03% | +| BP | Zinc blende (FCC) | 4.538 | 4.54682 | 4.54711 | 0.20% | 0.01% | +| Ba | BCC | 5.02 | 5.0303 | 5.03454 | 0.29% | 0.08% | +| C (diamond) | Diamond (FCC) | 3.567 | 3.57371 | 3.5718 | 0.13% | -0.05% | +| Ca | FCC | 5.58 | 5.50737 | 5.52597 | -0.97% | 0.34% | +| CaVO3 | Cubic perovskite | 3.767 | 3.83041 | 3.83451 | 1.79% | 0.11% | +| CdS | Zinc blende (FCC) | 5.832 | 5.94083 | 5.9419 | 1.88% | 0.02% | +| CdSe | Zinc blende (FCC) | 6.05 | 6.21283 | 6.20987 | 2.64% | -0.05% | +| CdTe | Zinc blende (FCC) | 6.482 | 6.62905 | 6.62619 | 2.22% | -0.04% | +| Ce | FCC | 5.16 | 4.72044 | 4.71921 | -8.54% | -0.03% | +| Cr | BCC | 2.88 | 2.87403 | 2.84993 | -1.04% | -0.84% | +| CrN | Halite | 4.149 | - | 4.16068 | 0.28% | - | +| Cs | BCC | 6.05 | 6.11004 | 5.27123 | -12.87% | -13.73% | +| CsCl | Caesium chloride | 4.123 | 4.20906 | 4.20308 | 1.94% | -0.14% | +| CsF | Halite | 6.02 | 6.11801 | 6.1265 | 1.77% | 0.14% | +| CsI | Caesium chloride | 4.567 | 4.66521 | 4.90767 | 7.46% | 5.20% | +| Cu | FCC | 3.597 | 3.62126 | 3.61199 | 0.42% | -0.26% | +| Eu | BCC | 4.61 | 4.63903 | 4.34783 | -5.69% | -6.28% | +| EuTiO3 | Cubic perovskite | 7.81 | 3.96119 | 3.92943 | -49.69% | -0.80% | +| Fe | BCC | 2.856 | 2.84005 | 2.85237 | -0.13% | 0.43% | +| GaAs | Zinc blende (FCC) | 5.653 | 5.75018 | 5.75055 | 1.73% | 0.01% | +| GaP | Zinc blende (FCC) | 5.4505 | 5.5063 | 5.5054 | 1.01% | -0.02% | +| GaSb | Zinc blende (FCC) | 6.0959 | 6.21906 | 6.21939 | 2.03% | 0.01% | +| Ge | Diamond (FCC) | 5.658 | 5.76286 | 5.7698 | 1.98% | 0.12% | +| HfC0.99 | Halite | 4.64 | 4.65131 | 4.65023 | 0.22% | -0.02% | +| HfN | Halite | 4.392 | 4.53774 | 4.53838 | 3.33% | 0.01% | +| InAs | Zinc blende (FCC) | 6.0583 | 6.18148 | 6.25374 | 3.23% | 1.17% | +| InP | Zinc blende (FCC) | 5.869 | 5.95673 | 5.9679 | 1.69% | 0.19% | +| InSb | Zinc blende (FCC) | 6.479 | 6.63322 | 6.63863 | 2.46% | 0.08% | +| Ir | FCC | 3.84 | 3.87573 | 3.87716 | 0.97% | 0.04% | +| K | BCC | 5.23 | 5.26212 | 5.4993 | 5.15% | 4.51% | +| KBr | Halite | 6.6 | 6.70308 | 6.70797 | 1.64% | 0.07% | +| KCl | Halite | 6.29 | 6.38359 | 6.39634 | 1.69% | 0.20% | +| KF | Halite | 5.34 | 5.42398 | 5.41971 | 1.49% | -0.08% | +| KI | Halite | 7.07 | 7.18534 | 7.18309 | 1.60% | -0.03% | +| KTaO3 | Cubic perovskite | 3.9885 | 4.03084 | 4.03265 | 1.11% | 0.05% | +| Kr | FCC | 5.72 | 6.49646 | 6.25924 | 9.43% | -3.65% | +| Li | BCC | 3.49 | 3.42682 | 3.41891 | -2.04% | -0.23% | +| LiBr | Halite | 5.5 | 5.51343 | 5.51076 | 0.20% | -0.05% | +| LiCl | Halite | 5.14 | 5.15275 | 5.14745 | 0.15% | -0.10% | +| LiF | Halite | 4.03 | 4.08343 | 4.08531 | 1.37% | 0.05% | +| LiI | Halite | 6.01 | 6.0257 | 6.02709 | 0.28% | 0.02% | +| MgO | Halite (FCC) | 4.212 | 4.25648 | 4.2567 | 1.06% | 0.01% | +| Mo | BCC | 3.142 | 3.16762 | 3.16937 | 0.87% | 0.06% | +| Na | BCC | 4.23 | 4.17262 | 4.19684 | -0.78% | 0.58% | +| NaBr | Halite | 5.97 | 6.0276 | 6.01922 | 0.82% | -0.14% | +| NaCl | Halite | 5.64 | 5.69169 | 5.69497 | 0.97% | 0.06% | +| NaF | Halite | 4.63 | 4.69625 | 4.69553 | 1.42% | -0.02% | +| NaI | Halite | 6.47 | 6.532 | 6.52739 | 0.89% | -0.07% | +| Nb | BCC | 3.3008 | 3.32052 | 3.32221 | 0.65% | 0.05% | +| NbN | Halite | 4.392 | 4.45247 | 4.45474 | 1.43% | 0.05% | +| Ne | FCC | 4.43 | 4.30383 | 6.95744 | 57.05% | 61.66% | +| Ni | FCC | 3.499 | 3.5058 | 3.5086 | 0.27% | 0.08% | +| Pb | FCC | 4.92 | 5.05053 | 5.02849 | 2.21% | -0.44% | +| PbS | Halite (FCC) | 5.9362 | 6.00645 | 6.01752 | 1.37% | 0.18% | +| PbTe | Halite (FCC) | 6.462 | 6.56567 | 6.56111 | 1.53% | -0.07% | +| Pd | FCC | 3.859 | 3.95707 | 3.95466 | 2.48% | -0.06% | +| Pt | FCC | 3.912 | 3.97677 | 3.97714 | 1.67% | 0.01% | +| Rb | BCC | 5.59 | 5.64416 | 5.63235 | 0.76% | -0.21% | +| RbBr | Halite | 6.89 | 7.02793 | 6.98219 | 1.34% | -0.65% | +| RbCl | Halite | 6.59 | 6.69873 | 6.67994 | 1.36% | -0.28% | +| RbF | Halite | 5.65 | 5.73892 | 5.76843 | 2.10% | 0.51% | +| RbI | Halite | 7.35 | 7.48785 | 7.61756 | 3.64% | 1.73% | +| Rh | FCC | 3.8 | 3.8439 | 3.84935 | 1.30% | 0.14% | +| ScN | Halite | 4.52 | 4.51831 | 4.51797 | -0.04% | -0.01% | +| Si | Diamond (FCC) | 5.43102 | 5.46873 | 5.45002 | 0.35% | -0.34% | +| Sr | FCC | 6.08 | 6.02253 | 6.04449 | -0.58% | 0.36% | +| SrTiO3 | Cubic perovskite | 3.98805 | 3.94513 | 3.94481 | -1.08% | -0.01% | +| SrVO3 | Cubic perovskite | 3.838 | 3.90089 | 3.90604 | 1.77% | 0.13% | +| Ta | BCC | 3.3058 | 3.32229 | 3.31741 | 0.35% | -0.15% | +| TaC0.99 | Halite | 4.456 | 4.48208 | 4.48225 | 0.59% | 0.00% | +| Th | FCC | 5.08 | 5.04122 | 5.04483 | -0.69% | 0.07% | +| TiC | Halite | 4.328 | 4.33565 | 4.33493 | 0.16% | -0.02% | +| TiN | Halite | 4.249 | 4.25353 | 4.25254 | 0.08% | -0.02% | +| V | BCC | 3.0399 | 2.99254 | 2.99346 | -1.53% | 0.03% | +| VC0.97 | Halite | 4.166 | 4.16195 | 4.16476 | -0.03% | 0.07% | +| VN | Halite | 4.136 | 4.12493 | 4.1281 | -0.19% | 0.08% | +| W | BCC | 3.155 | 3.18741 | 3.18826 | 1.05% | 0.03% | +| Xe | FCC | 6.2 | 6.66148 | 7.06991 | 14.03% | 6.13% | +| Yb | FCC | 5.49 | 5.44925 | 5.45807 | -0.58% | 0.16% | +| ZnO | Halite (FCC) | 4.58 | 4.33888 | 4.33424 | -5.37% | -0.11% | +| ZnS | Zinc blende (FCC) | 5.42 | 5.45027 | 5.45297 | 0.61% | 0.05% | +| ZrC0.97 | Halite | 4.698 | 4.72434 | 4.72451 | 0.56% | 0.00% | +| ZrN | Halite | 4.577 | 4.61762 | 4.61602 | 0.85% | -0.03% | From the table, it can be observed that almost all M3GNet-relaxed cubic lattice constants are within 1% of the DFT values. The only major errors are with EuTiO3, iodides (RbI and CsI) and the noble gases. It is quite likely the @@ -238,7 +252,7 @@ from pymatgen.core import Structure, Lattice from m3gnet.models import MolecularDynamics # Init a Mo structure with stretched lattice (DFT lattice constant ~ 3.168) -mo = Structure(Lattice.cubic(3.3), +mo = Structure(Lattice.cubic(3.3), ["Mo", "Mo"], [[0., 0., 0.], [0.5, 0.5, 0.5]]) md = MolecularDynamics( @@ -271,21 +285,22 @@ Time[ps] Etot[eV] Epot[eV] Ekin[eV] T[K] 1.0000 -20.8908 -21.0772 0.1864 721.2 ``` -The MD run takes less than 1 minute. +The MD run takes less than 1 minute. # Model training You can also train your own IAP using the `PotentialTrainer` in `m3gnet.trainers`. The training dataset can include: + - structures, a list of pymatgen Structures - energies, a list of energy floats with unit eV. -- forces, a list of nx3 force matrix with unit eV/Å, where n is the number of atom in - each structure. n does not need to be the same for all structures. +- forces, a list of nx3 force matrix with unit eV/Å, where n is the number of atom in + each structure. n does not need to be the same for all structures. - stresses, a list of 3x3 stress matrices with unit GPa (optional) For stresses, we use the convention that compressive stress gives negative values. Stresses obtained from VASP calculations (default unit is kBar) should be multiplied by -0.1 to work directly with the model. -We use validation dataset to select the stopping epoch number. The dataset has similar format as the training dataset. +We use validation dataset to select the stopping epoch number. The dataset has similar format as the training dataset. A minimal example of model training is shown below. @@ -322,20 +337,19 @@ trainer.train( The training data used to develop the universal M3GNet IAP is `MPF.2021.2.8` and is hosted on [figshare](https://figshare.com/articles/dataset/MPF_2021_2_8/19470599) with DOI `10.6084/m9.figshare.19470599`. - # Reference Please cite the following work: -``` -Chi Chen, and Shyue Ping Ong. "A Universal Graph Deep Learning Interatomic Potential for the Periodic Table." +```txt +Chi Chen, and Shyue Ping Ong. "A Universal Graph Deep Learning Interatomic Potential for the Periodic Table." arXiv preprint [arXiv:2202.02450](https://arxiv.org/abs/2202.02450) (2022). ``` # Acknowledgements -This work was primarily supported by the Materials Project, funded by the U.S. Department of Energy, Office of Science, -Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract no. +This work was primarily supported by the Materials Project, funded by the U.S. Department of Energy, Office of Science, +Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract no. DE-AC02-05-CH11231: Materials Project program KC23MP. This work used the Expanse supercomputing cluster at the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. diff --git a/examples/Cubic Crystal Test.ipynb b/examples/Cubic Crystal Test.ipynb index 18917e8..dac6ee2 100644 --- a/examples/Cubic Crystal Test.ipynb +++ b/examples/Cubic Crystal Test.ipynb @@ -7,21 +7,47 @@ "metadata": {}, "outputs": [], "source": [ - "import re\n", "import warnings\n", "\n", "import pandas as pd\n", - "import datetime\n", - "\n", - "from pymatgen.core import Structure, Lattice, Composition\n", - "from pymatgen.symmetry.analyzer import SpacegroupAnalyzer\n", + "from pymatgen.core import Composition, Lattice, Structure\n", "from pymatgen.ext.matproj import MPRester\n", - "from m3gnet.models import Relaxer" + "from pymatgen.symmetry.analyzer import SpacegroupAnalyzer\n", + "from tqdm import tqdm\n", + "\n", + "from m3gnet.models import Relaxer\n", + "\n", + "for category in (UserWarning, DeprecationWarning):\n", + " warnings.filterwarnings(\"ignore\", category=category, module=\"tensorflow\")\n", + " warnings.filterwarnings(\"ignore\", category=category, module=\"pymatgen\")" ] }, { "cell_type": "code", "execution_count": 2, + "id": "82a91ea4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "tf.config.experimental.list_physical_devices(\"GPU\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "id": "0d431cc0", "metadata": {}, "outputs": [ @@ -49,9 +75,12 @@ ], "source": [ "data = pd.read_html(\"http://en.wikipedia.org/wiki/Lattice_constant\")[0]\n", - "data = data[~data[\"Crystal structure\"].isin([\"Hexagonal\", \"Wurtzite\", \"Wurtzite (HCP)\", \n", - " \"Orthorombic\", \"Tetragonal perovskite\", \"Orthorhombic perovskite\"])]\n", - "data.rename(columns = {'Lattice constant (Å)':'a'}, inplace = True)\n", + "data = data[\n", + " ~data[\"Crystal structure\"].isin(\n", + " [\"Hexagonal\", \"Wurtzite\", \"Wurtzite (HCP)\", \"Orthorombic\", \"Tetragonal perovskite\", \"Orthorhombic perovskite\"]\n", + " )\n", + "]\n", + "data.rename(columns={\"Lattice constant (Å)\": \"a\"}, inplace=True)\n", "data.drop(columns=[\"Ref.\"], inplace=True)\n", "data[\"a\"] = data[\"a\"].map(float)\n", "data = data[[\"Material\", \"Crystal structure\", \"a\"]]\n", @@ -76,16 +105,18 @@ "26 Fe 2.87 74 W 3.16\n", "37 Rb 5.59 41 Nb 3.30\"\"\"\n", "\n", + "\n", "def add_new(str_, structure_type, df):\n", - " toks = str_.split()\n", + " tokens = str_.split()\n", " new_crystals = []\n", - " for i in range(int(len(toks) / 3)):\n", - " el = toks[3*i+1].strip()\n", + " for i in range(int(len(tokens) / 3)):\n", + " el = tokens[3 * i + 1].strip()\n", " if el not in df[\"Material\"].values:\n", - " new_crystals.append([toks[3*i+1], structure_type, float(toks[3*i+2])])\n", + " new_crystals.append([tokens[3 * i + 1], structure_type, float(tokens[3 * i + 2])])\n", " df2 = pd.DataFrame(new_crystals, columns=data.columns)\n", " return pd.concat([df, df2])\n", "\n", + "\n", "data = add_new(additional_fcc, \"FCC\", data)\n", "data = add_new(additional_bcc, \"BCC\", data)\n", "data.set_index(\"Material\", inplace=True)\n", @@ -94,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "2541aebf", "metadata": {}, "outputs": [ @@ -102,222 +133,1660 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-06-16 20:00:37.678599: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n", - "Traceback (most recent call last):\n", - " File \"/var/folders/ql/m5k56v8n5sz5880n5sksmc9w0000gn/T/ipykernel_6696/1671824916.py\", line 62, in \n", + " 0%| | 0/92 [00:00\n", " raise RuntimeError\n", - "RuntimeError\n" + "RuntimeError\n", + "100%|██████████| 92/92 [06:39<00:00, 4.34s/it]\n" ] } ], "source": [ "predicted = []\n", "mp = []\n", - "relaxation_time = []\n", "mpr = MPRester()\n", "relaxer = Relaxer() # This loads the default pre-trained model\n", "\n", - "for formula, v in data.iterrows():\n", - " with warnings.catch_warnings(record=True) as caught_warnings: # just to capture all the annoying TF warnings.\n", - " warnings.simplefilter(\"always\")\n", - " formula = formula.split()[0]\n", - " c = Composition(formula)\n", - " els = sorted(c.elements)\n", - " cs = v[\"Crystal structure\"]\n", + "# warnings.filterwarnings(action=\"ignore\", category=UserWarning, module=\"tensorflow\")\n", "\n", - " # We initialize all the crystals with an arbitrary lattice constant of 5 angstroms.\n", - " if \"Zinc blende\" in cs:\n", - " s = Structure.from_spacegroup(\"F-43m\", Lattice.cubic(5), [els[0], els[1]], [[0, 0, 0], [0.25, 0.25, 0.75]])\n", - " elif \"Halite\" in cs:\n", - " s = Structure.from_spacegroup(\"Fm-3m\", Lattice.cubic(5), [els[0], els[1]], [[0, 0, 0], [0.5, 0, 0]])\n", - " elif \"Caesium chloride\" in cs:\n", - " s = Structure.from_spacegroup(\"Pm-3m\", Lattice.cubic(5), [els[0], els[1]], [[0, 0, 0], [0.5, 0.5, 0.5]])\n", - " elif \"Cubic perovskite\" in cs:\n", - " s = Structure(Lattice.cubic(5), [els[0], els[1], els[2], els[2], els[2]], \n", - " [[0., 0., 0.], [0.5, 0.5, 0.5], [0.5, 0.5, 0], [0., 0.5, 0.5], [0.5, 0, 0.5]])\n", - " elif \"Diamond\" in cs:\n", - " s = Structure.from_spacegroup(\"Fd-3m\", Lattice.cubic(5), [els[0]], [[0.25, 0.75, 0.25]])\n", - " elif \"BCC\" in cs:\n", - " s = Structure(Lattice.cubic(5), [els[0]] * 2, [[0., 0., 0.], [0.5, 0.5, 0.5]])\n", - " elif \"FCC\" in cs:\n", - " s = Structure(Lattice.cubic(5), [els[0]] * 4, [[0., 0., 0.], [0.5, 0.5, 0], [0., 0.5, 0.5], [0.5, 0, 0.5]])\n", - " else:\n", - " predicted.append(0)\n", - " mp.append(0)\n", - " continue\n", + "for formula, v in tqdm(data.iterrows(), total=len(data)):\n", + " formula = formula.split()[0]\n", + " c = Composition(formula)\n", + " els = sorted(c.elements)\n", + " cs = v[\"Crystal structure\"]\n", "\n", - " start = datetime.datetime.now()\n", + " # We initialize all the crystals with an arbitrary lattice constant of 5 angstroms.\n", + " if \"Zinc blende\" in cs:\n", + " s = Structure.from_spacegroup(\"F-43m\", Lattice.cubic(5), [els[0], els[1]], [[0, 0, 0], [0.25, 0.25, 0.75]])\n", + " elif \"Halite\" in cs:\n", + " s = Structure.from_spacegroup(\"Fm-3m\", Lattice.cubic(5), [els[0], els[1]], [[0, 0, 0], [0.5, 0, 0]])\n", + " elif \"Caesium chloride\" in cs:\n", + " s = Structure.from_spacegroup(\"Pm-3m\", Lattice.cubic(5), [els[0], els[1]], [[0, 0, 0], [0.5, 0.5, 0.5]])\n", + " elif \"Cubic perovskite\" in cs:\n", + " s = Structure(\n", + " Lattice.cubic(5),\n", + " [els[0], els[1], els[2], els[2], els[2]],\n", + " [[0.0, 0.0, 0.0], [0.5, 0.5, 0.5], [0.5, 0.5, 0], [0.0, 0.5, 0.5], [0.5, 0, 0.5]],\n", + " )\n", + " elif \"Diamond\" in cs:\n", + " s = Structure.from_spacegroup(\"Fd-3m\", Lattice.cubic(5), [els[0]], [[0.25, 0.75, 0.25]])\n", + " elif \"BCC\" in cs:\n", + " s = Structure(Lattice.cubic(5), [els[0]] * 2, [[0.0, 0.0, 0.0], [0.5, 0.5, 0.5]])\n", + " elif \"FCC\" in cs:\n", + " s = Structure(Lattice.cubic(5), [els[0]] * 4, [[0.0, 0.0, 0.0], [0.5, 0.5, 0], [0.0, 0.5, 0.5], [0.5, 0, 0.5]])\n", + " else:\n", + " predicted.append(0)\n", + " mp.append(0)\n", + " continue\n", "\n", - " relax_results = relaxer.relax(s)\n", + " relax_results = relaxer.relax(s)\n", "\n", - " final_structure = relax_results['final_structure']\n", + " final_structure = relax_results[\"final_structure\"]\n", "\n", - " timetaken = datetime.datetime.now() - start\n", + " predicted.append(final_structure.lattice.a)\n", + "\n", + " try:\n", + " entries = mpr.get_entries(\n", + " {\"pretty_formula\": s.composition.reduced_formula},\n", + " inc_structure=True,\n", + " property_data=[\"e_above_hull\"],\n", + " sort_by_e_above_hull=True,\n", + " )\n", + " for e in entries:\n", + " try:\n", + " sga = SpacegroupAnalyzer(e.structure)\n", + " sga2 = SpacegroupAnalyzer(final_structure)\n", + " if sga.get_space_group_number() == sga2.get_space_group_number():\n", + " conv = sga.get_conventional_standard_structure()\n", + " mp.append(conv.lattice.a)\n", + " break\n", + " except:\n", + " pass\n", + " else:\n", + " raise RuntimeError\n", + " except Exception as ex:\n", + " mp.append(0)\n", + " import traceback\n", "\n", - " predicted.append(final_structure.lattice.a)\n", - " relaxation_time.append(timetaken)\n", + " traceback.print_exc()\n", "\n", - " try:\n", - " entries = mpr.get_entries({\"pretty_formula\": s.composition.reduced_formula}, \n", - " inc_structure=True, property_data=[\"e_above_hull\"])\n", - " entries = sorted(entries, key=lambda e: e.data[\"e_above_hull\"])\n", - " for e in entries:\n", - " try:\n", - " sga = SpacegroupAnalyzer(e.structure)\n", - " sga2 = SpacegroupAnalyzer(final_structure)\n", - " if sga.get_space_group_number() == sga2.get_space_group_number():\n", - " conv = sga.get_conventional_standard_structure()\n", - " mp.append(conv.lattice.a)\n", - " break\n", - " except:\n", - " pass\n", - " else:\n", - " raise RuntimeError\n", - " except Exception as ex:\n", - " mp.append(0)\n", - " import traceback\n", - " traceback.print_exc()\n", "data[\"MP a\"] = mp\n", "data[\"Predicted a\"] = predicted" ] }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8cdd6407", - "metadata": {}, - "outputs": [], - "source": [ - "data[\"% error vs Expt\"] = (data[\"Predicted a\"] - data[\"a\"])/data[\"a\"] * 100\n", - "data[\"% error vs MP\"] = (data[\"Predicted a\"] - data[\"MP a\"])/data[\"MP a\"] * 100" - ] - }, { "cell_type": "code", "execution_count": 5, - "id": "fc04aa83", + "id": "8cdd6407", "metadata": {}, "outputs": [], "source": [ - "data[\"% error vs Expt\"] = data[\"% error vs Expt\"].map('{:,.2f}%'.format)\n", - "data[\"% error vs MP\"] = data[\"% error vs MP\"].map('{:,.2f}%'.format)" + "data[\"% error vs Expt\"] = (data[\"Predicted a\"] - data[\"a\"]) / data[\"a\"]\n", + "data[\"% error vs MP\"] = (data[\"Predicted a\"] - data[\"MP a\"]) / data[\"MP a\"]" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "b71312dc", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "| Material | Crystal structure | a | MP a | Predicted a | % error vs Expt | % error vs MP |\n", - "|:------------|:--------------------|--------:|--------:|--------------:|:------------------|:----------------|\n", - "| Ac | FCC | 5.31 | 5.66226 | 5.6646 | 6.68% | 0.04% |\n", - "| Ag | FCC | 4.079 | 4.16055 | 4.16702 | 2.16% | 0.16% |\n", - "| Al | FCC | 4.046 | 4.03893 | 4.04108 | -0.12% | 0.05% |\n", - "| AlAs | Zinc blende (FCC) | 5.6605 | 5.73376 | 5.73027 | 1.23% | -0.06% |\n", - "| AlP | Zinc blende (FCC) | 5.451 | 5.50711 | 5.50346 | 0.96% | -0.07% |\n", - "| AlSb | Zinc blende (FCC) | 6.1355 | 6.23376 | 6.22817 | 1.51% | -0.09% |\n", - "| Ar | FCC | 5.26 | 5.64077 | 5.62745 | 6.99% | -0.24% |\n", - "| Au | FCC | 4.065 | 4.17129 | 4.17431 | 2.69% | 0.07% |\n", - "| BN | Zinc blende (FCC) | 3.615 | 3.626 | 3.62485 | 0.27% | -0.03% |\n", - "| BP | Zinc blende (FCC) | 4.538 | 4.54682 | 4.54711 | 0.20% | 0.01% |\n", - "| Ba | BCC | 5.02 | 5.0303 | 5.03454 | 0.29% | 0.08% |\n", - "| C (diamond) | Diamond (FCC) | 3.567 | 3.57371 | 3.5718 | 0.13% | -0.05% |\n", - "| Ca | FCC | 5.58 | 5.50737 | 5.52597 | -0.97% | 0.34% |\n", - "| CaVO3 | Cubic perovskite | 3.767 | 3.83041 | 3.83451 | 1.79% | 0.11% |\n", - "| CdS | Zinc blende (FCC) | 5.832 | 5.94083 | 5.9419 | 1.88% | 0.02% |\n", - "| CdSe | Zinc blende (FCC) | 6.05 | 6.21283 | 6.20987 | 2.64% | -0.05% |\n", - "| CdTe | Zinc blende (FCC) | 6.482 | 6.62905 | 6.62619 | 2.22% | -0.04% |\n", - "| Ce | FCC | 5.16 | 4.72044 | 4.71921 | -8.54% | -0.03% |\n", - "| Cr | BCC | 2.88 | 2.87403 | 2.84993 | -1.04% | -0.84% |\n", - "| CrN | Halite | 4.149 | 0 | 4.16068 | 0.28% | inf% |\n", - "| Cs | BCC | 6.05 | 6.11004 | 5.27123 | -12.87% | -13.73% |\n", - "| CsCl | Caesium chloride | 4.123 | 4.20906 | 4.20308 | 1.94% | -0.14% |\n", - "| CsF | Halite | 6.02 | 6.11801 | 6.1265 | 1.77% | 0.14% |\n", - "| CsI | Caesium chloride | 4.567 | 4.66521 | 4.90767 | 7.46% | 5.20% |\n", - "| Cu | FCC | 3.597 | 3.62126 | 3.61199 | 0.42% | -0.26% |\n", - "| Eu | BCC | 4.61 | 4.63903 | 4.34783 | -5.69% | -6.28% |\n", - "| EuTiO3 | Cubic perovskite | 7.81 | 3.96119 | 3.92943 | -49.69% | -0.80% |\n", - "| Fe | BCC | 2.856 | 2.84005 | 2.85237 | -0.13% | 0.43% |\n", - "| GaAs | Zinc blende (FCC) | 5.653 | 5.75018 | 5.75055 | 1.73% | 0.01% |\n", - "| GaP | Zinc blende (FCC) | 5.4505 | 5.5063 | 5.5054 | 1.01% | -0.02% |\n", - "| GaSb | Zinc blende (FCC) | 6.0959 | 6.21906 | 6.21939 | 2.03% | 0.01% |\n", - "| Ge | Diamond (FCC) | 5.658 | 5.76286 | 5.7698 | 1.98% | 0.12% |\n", - "| HfC0.99 | Halite | 4.64 | 4.65131 | 4.65023 | 0.22% | -0.02% |\n", - "| HfN | Halite | 4.392 | 4.53774 | 4.53838 | 3.33% | 0.01% |\n", - "| InAs | Zinc blende (FCC) | 6.0583 | 6.18148 | 6.25374 | 3.23% | 1.17% |\n", - "| InP | Zinc blende (FCC) | 5.869 | 5.95673 | 5.9679 | 1.69% | 0.19% |\n", - "| InSb | Zinc blende (FCC) | 6.479 | 6.63322 | 6.63863 | 2.46% | 0.08% |\n", - "| Ir | FCC | 3.84 | 3.87573 | 3.87716 | 0.97% | 0.04% |\n", - "| K | BCC | 5.23 | 5.26212 | 5.4993 | 5.15% | 4.51% |\n", - "| KBr | Halite | 6.6 | 6.70308 | 6.70797 | 1.64% | 0.07% |\n", - "| KCl | Halite | 6.29 | 6.38359 | 6.39634 | 1.69% | 0.20% |\n", - "| KF | Halite | 5.34 | 5.42398 | 5.41971 | 1.49% | -0.08% |\n", - "| KI | Halite | 7.07 | 7.18534 | 7.18309 | 1.60% | -0.03% |\n", - "| KTaO3 | Cubic perovskite | 3.9885 | 4.03084 | 4.03265 | 1.11% | 0.05% |\n", - "| Kr | FCC | 5.72 | 6.49646 | 6.25924 | 9.43% | -3.65% |\n", - "| Li | BCC | 3.49 | 3.42682 | 3.41891 | -2.04% | -0.23% |\n", - "| LiBr | Halite | 5.5 | 5.51343 | 5.51076 | 0.20% | -0.05% |\n", - "| LiCl | Halite | 5.14 | 5.15275 | 5.14745 | 0.15% | -0.10% |\n", - "| LiF | Halite | 4.03 | 4.08343 | 4.08531 | 1.37% | 0.05% |\n", - "| LiI | Halite | 6.01 | 6.0257 | 6.02709 | 0.28% | 0.02% |\n", - "| MgO | Halite (FCC) | 4.212 | 4.25648 | 4.2567 | 1.06% | 0.01% |\n", - "| Mo | BCC | 3.142 | 3.16762 | 3.16937 | 0.87% | 0.06% |\n", - "| Na | BCC | 4.23 | 4.17262 | 4.19684 | -0.78% | 0.58% |\n", - "| NaBr | Halite | 5.97 | 6.0276 | 6.01922 | 0.82% | -0.14% |\n", - "| NaCl | Halite | 5.64 | 5.69169 | 5.69497 | 0.97% | 0.06% |\n", - "| NaF | Halite | 4.63 | 4.69625 | 4.69553 | 1.42% | -0.02% |\n", - "| NaI | Halite | 6.47 | 6.532 | 6.52739 | 0.89% | -0.07% |\n", - "| Nb | BCC | 3.3008 | 3.32052 | 3.32221 | 0.65% | 0.05% |\n", - "| NbN | Halite | 4.392 | 4.45247 | 4.45474 | 1.43% | 0.05% |\n", - "| Ne | FCC | 4.43 | 4.30383 | 6.95744 | 57.05% | 61.66% |\n", - "| Ni | FCC | 3.499 | 3.5058 | 3.5086 | 0.27% | 0.08% |\n", - "| Pb | FCC | 4.92 | 5.05053 | 5.02849 | 2.21% | -0.44% |\n", - "| PbS | Halite (FCC) | 5.9362 | 6.00645 | 6.01752 | 1.37% | 0.18% |\n", - "| PbTe | Halite (FCC) | 6.462 | 6.56567 | 6.56111 | 1.53% | -0.07% |\n", - "| Pd | FCC | 3.859 | 3.95707 | 3.95466 | 2.48% | -0.06% |\n", - "| Pt | FCC | 3.912 | 3.97677 | 3.97714 | 1.67% | 0.01% |\n", - "| Rb | BCC | 5.59 | 5.64416 | 5.63235 | 0.76% | -0.21% |\n", - "| RbBr | Halite | 6.89 | 7.02793 | 6.98219 | 1.34% | -0.65% |\n", - "| RbCl | Halite | 6.59 | 6.69873 | 6.67994 | 1.36% | -0.28% |\n", - "| RbF | Halite | 5.65 | 5.73892 | 5.76843 | 2.10% | 0.51% |\n", - "| RbI | Halite | 7.35 | 7.48785 | 7.61756 | 3.64% | 1.73% |\n", - "| Rh | FCC | 3.8 | 3.8439 | 3.84935 | 1.30% | 0.14% |\n", - "| ScN | Halite | 4.52 | 4.51831 | 4.51797 | -0.04% | -0.01% |\n", - "| Si | Diamond (FCC) | 5.43102 | 5.46873 | 5.45002 | 0.35% | -0.34% |\n", - "| Sr | FCC | 6.08 | 6.02253 | 6.04449 | -0.58% | 0.36% |\n", - "| SrTiO3 | Cubic perovskite | 3.98805 | 3.94513 | 3.94481 | -1.08% | -0.01% |\n", - "| SrVO3 | Cubic perovskite | 3.838 | 3.90089 | 3.90604 | 1.77% | 0.13% |\n", - "| Ta | BCC | 3.3058 | 3.32229 | 3.31741 | 0.35% | -0.15% |\n", - "| TaC0.99 | Halite | 4.456 | 4.48208 | 4.48225 | 0.59% | 0.00% |\n", - "| Th | FCC | 5.08 | 5.04122 | 5.04483 | -0.69% | 0.07% |\n", - "| TiC | Halite | 4.328 | 4.33565 | 4.33493 | 0.16% | -0.02% |\n", - "| TiN | Halite | 4.249 | 4.25353 | 4.25254 | 0.08% | -0.02% |\n", - "| V | BCC | 3.0399 | 2.99254 | 2.99346 | -1.53% | 0.03% |\n", - "| VC0.97 | Halite | 4.166 | 4.16195 | 4.16476 | -0.03% | 0.07% |\n", - "| VN | Halite | 4.136 | 4.12493 | 4.1281 | -0.19% | 0.08% |\n", - "| W | BCC | 3.155 | 3.18741 | 3.18826 | 1.05% | 0.03% |\n", - "| Xe | FCC | 6.2 | 6.66148 | 7.06991 | 14.03% | 6.13% |\n", - "| Yb | FCC | 5.49 | 5.44925 | 5.45807 | -0.58% | 0.16% |\n", - "| ZnO | Halite (FCC) | 4.58 | 4.33888 | 4.33424 | -5.37% | -0.11% |\n", - "| ZnS | Zinc blende (FCC) | 5.42 | 5.45027 | 5.45297 | 0.61% | 0.05% |\n", - "| ZrC0.97 | Halite | 4.698 | 4.72434 | 4.72451 | 0.56% | 0.00% |\n", - "| ZrN | Halite | 4.577 | 4.61762 | 4.61602 | 0.85% | -0.03% |\n" + "/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.9/site-packages/pandas/io/formats/style.py:3559: RuntimeWarning: invalid value encountered in double_scalars\n", + " norm = mpl.colors.Normalize(smin - (rng * low), smax + (rng * high))\n" ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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 Crystal structureaMP aPredicted a% error vs Expt% error vs MP
Material      
AcFCC5.3100005.6622565.6646006.68%0.04%
AgFCC4.0790004.1605484.1670172.16%0.16%
AlFCC4.0460004.0389304.041083-0.12%0.05%
AlAsZinc blende (FCC)5.6605005.7337565.7302711.23%-0.06%
AlPZinc blende (FCC)5.4510005.5071125.5034620.96%-0.07%
AlSbZinc blende (FCC)6.1355006.2337606.2281681.51%-0.09%
ArFCC5.2600005.6407725.6274476.99%-0.24%
AuFCC4.0650004.1712884.1743142.69%0.07%
BNZinc blende (FCC)3.6150003.6260023.6248530.27%-0.03%
BPZinc blende (FCC)4.5380004.5468164.5471110.20%0.01%
BaBCC5.0200005.0303005.0345430.29%0.08%
C (diamond)Diamond (FCC)3.5670003.5737103.5718040.13%-0.05%
CaFCC5.5800005.5073705.525968-0.97%0.34%
CaVO3Cubic perovskite3.7670003.8304063.8345131.79%0.11%
CdSZinc blende (FCC)5.8320005.9408345.9419021.88%0.02%
CdSeZinc blende (FCC)6.0500006.2128346.2098692.64%-0.05%
CdTeZinc blende (FCC)6.4820006.6290486.6261932.22%-0.04%
CeFCC5.1600004.7204364.719214-8.54%-0.03%
CrBCC2.8800002.8740252.849934-1.04%-0.84%
CrNHalite4.1490000.0000004.1606820.28%inf%
CsBCC6.0500006.1100405.271233-12.87%-13.73%
CsClCaesium chloride4.1230004.2090554.2030851.94%-0.14%
CsFHalite6.0200006.1180106.1265021.77%0.14%
CsICaesium chloride4.5670004.6652124.9076667.46%5.20%
CuFCC3.5970003.6212623.6119860.42%-0.26%
EuBCC4.6100004.6390344.347832-5.69%-6.28%
EuTiO3Cubic perovskite7.8100003.9611953.929427-49.69%-0.80%
FeBCC2.8560002.8400522.852366-0.13%0.43%
GaAsZinc blende (FCC)5.6530005.7501825.7505481.73%0.01%
GaPZinc blende (FCC)5.4505005.5063005.5053961.01%-0.02%
GaSbZinc blende (FCC)6.0959006.2190626.2193862.03%0.01%
GeDiamond (FCC)5.6580005.7628625.7698031.98%0.12%
HfC0.99Halite4.6400004.6513124.6502290.22%-0.02%
HfNHalite4.3920004.5377424.5383753.33%0.01%
InAsZinc blende (FCC)6.0583006.1814786.2537453.23%1.17%
InPZinc blende (FCC)5.8690005.9567265.9678971.69%0.19%
InSbZinc blende (FCC)6.4790006.6332226.6386312.46%0.08%
IrFCC3.8400003.8757283.8771630.97%0.04%
KBCC5.2300005.2621205.4992985.15%4.51%
KBrHalite6.6000006.7030766.7079681.64%0.07%
KClHalite6.2900006.3835926.3963421.69%0.20%
KFHalite5.3400005.4239845.4197131.49%-0.08%
KIHalite7.0700007.1853447.1830921.60%-0.03%
KTaO3Cubic perovskite3.9885004.0308394.0326551.11%0.05%
KrFCC5.7200006.4964626.2592419.43%-3.65%
LiBCC3.4900003.4268183.418905-2.04%-0.23%
LiBrHalite5.5000005.5134285.5107620.20%-0.05%
LiClHalite5.1400005.1527485.1474550.15%-0.10%
LiFHalite4.0300004.0834284.0853151.37%0.05%
LiIHalite6.0100006.0257006.0270920.28%0.02%
MgOHalite (FCC)4.2120004.2564844.2567031.06%0.01%
MoBCC3.1420003.1676183.1693690.87%0.06%
NaBCC4.2300004.1726224.196835-0.78%0.58%
NaBrHalite5.9700006.0275966.0192180.82%-0.14%
NaClHalite5.6400005.6916945.6949710.97%0.06%
NaFHalite4.6300004.6962464.6955271.42%-0.02%
NaIHalite6.4700006.5319966.5273950.89%-0.07%
NbBCC3.3008003.3205203.3222130.65%0.05%
NbNHalite4.3920004.4524684.4547361.43%0.05%
NeFCC4.4300004.3038266.95744157.05%61.66%
NiFCC3.4990003.5057983.5086030.27%0.08%
PbFCC4.9200005.0505345.0284902.21%-0.44%
PbSHalite (FCC)5.9362006.0064466.0175181.37%0.18%
PbTeHalite (FCC)6.4620006.5656746.5611051.53%-0.07%
PdFCC3.8590003.9570663.9546612.48%-0.06%
PtFCC3.9120003.9767703.9771401.67%0.01%
RbBCC5.5900005.6441565.6323500.76%-0.21%
RbBrHalite6.8900007.0279306.9821891.34%-0.65%
RbClHalite6.5900006.6987286.6799431.36%-0.28%
RbFHalite5.6500005.7389205.7684272.10%0.51%
RbIHalite7.3500007.4878527.6175653.64%1.73%
RhFCC3.8000003.8438983.8493511.30%0.14%
ScNHalite4.5200004.5183104.517974-0.04%-0.01%
SiDiamond (FCC)5.4310215.4687285.4500230.35%-0.34%
SrFCC6.0800006.0225306.044491-0.58%0.36%
SrTiO3Cubic perovskite3.9880503.9451303.944807-1.08%-0.01%
SrVO3Cubic perovskite3.8380003.9008913.9060431.77%0.13%
TaBCC3.3058003.3222883.3174140.35%-0.15%
TaC0.99Halite4.4560004.4820764.4822520.59%0.00%
ThFCC5.0800005.0412185.044834-0.69%0.07%
TiCHalite4.3280004.3356504.3349330.16%-0.02%
TiNHalite4.2490004.2535344.2525410.08%-0.02%
VBCC3.0399002.9925442.993463-1.53%0.03%
VC0.97Halite4.1660004.1619464.164755-0.03%0.07%
VNHalite4.1360004.1249304.128105-0.19%0.08%
WBCC3.1550003.1874143.1882561.05%0.03%
XeFCC6.2000006.6614767.06991214.03%6.13%
YbFCC5.4900005.4492505.458069-0.58%0.16%
ZnOHalite (FCC)4.5800004.3388844.334245-5.37%-0.11%
ZnSZinc blende (FCC)5.4200005.4502705.4529730.61%0.05%
ZrC0.97Halite4.6980004.7243404.7245100.56%0.00%
ZrNHalite4.5770004.6176244.6160210.85%-0.03%
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(data.sort_index().to_markdown())" + "data.sort_index().style.format({\"% error vs Expt\": \"{:,.2%}\", \"% error vs MP\": \"{:,.2%}\"}).background_gradient()" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.10.4 64-bit ('m3gnet')", "language": "python", "name": "python3" }, @@ -331,7 +1800,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "95e5cb0dae99d6911ce19b14750141ca4ad955f9302739260772daefb06054c0" + } } }, "nbformat": 4, diff --git a/examples/Relaxation of LiFePO4.ipynb b/examples/Relaxation of LiFePO4.ipynb index 7d731f9..57a1304 100644 --- a/examples/Relaxation of LiFePO4.ipynb +++ b/examples/Relaxation of LiFePO4.ipynb @@ -12,22 +12,60 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "5e008446", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mRunning cells with 'Python 3.9.12 ('base')' requires ipykernel package.\n", + "Run the following command to install 'ipykernel' into the Python environment. \n", + "Command: 'conda install -n base ipykernel --update-deps --force-reinstall'" + ] + } + ], "source": [ - "import numpy as np\n", + "import warnings\n", "\n", + "import numpy as np\n", "from pymatgen.ext.matproj import MPRester\n", "from pymatgen.util.coord import pbc_diff\n", "\n", - "from m3gnet.models import Relaxer" + "from m3gnet.models import Relaxer\n", + "\n", + "for category in (UserWarning, DeprecationWarning):\n", + " warnings.filterwarnings(\"ignore\", category=category, module=\"tensorflow\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd27f94f", + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mRunning cells with 'Python 3.9.12 ('base')' requires ipykernel package.\n", + "Run the following command to install 'ipykernel' into the Python environment. \n", + "Command: 'conda install -n base ipykernel --update-deps --force-reinstall'" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "tf.config.experimental.list_physical_devices(\"GPU\")" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "0cc2e942", "metadata": {}, "outputs": [], @@ -36,199 +74,78 @@ "lfp = mpr.get_structure_by_material_id(\"mp-19017\") # This is LiFePO4.\n", "\n", "lfp_strained = lfp.copy() # We create a copy.\n", - "strains = np.random.uniform(low=-0.05, high=0.05, size=3) # Create a random strain between -5% and 5% for each direction\n", + "# Create a random strain between -5% and 5% for each direction\n", + "strains = np.random.uniform(low=-0.05, high=0.05, size=3)\n", "lfp_strained.apply_strain(strains)\n", - "lfp_strained.perturb(0.1) # In addition to the lattice strains, we also perturb the atoms by a distance of 0.1 angstrom." + "# In addition to the lattice strains, we also perturb the atoms by a distance of 0.1 angstrom.\n", + "lfp_strained.perturb(0.1)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "f8897c0e", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"PartitionedCall:4\", shape=(None,), dtype=int32), values=Tensor(\"PartitionedCall:3\", shape=(None, 3, 3), dtype=float32), dense_shape=Tensor(\"PartitionedCall:5\", shape=(3,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"PartitionedCall:1\", shape=(2628,), dtype=int32), values=Tensor(\"Neg:0\", shape=(2628, 3), dtype=float32), dense_shape=Tensor(\"PartitionedCall:2\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "2022-05-31 15:12:30.665157: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" + "Metal device set to: Apple M1 Pro\n", + "\n", + "systemMemory: 16.00 GB\n", + "maxCacheSize: 5.33 GB\n", + "\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " Step Time Energy fmax\n", - "*Force-consistent energies used in optimization.\n", - "FIRE: 0 15:12:30 -188.051178* 35.3037\n" + "2022-07-10 12:50:06.141194: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", + "2022-07-10 12:50:06.141334: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )\n", + "2022-07-10 12:50:09.946045: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n", + "2022-07-10 12:50:09.957309: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/three_d_interaction_2/GatherV2_1_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/three_d_interaction_2/GatherV2_1_grad/Reshape:0\", dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/three_d_interaction_2/GatherV2_1_grad/Cast:0\", shape=(None,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/graph_network_layer_2/gated_atom_update_2/GatherV2_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/graph_network_layer_2/gated_atom_update_2/GatherV2_grad/Reshape:0\", dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/graph_network_layer_2/gated_atom_update_2/GatherV2_grad/Cast:0\", shape=(None,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/graph_network_layer_2/gated_atom_update_2/GatherV2_1_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/graph_network_layer_2/gated_atom_update_2/GatherV2_1_grad/Reshape:0\", dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/graph_network_layer_2/gated_atom_update_2/GatherV2_1_grad/Cast:0\", shape=(None,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/graph_network_layer_2/concat_atoms_2/GatherV2_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/graph_network_layer_2/concat_atoms_2/GatherV2_grad/Reshape:0\", dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/graph_network_layer_2/concat_atoms_2/GatherV2_grad/Cast:0\", shape=(None,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/three_d_interaction_1/GatherV2_1_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/three_d_interaction_1/GatherV2_1_grad/Reshape:0\", dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/three_d_interaction_1/GatherV2_1_grad/Cast:0\", shape=(None,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/graph_network_layer_1/gated_atom_update_1/GatherV2_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/graph_network_layer_1/gated_atom_update_1/GatherV2_grad/Reshape:0\", dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/graph_network_layer_1/gated_atom_update_1/GatherV2_grad/Cast:0\", shape=(None,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/graph_network_layer_1/gated_atom_update_1/GatherV2_1_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/graph_network_layer_1/gated_atom_update_1/GatherV2_1_grad/Reshape:0\", dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/graph_network_layer_1/gated_atom_update_1/GatherV2_1_grad/Cast:0\", shape=(None,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/graph_network_layer_1/concat_atoms_1/GatherV2_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/graph_network_layer_1/concat_atoms_1/GatherV2_grad/Reshape:0\", dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/graph_network_layer_1/concat_atoms_1/GatherV2_grad/Cast:0\", shape=(None,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/concat_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/concat:0\", shape=(None,), dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/three_d_interaction_2/GatherV2_2_grad/Cast:0\", shape=(1,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/GatherV2_5_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/GatherV2_5_grad/Reshape:0\", shape=(None,), dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/GatherV2_5_grad/Cast:0\", shape=(1,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/GatherV2_6_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/GatherV2_6_grad/Reshape:0\", shape=(None,), dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/GatherV2_6_grad/Cast:0\", shape=(1,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/GatherV2_3_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/GatherV2_3_grad/Reshape:0\", shape=(None, 3), dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/GatherV2_3_grad/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/m3g_net/GatherV2_4_grad/Reshape_1:0\", shape=(None,), dtype=int32), values=Tensor(\"gradients/m3g_net/GatherV2_4_grad/Reshape:0\", shape=(None, 3), dtype=float32), dense_shape=Tensor(\"gradients/m3g_net/GatherV2_4_grad/Cast:0\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n", - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"PartitionedCall:1\", shape=(None,), dtype=int32), values=Tensor(\"Neg:0\", shape=(None, 3), dtype=float32), dense_shape=Tensor(\"PartitionedCall:2\", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", - " warnings.warn(\n" + "ename": "NotFoundError", + "evalue": "Graph execution error:\n\nDetected at node 'gradients/m3g_net/graph_network_layer/gated_atom_update/UnsortedSegmentSum_grad/and' defined at (most recent call last):\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n return _run_code(code, main_globals, None,\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/runpy.py\", line 86, in _run_code\n exec(code, run_globals)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel_launcher.py\", line 17, in \n app.launch_new_instance()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/traitlets/config/application.py\", line 976, in launch_instance\n app.start()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/kernelapp.py\", line 712, in start\n self.io_loop.start()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/tornado/platform/asyncio.py\", line 215, in start\n self.asyncio_loop.run_forever()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/asyncio/base_events.py\", line 600, in run_forever\n self._run_once()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/asyncio/base_events.py\", line 1896, in _run_once\n handle._run()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/asyncio/events.py\", line 80, in _run\n self._context.run(self._callback, *self._args)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 510, in dispatch_queue\n await self.process_one()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 499, in process_one\n await dispatch(*args)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 406, in dispatch_shell\n await result\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 730, in execute_request\n reply_content = await reply_content\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 383, in do_execute\n res = shell.run_cell(\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/zmqshell.py\", line 528, in run_cell\n return super().run_cell(*args, **kwargs)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 2881, in run_cell\n result = self._run_cell(\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 2936, in _run_cell\n return runner(coro)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n coro.send(None)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3135, in run_cell_async\n has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3338, in run_ast_nodes\n if await self.run_code(code, result, async_=asy):\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3398, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"/var/folders/5m/sb0wx83j0m36xk_zswkfnffh0000gn/T/ipykernel_9555/3879703908.py\", line 2, in \n get_ipython().run_line_magic('time', 'relax_results = relaxer.relax(lfp_strained)')\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 2305, in run_line_magic\n result = fn(*args, **kwargs)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/magics/execution.py\", line 1316, in time\n exec(code, glob, local_ns)\n File \"\", line 1, in \n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/m3gnet/models/_dynamics.py\", line 167, in relax\n optimizer = self.opt_class(atoms, **kwargs)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/optimize/fire.py\", line 54, in __init__\n Optimizer.__init__(self, atoms, restart, logfile, trajectory,\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/optimize/optimize.py\", line 234, in __init__\n self.set_force_consistent()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/optimize/optimize.py\", line 325, in set_force_consistent\n self.atoms.get_potential_energy(force_consistent=True)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/constraints.py\", line 2420, in get_potential_energy\n atoms_energy = self.atoms.get_potential_energy(\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/atoms.py\", line 728, in get_potential_energy\n energy = self._calc.get_potential_energy(\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/calculators/calculator.py\", line 709, in get_potential_energy\n energy = self.get_property('energy', atoms)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/calculators/calculator.py\", line 737, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/m3gnet/models/_dynamics.py\", line 86, in calculate\n results = self.potential.get_efs_tensor(graph_list, include_stresses=self.compute_stress)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/m3gnet/models/_base.py\", line 188, in get_efs_tensor\n derivatives = tape.gradient(energies, derivatives)\nNode: 'gradients/m3g_net/graph_network_layer/gated_atom_update/UnsortedSegmentSum_grad/and'\nNo registered 'BroadcastTo' OpKernel for 'GPU' devices compatible with node {{node gradients/m3g_net/graph_network_layer/gated_atom_update/UnsortedSegmentSum_grad/and}}\n\t (OpKernel was found, but attributes didn't match) Requested Attributes: T=DT_BOOL, Tidx=DT_INT32, _XlaHasReferenceVars=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"\n\t. Registered: device='XLA_CPU_JIT'; Tidx in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, 16005131165644881776, DT_UINT16, DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64]\n device='GPU'; T in [DT_FLOAT]\n device='DEFAULT'; T in [DT_INT32]\n device='CPU'; T in [DT_UINT64]\n device='CPU'; T in [DT_INT64]\n device='CPU'; T in [DT_UINT32]\n device='CPU'; T in [DT_UINT16]\n device='CPU'; T in [DT_INT16]\n device='CPU'; T in [DT_UINT8]\n device='CPU'; T in [DT_INT8]\n device='CPU'; T in [DT_INT32]\n device='CPU'; T in [DT_HALF]\n device='CPU'; T in [DT_BFLOAT16]\n device='CPU'; T in [DT_FLOAT]\n device='CPU'; T in [DT_DOUBLE]\n device='CPU'; T in [DT_COMPLEX64]\n device='CPU'; T in [DT_COMPLEX128]\n device='CPU'; T in [DT_BOOL]\n device='CPU'; T in [DT_STRING]\n device='CPU'; T in [DT_RESOURCE]\n device='CPU'; T in [DT_VARIANT]\n\n\t [[PartitionedCall/gradients/m3g_net/graph_network_layer/gated_atom_update/UnsortedSegmentSum_grad/and]] [Op:__inference_get_efs_tensor_7426]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNotFoundError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/m3gnet/models/_dynamics.py:167\u001b[0m, in \u001b[0;36mRelaxer.relax\u001b[0;34m(self, atoms, fmax, steps, traj_file, interval, verbose, **kwargs)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrelax_cell:\n\u001b[1;32m 166\u001b[0m atoms \u001b[39m=\u001b[39m ExpCellFilter(atoms)\n\u001b[0;32m--> 167\u001b[0m optimizer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mopt_class(atoms, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 168\u001b[0m optimizer\u001b[39m.\u001b[39mattach(obs, interval\u001b[39m=\u001b[39minterval)\n\u001b[1;32m 169\u001b[0m optimizer\u001b[39m.\u001b[39mrun(fmax\u001b[39m=\u001b[39mfmax, steps\u001b[39m=\u001b[39msteps)\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/optimize/fire.py:54\u001b[0m, in \u001b[0;36mFIRE.__init__\u001b[0;34m(self, atoms, restart, logfile, trajectory, dt, maxstep, maxmove, dtmax, Nmin, finc, fdec, astart, fa, a, master, downhill_check, position_reset_callback, force_consistent)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, atoms, restart\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, logfile\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m-\u001b[39m\u001b[39m'\u001b[39m, trajectory\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 9\u001b[0m dt\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m, maxstep\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, maxmove\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, dtmax\u001b[39m=\u001b[39m\u001b[39m1.0\u001b[39m, Nmin\u001b[39m=\u001b[39m\u001b[39m5\u001b[39m,\n\u001b[1;32m 10\u001b[0m finc\u001b[39m=\u001b[39m\u001b[39m1.1\u001b[39m, fdec\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m,\n\u001b[1;32m 11\u001b[0m astart\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m, fa\u001b[39m=\u001b[39m\u001b[39m0.99\u001b[39m, a\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m, master\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, downhill_check\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m 12\u001b[0m position_reset_callback\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, force_consistent\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m 13\u001b[0m \u001b[39m\"\"\"Parameters:\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \n\u001b[1;32m 15\u001b[0m \u001b[39m atoms: Atoms object\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[39m when downhill_check is True.\u001b[39;00m\n\u001b[1;32m 53\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 54\u001b[0m Optimizer\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(\u001b[39mself\u001b[39;49m, atoms, restart, logfile, trajectory,\n\u001b[1;32m 55\u001b[0m master, force_consistent\u001b[39m=\u001b[39;49mforce_consistent)\n\u001b[1;32m 57\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdt \u001b[39m=\u001b[39m dt\n\u001b[1;32m 59\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mNsteps \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/optimize/optimize.py:234\u001b[0m, in \u001b[0;36mOptimizer.__init__\u001b[0;34m(self, atoms, restart, logfile, trajectory, master, append_trajectory, force_consistent)\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mforce_consistent \u001b[39m=\u001b[39m force_consistent\n\u001b[1;32m 233\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mforce_consistent \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 234\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_force_consistent()\n\u001b[1;32m 236\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrestart \u001b[39m=\u001b[39m restart\n\u001b[1;32m 238\u001b[0m \u001b[39m# initialize attribute\u001b[39;00m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/optimize/optimize.py:325\u001b[0m, in \u001b[0;36mOptimizer.set_force_consistent\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 322\u001b[0m \u001b[39m\"\"\"Automatically sets force_consistent to True if force_consistent\u001b[39;00m\n\u001b[1;32m 323\u001b[0m \u001b[39menergies are supported by calculator; else False.\"\"\"\u001b[39;00m\n\u001b[1;32m 324\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 325\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49matoms\u001b[39m.\u001b[39;49mget_potential_energy(force_consistent\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m 326\u001b[0m \u001b[39mexcept\u001b[39;00m PropertyNotImplementedError:\n\u001b[1;32m 327\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mforce_consistent \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/constraints.py:2420\u001b[0m, in \u001b[0;36mUnitCellFilter.get_potential_energy\u001b[0;34m(self, force_consistent)\u001b[0m\n\u001b[1;32m 2416\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_potential_energy\u001b[39m(\u001b[39mself\u001b[39m, force_consistent\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[1;32m 2417\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 2418\u001b[0m \u001b[39m returns potential energy including enthalpy PV term.\u001b[39;00m\n\u001b[1;32m 2419\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2420\u001b[0m atoms_energy \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49matoms\u001b[39m.\u001b[39;49mget_potential_energy(\n\u001b[1;32m 2421\u001b[0m force_consistent\u001b[39m=\u001b[39;49mforce_consistent)\n\u001b[1;32m 2422\u001b[0m \u001b[39mreturn\u001b[39;00m atoms_energy \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscalar_pressure \u001b[39m*\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39matoms\u001b[39m.\u001b[39mget_volume()\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/atoms.py:728\u001b[0m, in \u001b[0;36mAtoms.get_potential_energy\u001b[0;34m(self, force_consistent, apply_constraint)\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mAtoms object has no calculator.\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 727\u001b[0m \u001b[39mif\u001b[39;00m force_consistent:\n\u001b[0;32m--> 728\u001b[0m energy \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_calc\u001b[39m.\u001b[39;49mget_potential_energy(\n\u001b[1;32m 729\u001b[0m \u001b[39mself\u001b[39;49m, force_consistent\u001b[39m=\u001b[39;49mforce_consistent)\n\u001b[1;32m 730\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 731\u001b[0m energy \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_calc\u001b[39m.\u001b[39mget_potential_energy(\u001b[39mself\u001b[39m)\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/calculators/calculator.py:709\u001b[0m, in \u001b[0;36mCalculator.get_potential_energy\u001b[0;34m(self, atoms, force_consistent)\u001b[0m\n\u001b[1;32m 708\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_potential_energy\u001b[39m(\u001b[39mself\u001b[39m, atoms\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, force_consistent\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m--> 709\u001b[0m energy \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_property(\u001b[39m'\u001b[39;49m\u001b[39menergy\u001b[39;49m\u001b[39m'\u001b[39;49m, atoms)\n\u001b[1;32m 710\u001b[0m \u001b[39mif\u001b[39;00m force_consistent:\n\u001b[1;32m 711\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mfree_energy\u001b[39m\u001b[39m'\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresults:\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/calculators/calculator.py:737\u001b[0m, in \u001b[0;36mCalculator.get_property\u001b[0;34m(self, name, atoms, allow_calculation)\u001b[0m\n\u001b[1;32m 735\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m allow_calculation:\n\u001b[1;32m 736\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m--> 737\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcalculate(atoms, [name], system_changes)\n\u001b[1;32m 739\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresults:\n\u001b[1;32m 740\u001b[0m \u001b[39m# For some reason the calculator was not able to do what we want,\u001b[39;00m\n\u001b[1;32m 741\u001b[0m \u001b[39m# and that is OK.\u001b[39;00m\n\u001b[1;32m 742\u001b[0m \u001b[39mraise\u001b[39;00m PropertyNotImplementedError(\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m not present in this \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 743\u001b[0m \u001b[39m'\u001b[39m\u001b[39mcalculation\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(name))\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/m3gnet/models/_dynamics.py:86\u001b[0m, in \u001b[0;36mM3GNetCalculator.calculate\u001b[0;34m(self, atoms, properties, system_changes)\u001b[0m\n\u001b[1;32m 84\u001b[0m graph \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpotential\u001b[39m.\u001b[39mgraph_converter(atoms)\n\u001b[1;32m 85\u001b[0m graph_list \u001b[39m=\u001b[39m graph\u001b[39m.\u001b[39mas_tf()\u001b[39m.\u001b[39mas_list()\n\u001b[0;32m---> 86\u001b[0m results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpotential\u001b[39m.\u001b[39;49mget_efs_tensor(graph_list, include_stresses\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcompute_stress)\n\u001b[1;32m 87\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresults\u001b[39m.\u001b[39mupdate(\n\u001b[1;32m 88\u001b[0m energy\u001b[39m=\u001b[39mresults[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mnumpy()\u001b[39m.\u001b[39mravel(),\n\u001b[1;32m 89\u001b[0m free_energy\u001b[39m=\u001b[39mresults[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mnumpy()\u001b[39m.\u001b[39mravel(),\n\u001b[1;32m 90\u001b[0m forces\u001b[39m=\u001b[39mresults[\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mnumpy(),\n\u001b[1;32m 91\u001b[0m )\n\u001b[1;32m 92\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcompute_stress:\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:153\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 152\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[0;32m--> 153\u001b[0m \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[1;32m 154\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 155\u001b[0m \u001b[39mdel\u001b[39;00m filtered_tb\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 54\u001b[0m tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39mTFE_Py_Execute(ctx\u001b[39m.\u001b[39m_handle, device_name, op_name,\n\u001b[1;32m 55\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 56\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 57\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", + "\u001b[0;31mNotFoundError\u001b[0m: Graph execution error:\n\nDetected at node 'gradients/m3g_net/graph_network_layer/gated_atom_update/UnsortedSegmentSum_grad/and' defined at (most recent call last):\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n return _run_code(code, main_globals, None,\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/runpy.py\", line 86, in _run_code\n exec(code, run_globals)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel_launcher.py\", line 17, in \n app.launch_new_instance()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/traitlets/config/application.py\", line 976, in launch_instance\n app.start()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/kernelapp.py\", line 712, in start\n self.io_loop.start()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/tornado/platform/asyncio.py\", line 215, in start\n self.asyncio_loop.run_forever()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/asyncio/base_events.py\", line 600, in run_forever\n self._run_once()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/asyncio/base_events.py\", line 1896, in _run_once\n handle._run()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/asyncio/events.py\", line 80, in _run\n self._context.run(self._callback, *self._args)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 510, in dispatch_queue\n await self.process_one()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 499, in process_one\n await dispatch(*args)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 406, in dispatch_shell\n await result\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 730, in execute_request\n reply_content = await reply_content\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 383, in do_execute\n res = shell.run_cell(\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ipykernel/zmqshell.py\", line 528, in run_cell\n return super().run_cell(*args, **kwargs)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 2881, in run_cell\n result = self._run_cell(\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 2936, in _run_cell\n return runner(coro)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n coro.send(None)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3135, in run_cell_async\n has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3338, in run_ast_nodes\n if await self.run_code(code, result, async_=asy):\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3398, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"/var/folders/5m/sb0wx83j0m36xk_zswkfnffh0000gn/T/ipykernel_9555/3879703908.py\", line 2, in \n get_ipython().run_line_magic('time', 'relax_results = relaxer.relax(lfp_strained)')\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 2305, in run_line_magic\n result = fn(*args, **kwargs)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/IPython/core/magics/execution.py\", line 1316, in time\n exec(code, glob, local_ns)\n File \"\", line 1, in \n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/m3gnet/models/_dynamics.py\", line 167, in relax\n optimizer = self.opt_class(atoms, **kwargs)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/optimize/fire.py\", line 54, in __init__\n Optimizer.__init__(self, atoms, restart, logfile, trajectory,\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/optimize/optimize.py\", line 234, in __init__\n self.set_force_consistent()\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/optimize/optimize.py\", line 325, in set_force_consistent\n self.atoms.get_potential_energy(force_consistent=True)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/constraints.py\", line 2420, in get_potential_energy\n atoms_energy = self.atoms.get_potential_energy(\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/atoms.py\", line 728, in get_potential_energy\n energy = self._calc.get_potential_energy(\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/calculators/calculator.py\", line 709, in get_potential_energy\n energy = self.get_property('energy', atoms)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/ase/calculators/calculator.py\", line 737, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/m3gnet/models/_dynamics.py\", line 86, in calculate\n results = self.potential.get_efs_tensor(graph_list, include_stresses=self.compute_stress)\n File \"/opt/homebrew/Caskroom/miniconda/base/envs/m3gnet/lib/python3.10/site-packages/m3gnet/models/_base.py\", line 188, in get_efs_tensor\n derivatives = tape.gradient(energies, derivatives)\nNode: 'gradients/m3g_net/graph_network_layer/gated_atom_update/UnsortedSegmentSum_grad/and'\nNo registered 'BroadcastTo' OpKernel for 'GPU' devices compatible with node {{node gradients/m3g_net/graph_network_layer/gated_atom_update/UnsortedSegmentSum_grad/and}}\n\t (OpKernel was found, but attributes didn't match) Requested Attributes: T=DT_BOOL, Tidx=DT_INT32, _XlaHasReferenceVars=false, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"\n\t. Registered: device='XLA_CPU_JIT'; Tidx in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, 16005131165644881776, DT_UINT16, DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64]\n device='GPU'; T in [DT_FLOAT]\n device='DEFAULT'; T in [DT_INT32]\n device='CPU'; T in [DT_UINT64]\n device='CPU'; T in [DT_INT64]\n device='CPU'; T in [DT_UINT32]\n device='CPU'; T in [DT_UINT16]\n device='CPU'; T in [DT_INT16]\n device='CPU'; T in [DT_UINT8]\n device='CPU'; T in [DT_INT8]\n device='CPU'; T in [DT_INT32]\n device='CPU'; T in [DT_HALF]\n device='CPU'; T in [DT_BFLOAT16]\n device='CPU'; T in [DT_FLOAT]\n device='CPU'; T in [DT_DOUBLE]\n device='CPU'; T in [DT_COMPLEX64]\n device='CPU'; T in [DT_COMPLEX128]\n device='CPU'; T in [DT_BOOL]\n device='CPU'; T in [DT_STRING]\n device='CPU'; T in [DT_RESOURCE]\n device='CPU'; T in [DT_VARIANT]\n\n\t [[PartitionedCall/gradients/m3g_net/graph_network_layer/gated_atom_update/UnsortedSegmentSum_grad/and]] [Op:__inference_get_efs_tensor_7426]" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FIRE: 1 15:12:32 -181.340408* 96.6008\n", - "FIRE: 2 15:12:32 -188.431458* 51.0341\n", - "FIRE: 3 15:12:32 -189.647415* 5.0530\n", - "FIRE: 4 15:12:32 -189.025314* 38.3522\n", - "FIRE: 5 15:12:33 -189.307358* 31.2309\n", - "FIRE: 6 15:12:33 -189.671722* 17.7556\n", - "FIRE: 7 15:12:33 -189.856201* 3.1567\n", - "FIRE: 8 15:12:33 -189.775055* 15.3738\n", - "FIRE: 9 15:12:33 -189.790939* 14.5070\n", - "FIRE: 10 15:12:33 -189.819687* 12.8315\n", - "FIRE: 11 15:12:33 -189.856003* 10.4586\n", - "FIRE: 12 15:12:33 -189.893524* 7.5417\n", - "FIRE: 13 15:12:33 -189.926361* 4.2685\n", - "FIRE: 14 15:12:33 -189.950577* 2.9155\n", - "FIRE: 15 15:12:33 -189.965820* 2.8362\n", - "FIRE: 16 15:12:34 -189.976242* 5.1841\n", - "FIRE: 17 15:12:34 -189.977158* 5.1106\n", - "FIRE: 18 15:12:34 -189.978989* 4.9645\n", - "FIRE: 19 15:12:34 -189.981644* 4.7485\n", - "FIRE: 20 15:12:34 -189.985092* 4.4661\n", - "FIRE: 21 15:12:34 -189.989227* 4.1226\n", - "FIRE: 22 15:12:34 -189.993958* 3.7245\n", - "FIRE: 23 15:12:34 -189.999207* 3.2799\n", - "FIRE: 24 15:12:34 -190.005478* 2.7457\n", - "FIRE: 25 15:12:34 -190.012878* 2.7087\n", - "FIRE: 26 15:12:35 -190.021469* 2.6941\n", - "FIRE: 27 15:12:35 -190.031372* 2.6743\n", - "FIRE: 28 15:12:35 -190.042816* 2.6478\n", - "FIRE: 29 15:12:35 -190.056198* 2.6124\n", - "FIRE: 30 15:12:35 -190.072144* 2.5658\n", - "FIRE: 31 15:12:35 -190.091476* 2.5057\n", - "FIRE: 32 15:12:35 -190.114990* 2.4296\n", - "FIRE: 33 15:12:35 -190.143097* 2.3354\n", - "FIRE: 34 15:12:35 -190.175598* 2.2213\n", - "FIRE: 35 15:12:35 -190.211670* 2.0866\n", - "FIRE: 36 15:12:35 -190.250824* 1.9316\n", - "FIRE: 37 15:12:36 -190.293610* 1.8501\n", - "FIRE: 38 15:12:36 -190.340302* 1.5647\n", - "FIRE: 39 15:12:36 -190.388489* 1.3525\n", - "FIRE: 40 15:12:36 -190.434357* 1.2614\n", - "FIRE: 41 15:12:36 -190.477478* 1.3377\n", - "FIRE: 42 15:12:36 -190.515076* 0.6857\n", - "FIRE: 43 15:12:36 -190.542282* 1.3668\n", - "FIRE: 44 15:12:36 -190.560364* 0.6583\n", - "FIRE: 45 15:12:36 -190.567139* 1.4170\n", - "FIRE: 46 15:12:36 -190.568558* 1.0647\n", - "FIRE: 47 15:12:36 -190.570847* 0.8340\n", - "FIRE: 48 15:12:36 -190.573517* 0.8014\n", - "FIRE: 49 15:12:37 -190.576538* 0.7826\n", - "FIRE: 50 15:12:37 -190.580292* 0.9529\n", - "FIRE: 51 15:12:37 -190.584900* 0.7980\n", - "FIRE: 52 15:12:37 -190.589996* 0.6077\n", - "FIRE: 53 15:12:37 -190.595657* 0.5409\n", - "FIRE: 54 15:12:37 -190.601852* 0.7929\n", - "FIRE: 55 15:12:37 -190.609085* 0.6564\n", - "FIRE: 56 15:12:37 -190.616974* 0.3803\n", - "FIRE: 57 15:12:37 -190.624756* 0.6656\n", - "FIRE: 58 15:12:37 -190.632980* 0.3499\n", - "FIRE: 59 15:12:37 -190.640747* 0.6661\n", - "FIRE: 60 15:12:38 -190.648621* 0.3273\n", - "FIRE: 61 15:12:38 -190.656113* 0.8515\n", - "FIRE: 62 15:12:38 -190.664642* 0.3345\n", - "FIRE: 63 15:12:38 -190.673843* 0.4768\n", - "FIRE: 64 15:12:38 -190.683670* 1.1790\n", - "FIRE: 65 15:12:38 -190.693268* 1.8485\n", - "FIRE: 66 15:12:38 -190.695587* 0.4513\n", - "FIRE: 67 15:12:38 -190.695633* 1.2318\n", - "FIRE: 68 15:12:38 -190.696014* 1.0090\n", - "FIRE: 69 15:12:38 -190.696579* 0.6041\n", - "FIRE: 70 15:12:38 -190.697052* 0.2502\n", - "FIRE: 71 15:12:38 -190.697327* 0.3880\n", - "FIRE: 72 15:12:39 -190.697571* 0.7088\n", - "FIRE: 73 15:12:39 -190.697998* 0.8129\n", - "FIRE: 74 15:12:39 -190.698639* 0.7013\n", - "FIRE: 75 15:12:39 -190.699509* 0.3706\n", - "FIRE: 76 15:12:39 -190.700363* 0.2268\n", - "FIRE: 77 15:12:39 -190.701141* 0.5390\n", - "FIRE: 78 15:12:39 -190.702133* 0.6409\n", - "FIRE: 79 15:12:39 -190.703506* 0.3131\n", - "FIRE: 80 15:12:40 -190.704834* 0.3061\n", - "FIRE: 81 15:12:40 -190.706192* 0.5611\n", - "FIRE: 82 15:12:40 -190.707977* 0.1652\n", - "FIRE: 83 15:12:40 -190.709595* 0.5395\n", - "FIRE: 84 15:12:40 -190.711655* 0.2361\n", - "FIRE: 85 15:12:40 -190.713608* 0.6661\n", - "FIRE: 86 15:12:40 -190.716309* 0.1495\n", - "FIRE: 87 15:12:40 -190.719055* 0.5651\n", - "FIRE: 88 15:12:40 -190.722122* 0.9481\n", - "FIRE: 89 15:12:40 -190.725540* 1.2047\n", - "FIRE: 90 15:12:40 -190.726501* 0.2344\n", - "FIRE: 91 15:12:40 -190.726303* 0.9098\n", - "FIRE: 92 15:12:41 -190.726501* 0.7271\n", - "FIRE: 93 15:12:41 -190.726776* 0.3996\n", - "FIRE: 94 15:12:41 -190.726944* 0.1152\n", - "FIRE: 95 15:12:41 -190.726990* 0.3586\n", - "FIRE: 96 15:12:41 -190.727005* 0.3403\n", - "FIRE: 97 15:12:41 -190.727036* 0.3045\n", - "FIRE: 98 15:12:41 -190.727066* 0.2533\n", - "FIRE: 99 15:12:41 -190.727112* 0.1895\n", - "FIRE: 100 15:12:41 -190.727158* 0.1214\n", - "FIRE: 101 15:12:41 -190.727203* 0.1119\n", - "FIRE: 102 15:12:42 -190.727249* 0.1089\n", - "FIRE: 103 15:12:42 -190.727310* 0.1087\n", - "FIRE: 104 15:12:42 -190.727386* 0.1568\n", - "FIRE: 105 15:12:42 -190.727478* 0.1901\n", - "FIRE: 106 15:12:42 -190.727585* 0.1909\n", - "FIRE: 107 15:12:42 -190.727722* 0.1526\n", - "FIRE: 108 15:12:42 -190.727905* 0.1071\n", - "FIRE: 109 15:12:42 -190.728104* 0.1064\n", - "FIRE: 110 15:12:42 -190.728348* 0.1129\n", - "FIRE: 111 15:12:43 -190.728638* 0.1420\n", - "FIRE: 112 15:12:43 -190.728989* 0.1039\n", - "FIRE: 113 15:12:43 -190.729416* 0.1028\n", - "FIRE: 114 15:12:43 -190.729904* 0.1271\n", - "FIRE: 115 15:12:43 -190.730484* 0.0999\n", - "CPU times: user 33.1 s, sys: 1min 14s, total: 1min 47s\n", - "Wall time: 15.1 s\n" + "ename": "NameError", + "evalue": "name 'relax_results' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/janosh/dev/m3gnet/examples/Relaxation of LiFePO4.ipynb Cell 5\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m relaxer \u001b[39m=\u001b[39m Relaxer()\n\u001b[1;32m 2\u001b[0m get_ipython()\u001b[39m.\u001b[39mrun_line_magic(\u001b[39m'\u001b[39m\u001b[39mtime\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mrelax_results = relaxer.relax(lfp_strained)\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m relaxed \u001b[39m=\u001b[39m relax_results[\u001b[39m\"\u001b[39m\u001b[39mfinal_structure\u001b[39m\u001b[39m\"\u001b[39m]\n", + "\u001b[0;31mNameError\u001b[0m: name 'relax_results' is not defined" ] } ], "source": [ "relaxer = Relaxer()\n", - "%time relax_results = relaxer.relax(lfp_strained)\n", - "relaxed = relax_results['final_structure']" + "%time relax_results = relaxer.relax(lfp_strained)\n", + "relaxed = relax_results[\"final_structure\"]" ] }, { @@ -241,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "id": "2abb1e07", "metadata": {}, "outputs": [ @@ -249,16 +166,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Original lattice parameters are (4.746441000202679, 10.443730003189714, 6.090226001838521)\n", - "Strained lattice parameters are (4.750053179813627, 10.356606838609148, 5.997110481663704)\n", - "Relaxed lattice parameters are (4.747618545215528, 10.481805968108853, 6.107802122731116)\n" + "Original lattice parameters are [4.746, 10.444, 6.09]\n", + "Strained lattice parameters are [4.751, 10.912, 5.849]\n", + "Relaxed lattice parameters are [4.75, 10.444, 6.106]\n" ] } ], "source": [ - "print(f\"Original lattice parameters are {lfp.lattice.abc}\")\n", - "print(f\"Strained lattice parameters are {lfp_strained.lattice.abc}\")\n", - "print(f\"Relaxed lattice parameters are {relaxed.lattice.abc}\")" + "print(f\"Original lattice parameters are {[round(x, 3) for x in lfp.lattice.abc]}\")\n", + "print(f\"Strained lattice parameters are {[round(x, 3) for x in lfp_strained.lattice.abc]}\")\n", + "print(f\"Relaxed lattice parameters are {[round(x, 3) for x in relaxed.lattice.abc]}\")" ] }, { @@ -272,34 +189,34 @@ "output_type": "stream", "text": [ "Diff in fractional coords:\n", - "[[-3.92604728e-03 4.07413997e-03 5.80356625e-03]\n", - " [ 1.43250511e-02 3.27431932e-03 4.42699735e-03]\n", - " [-3.52458018e-03 -3.08654170e-03 1.07293845e-02]\n", - " [-4.26860840e-03 4.58356758e-03 -2.96369922e-03]\n", - " [-5.55072174e-03 1.54835105e-03 2.47354774e-03]\n", - " [ 3.80921860e-03 4.32305748e-03 5.12550707e-03]\n", - " [ 3.53915883e-03 -8.38400924e-04 9.06917571e-03]\n", - " [ 2.47973569e-03 -8.64836750e-04 3.59526770e-03]\n", - " [-7.26667302e-05 -1.35534223e-03 -2.53138511e-03]\n", - " [ 1.14946368e-03 -4.82260909e-05 7.54579410e-03]\n", - " [ 4.24883341e-03 6.67052939e-04 3.70532881e-03]\n", - " [ 9.17448738e-04 3.24917845e-03 3.69315336e-04]\n", - " [ 5.82139415e-04 -3.18889133e-03 -3.03101342e-03]\n", - " [ 2.23741485e-03 2.38577303e-03 -3.57929180e-04]\n", - " [ 3.79457349e-03 -3.22175818e-04 1.67082825e-03]\n", - " [ 2.20562748e-04 5.94968752e-03 -1.96480681e-03]\n", - " [ 5.03072366e-03 5.13620895e-04 -6.28382551e-04]\n", - " [ 5.79797481e-03 3.02933989e-03 -2.87900284e-03]\n", - " [-5.31171473e-03 -6.63377559e-04 2.84482729e-03]\n", - " [-2.25493932e-04 2.51091086e-05 1.09263063e-02]\n", - " [-6.38000214e-04 -2.42169480e-03 -4.34413563e-03]\n", - " [ 8.37377953e-03 -2.47021565e-03 8.68791120e-03]\n", - " [-1.95083290e-03 -4.47534529e-04 7.58124443e-03]\n", - " [ 4.07845373e-03 2.81597502e-03 -4.31561765e-03]\n", - " [ 1.34242194e-03 -1.40126703e-03 7.71843519e-03]\n", - " [ 3.04241735e-04 2.76705060e-04 2.43693781e-03]\n", - " [-3.16574374e-03 3.20719941e-03 3.18210886e-03]\n", - " [ 3.91850131e-03 4.19336033e-03 5.22481774e-03]]\n" + "[[-0.00387997 -0.00279335 -0.00278419]\n", + " [-0.0105322 0.00017964 -0.00350815]\n", + " [ 0.00567224 -0.00023636 -0.00763473]\n", + " [ 0.00193715 0.00778645 -0.0020107 ]\n", + " [ 0.00225437 0.00170045 0.00172863]\n", + " [ 0.00097444 0.00094502 0.00388633]\n", + " [ 0.00345818 0.00368189 0.0024407 ]\n", + " [-0.00038704 0.00417402 0.00208854]\n", + " [ 0.00349925 0.00451124 -0.00329377]\n", + " [ 0.00301799 0.00162616 0.00426465]\n", + " [-0.00035351 0.0040381 0.00160278]\n", + " [-0.00239293 0.00165153 -0.00127454]\n", + " [ 0.00438634 0.00427503 -0.00071235]\n", + " [ 0.00422623 0.002672 0.00449112]\n", + " [-0.00079353 0.0043336 -0.00153869]\n", + " [-0.0033543 0.00193773 -0.00280839]\n", + " [-0.00151142 0.00393776 -0.00398973]\n", + " [-0.00481124 0.00160034 -0.00323102]\n", + " [ 0.00471888 0.0048146 -0.00417542]\n", + " [ 0.0007874 0.00072707 0.00049835]\n", + " [ 0.0041545 0.00410332 -0.00345285]\n", + " [ 0.00412935 0.0025092 0.00449315]\n", + " [ 0.00459609 0.0002639 0.00462091]\n", + " [ 0.00272673 0.00434772 -0.00369026]\n", + " [ 0.00018383 0.00028395 0.00456975]\n", + " [-0.0012521 0.00108056 0.00068582]\n", + " [-0.00093808 0.00435831 0.00110198]\n", + " [ 0.0003874 0.00588312 0.0040137 ]]\n" ] } ], @@ -314,19 +231,11 @@ "source": [ "Quite clealy, the relaxation using the M3GNet universal IAP has brought the lattice parameters much closer to the original DFT one and the coordinates are also within $10^{-3}$ of the original fractional coordinates." ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "770d7aa2", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.10.4 64-bit ('m3gnet')", "language": "python", "name": "python3" }, @@ -340,7 +249,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.4" + }, + "vscode": { + "interpreter": { + "hash": "95e5cb0dae99d6911ce19b14750141ca4ad955f9302739260772daefb06054c0" + } } }, "nbformat": 4, diff --git a/m3gnet/graph/_batch.py b/m3gnet/graph/_batch.py index ec47759..a5b19f2 100644 --- a/m3gnet/graph/_batch.py +++ b/m3gnet/graph/_batch.py @@ -124,7 +124,7 @@ def _concatenate(list_of_arrays: List, name: AnyStr) -> Optional[np.ndarray]: Returns: concatenated array """ - num_none = sum([i is None for i in list_of_arrays]) + num_none = sum(i is None for i in list_of_arrays) if num_none == len(list_of_arrays): return None diff --git a/m3gnet/graph/_converters.py b/m3gnet/graph/_converters.py index 2f1b959..4ae39d9 100644 --- a/m3gnet/graph/_converters.py +++ b/m3gnet/graph/_converters.py @@ -86,7 +86,6 @@ def convert(self, structure: StructureOrMolecule, **kwargs) -> MaterialGraph: Returns: """ - pass def convert_many(self, structures: List[StructureOrMolecule], **kwargs) -> MaterialGraph: """ diff --git a/m3gnet/graph/_types.py b/m3gnet/graph/_types.py index f15ce30..02ad582 100644 --- a/m3gnet/graph/_types.py +++ b/m3gnet/graph/_types.py @@ -113,7 +113,6 @@ def _check_graph(self) -> None: """ Check if the graph is valid """ - pass def replace(self, **kwargs): """ diff --git a/m3gnet/layers/_core.py b/m3gnet/layers/_core.py index 82f4e1a..fe2955d 100644 --- a/m3gnet/layers/_core.py +++ b/m3gnet/layers/_core.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ Core layers provide basic operations, e.g., MLP """ diff --git a/m3gnet/layers/tests/data/test_NaCl.json b/m3gnet/layers/tests/data/test_NaCl.json index 558eaa7..ae10166 100644 --- a/m3gnet/layers/tests/data/test_NaCl.json +++ b/m3gnet/layers/tests/data/test_NaCl.json @@ -729,4 +729,4 @@ ] ] ] -] \ No newline at end of file +] diff --git a/m3gnet/layers/tests/test_aggregate.py b/m3gnet/layers/tests/test_aggregate.py index f53c800..841c1ad 100644 --- a/m3gnet/layers/tests/test_aggregate.py +++ b/m3gnet/layers/tests/test_aggregate.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import unittest import numpy as np diff --git a/m3gnet/layers/tests/test_atom_ref.py b/m3gnet/layers/tests/test_atom_ref.py index 1ac2637..6ecfa9a 100644 --- a/m3gnet/layers/tests/test_atom_ref.py +++ b/m3gnet/layers/tests/test_atom_ref.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import unittest import numpy as np diff --git a/m3gnet/layers/tests/test_base.py b/m3gnet/layers/tests/test_base.py index ed8ae1a..440faaf 100644 --- a/m3gnet/layers/tests/test_base.py +++ b/m3gnet/layers/tests/test_base.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import unittest import numpy as np diff --git a/m3gnet/layers/tests/test_basis.py b/m3gnet/layers/tests/test_basis.py index 9eb6da0..a2cc1eb 100644 --- a/m3gnet/layers/tests/test_basis.py +++ b/m3gnet/layers/tests/test_basis.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import unittest import numpy as np diff --git a/m3gnet/layers/tests/test_bond.py b/m3gnet/layers/tests/test_bond.py index 49f427d..f599a6f 100644 --- a/m3gnet/layers/tests/test_bond.py +++ b/m3gnet/layers/tests/test_bond.py @@ -1,11 +1,10 @@ -# -*- coding: utf-8 -*- import unittest import numpy as np from pymatgen.core import Lattice, Molecule, Structure -from m3gnet.graph import Index, RadiusCutoffGraphConverter, tf_compute_distance_angle -from m3gnet.layers import ConcatAtoms, PairRadialBasisExpansion, ThreeDInteraction +from m3gnet.graph import Index, RadiusCutoffGraphConverter +from m3gnet.layers import PairRadialBasisExpansion class TestAgg(unittest.TestCase): diff --git a/m3gnet/layers/tests/test_core.py b/m3gnet/layers/tests/test_core.py index e7dfc13..919bfe4 100644 --- a/m3gnet/layers/tests/test_core.py +++ b/m3gnet/layers/tests/test_core.py @@ -4,7 +4,7 @@ import tensorflow as tf from pymatgen.core import Lattice, Structure -from m3gnet.graph import MaterialGraph, RadiusCutoffGraphConverter +from m3gnet.graph import RadiusCutoffGraphConverter from m3gnet.layers import MLP, Embedding, GatedMLP, Pipe diff --git a/m3gnet/layers/tests/test_gn.py b/m3gnet/layers/tests/test_gn.py index 9e6e5a3..b8c9fd6 100644 --- a/m3gnet/layers/tests/test_gn.py +++ b/m3gnet/layers/tests/test_gn.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import unittest import numpy as np diff --git a/m3gnet/layers/tests/test_readout.py b/m3gnet/layers/tests/test_readout.py index 202093c..461a9a3 100644 --- a/m3gnet/layers/tests/test_readout.py +++ b/m3gnet/layers/tests/test_readout.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import json import os import unittest diff --git a/m3gnet/models/_m3gnet.py b/m3gnet/models/_m3gnet.py index 61bcedd..707761b 100644 --- a/m3gnet/models/_m3gnet.py +++ b/m3gnet/models/_m3gnet.py @@ -329,7 +329,7 @@ def from_dir(cls, dirname: str, custom_objects: Optional[dict] = None) -> "M3GNe fname = os.path.join(dirname, MODEL_NAME + ".json") if not os.path.isfile(fname): raise ValueError("Model does not exists") - with open(fname, "r") as f: + with open(fname) as f: model_serialized = json.load(f) # model_serialized = _replace_compatibility(model_serialized) model = tf.keras.models.model_from_json(model_serialized, custom_objects=custom_objects) diff --git a/m3gnet/models/tests/test_model.py b/m3gnet/models/tests/test_model.py index d091984..3383fde 100644 --- a/m3gnet/models/tests/test_model.py +++ b/m3gnet/models/tests/test_model.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import os import unittest diff --git a/m3gnet/trainers/__init__.py b/m3gnet/trainers/__init__.py index d3909bb..1840e57 100644 --- a/m3gnet/trainers/__init__.py +++ b/m3gnet/trainers/__init__.py @@ -1,5 +1,4 @@ """M3GNet trainers""" -# -*- coding: utf-8 -*- from ._potential import PotentialTrainer from ._property import Trainer diff --git a/m3gnet/trainers/_property.py b/m3gnet/trainers/_property.py index 5a108db..76227d4 100644 --- a/m3gnet/trainers/_property.py +++ b/m3gnet/trainers/_property.py @@ -167,7 +167,7 @@ def train( pbar.set_params({"verbose": verbose, "epochs": epochs}) callbacks.append(pbar) - if len(callbacks) > len(set(i.__class__ for i in callbacks)): + if len(callbacks) > len({i.__class__ for i in callbacks}): logger.warning("Duplicated callbacks found") callback_list = tf.keras.callbacks.CallbackList(callbacks) diff --git a/m3gnet/trainers/tests/Li-O.json b/m3gnet/trainers/tests/Li-O.json index eff3154..c354e25 100644 --- a/m3gnet/trainers/tests/Li-O.json +++ b/m3gnet/trainers/tests/Li-O.json @@ -1 +1 @@ -{"mp-10173": {"structure": 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"Structure", "charge": null, "lattice": {"matrix": [[4.230595, 3.406919, 0.0], [-4.230595, 3.406919, 0.0], [0.0, 1.349883, 3.127495]], "a": 5.431853378597953, "b": 5.431853378597953, "c": 3.406377707875919, "alpha": 75.6081620899082, "beta": 75.6081620899082, "gamma": 102.31067082662148, "volume": 90.15501288202042}, "sites": [{"species": [{"element": "O", "occu": 1}], "abc": [0.774957, 0.113939, 0.647086], "xyz": [2.7964994457100003, 3.901887062362, 2.0237582295700003], "label": "O", "properties": {"magmom": 0.0}}, {"species": [{"element": "O", "occu": 1}], "abc": [0.886061, 0.225043, 0.852914], "xyz": [2.7964994457100003, 4.9367754376379995, 2.66748427043], "label": "O", "properties": {"magmom": 0.0}}, {"species": [{"element": "O", "occu": 1}], "abc": [0.225043, 0.886061, 0.352914], "xyz": [-2.7964994457100003, 4.2618339376379994, 1.10373677043], "label": "O", "properties": {"magmom": 0.0}}, {"species": [{"element": "O", "occu": 1}], "abc": [0.113939, 0.774957, 0.147086], "xyz": [-2.7964994457100003, 3.226945562362, 0.46001072957], "label": "O", "properties": {"magmom": 0.0}}, {"species": [{"element": "O", "occu": 1}], "abc": [0.717711, 0.753319, 0.827685], "xyz": [-0.15064302675999963, 6.128957967424999, 2.588580699075], "label": "O", "properties": {"magmom": -0.0}}, {"species": [{"element": "O", "occu": 1}], "abc": [0.246681, 0.282289, 0.672315], "xyz": [-0.15064302675999985, 2.709704532575, 2.102661800925], "label": "O", "properties": {"magmom": -0.0}}, {"species": [{"element": "O", "occu": 1}], "abc": [0.282289, 0.246681, 0.172315], "xyz": [0.15064302675999985, 2.034763032575, 0.538914300925], "label": "O", "properties": {"magmom": -0.0}}, {"species": [{"element": "O", "occu": 1}], "abc": [0.753319, 0.717711, 0.327685], "xyz": [0.15064302675999963, 5.454016467424999, 1.0248331990750001], "label": "O", "properties": {"magmom": -0.0}}]}, "energy": -33.43140082, "band_gap": 0.43019999999999997}, "mp-611836": {"structure": {"@module": "pymatgen.core.structure", "@class": "Structure", "charge": null, "lattice": {"matrix": [[1.766737, 2.797827, 0.0], [-1.766737, 2.797827, 0.0], [0.0, 2.076469, 3.735489]], "a": 3.308956867216313, "b": 3.308956867216313, "c": 4.273827509046428, "alpha": 65.7444382032942, "beta": 65.7444382032942, "gamma": 64.54216409093941, "volume": 36.92922714726946}, "sites": [{"species": [{"element": "O", "occu": 1}], "abc": [0.951806, 0.951806, 0.163979], "xyz": [0.0, 5.666474361275, 0.612541750731], "label": "O", "properties": {"magmom": -0.812}}, {"species": [{"element": "O", "occu": 1}], "abc": [0.048194, 0.048194, 0.836021], "xyz": [0.0, 2.005648638725, 3.122947249269], "label": "O", "properties": {"magmom": -0.812}}]}, "energy": -9.87596805, "band_gap": 1.3522999999999998}, "mp-1057818": {"structure": {"@module": "pymatgen.core.structure", "@class": "Structure", "charge": null, "lattice": {"matrix": [[0.0, 2.142855, 2.142855], [2.142855, 0.0, 2.142855], [2.142855, 2.142855, 0.0]], "a": 3.0304546031989985, "b": 3.0304546031989985, "c": 3.0304546031989985, "alpha": 59.99999999999999, "beta": 59.99999999999999, "gamma": 59.99999999999999, "volume": 19.679241253703353}, "sites": [{"species": [{"element": "O", "occu": 1}], "abc": [0.25, 0.25, 0.25], "xyz": [1.0714275, 1.0714275, 1.0714275], "label": "O", "properties": {"magmom": -0.0}}, {"species": [{"element": "O", "occu": 1}], "abc": [0.0, 0.0, 0.0], "xyz": [0.0, 0.0, 0.0], "label": "O", "properties": {"magmom": -0.0}}]}, "energy": -6.13209905, "band_gap": 0.0}, "mp-1065697": {"structure": {"@module": "pymatgen.core.structure", "@class": "Structure", "charge": null, "lattice": {"matrix": [[2.352226, 0.0, 0.0], [0.0, 2.352226, 0.0], [0.0, 0.0, 1.544743]], "a": 2.352226, "b": 2.352226, "c": 1.544743, "alpha": 90.0, "beta": 90.0, "gamma": 90.0, "volume": 8.547012282033565}, "sites": [{"species": [{"element": "O", "occu": 1}], "abc": [0.0, 0.0, 0.0], "xyz": [0.0, 0.0, 0.0], "label": "O", "properties": {"magmom": 0.0}}]}, "energy": -3.34714476, "band_gap": 0.0}} diff --git a/m3gnet/trainers/tests/test_trainer.py b/m3gnet/trainers/tests/test_trainer.py index 5e21e67..3bfe4bf 100644 --- a/m3gnet/trainers/tests/test_trainer.py +++ b/m3gnet/trainers/tests/test_trainer.py @@ -1,8 +1,6 @@ -# -*- coding: utf-8 -*- import json import os import unittest -from shutil import rmtree import numpy as np import tensorflow as tf diff --git a/m3gnet/utils/__init__.py b/m3gnet/utils/__init__.py index 3e63398..1cd5309 100644 --- a/m3gnet/utils/__init__.py +++ b/m3gnet/utils/__init__.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ Utilities for m3gnet """ diff --git a/m3gnet/utils/_math.py b/m3gnet/utils/_math.py index 0d3fc0d..064bdb6 100644 --- a/m3gnet/utils/_math.py +++ b/m3gnet/utils/_math.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ math functions and miscellaneous calculations """ diff --git a/m3gnet/utils/_tf.py b/m3gnet/utils/_tf.py index 722d28f..b9160ae 100644 --- a/m3gnet/utils/_tf.py +++ b/m3gnet/utils/_tf.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ Tensorflow related utility """ diff --git a/m3gnet/utils/tests/test_math.py b/m3gnet/utils/tests/test_math.py index b628d03..1d81a68 100644 --- a/m3gnet/utils/tests/test_math.py +++ b/m3gnet/utils/tests/test_math.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import unittest import numpy as np diff --git a/m3gnet/utils/tests/test_tf.py b/m3gnet/utils/tests/test_tf.py index 6b470b4..6faa7e9 100644 --- a/m3gnet/utils/tests/test_tf.py +++ b/m3gnet/utils/tests/test_tf.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import unittest import numpy as np diff --git a/pretrained/MP-2021.2.8-EFS/m3gnet.json b/pretrained/MP-2021.2.8-EFS/m3gnet.json index 97568fc..0087964 100644 --- a/pretrained/MP-2021.2.8-EFS/m3gnet.json +++ b/pretrained/MP-2021.2.8-EFS/m3gnet.json @@ -1 +1 @@ -"{\"class_name\": \"M3GNet\", \"config\": {\"name\": \"m3g_net\", \"max_n\": 3, \"max_l\": 3, \"n_blocks\": 3, \"units\": 64, \"cutoff\": 5.0, \"threebody_cutoff\": 4.0, \"include_states\": false, \"readout\": \"weighted_atom\", \"n_atom_types\": 94, \"task_type\": \"regression\", \"is_intensive\": false, \"mean\": 0.0, \"std\": 32.17109834917642, \"element_refs\": [0.0, -3.4843261911561028, -0.7929797687431355, -3.4573625172171165, -4.707765870771016, -8.011108016668768, -8.394581462422844, -7.750094492197886, -7.387677478973862, -4.946528796048113, -0.029288391672177685, -2.466164931685727, -2.0318155603403856, -5.122487716146491, -7.875463903751272, -6.908196189228034, -4.611218804437086, -3.027152379064371, -0.06279713634418577, -2.3532963902733193, -4.778241420229052, -8.066454839808204, -11.401003587685803, -8.927171971061405, -8.508408434441172, -8.170571408379823, -6.59277195296443, -5.234141518172957, -4.465330755509164, -3.296725580820661, -1.3526280480990915, -3.545950773762615, -4.6323977966864, -4.138259405833041, -3.6809167595262324, -2.840390787871695, 6.470248022224444, -2.230039686972397, -4.28467191958041, -10.288419163253234, -11.646994569088832, -11.897257027375439, -8.629010559078154, -9.868963036382208, -7.529920274034659, -5.566108785366125, -4.989987934464926, -1.8907720720017154, -0.7166445477628792, -2.7783827902612814, -3.8250237734025108, -3.3872574311610446, -2.540821374472112, -1.9316853365228492, 9.327723590340101, -2.631982538856448, -4.863763303187234, -8.941340884330346, -9.007528604842154, -7.925494962422752, -8.063533560475317, -6.535237679123891, -8.225441352062779, -12.392082038520687, -17.33760321554992, -7.479752317415126, -8.264982305581707, -8.248225450889509, -7.0921249481053525, -8.415686698628432, -3.419624756529247, -7.574452251703786, -12.67851552979807, -13.501372548858408, -9.474060607688976, -11.789707376091997, -9.746879324605413, -7.55581267880792, -5.675347011209681, -2.470084079873828, 0.3532971230155427, -2.3394321085720198, -3.5180003513248814, -3.1732941043540257, 1.0547455480334031e-16, -1.8536706833505882e-16, -5.78493996176134e-16, 1.3591855871025166e-15, 2.991253288696013e-28, -5.688410264859977, -10.404066708232437, -11.41133300856, -14.291848396754993, -14.633848842020615, -15.398398562127586]}, \"keras_version\": \"2.7.0\", \"backend\": \"tensorflow\"}" \ No newline at end of file +"{\"class_name\": \"M3GNet\", \"config\": {\"name\": \"m3g_net\", \"max_n\": 3, \"max_l\": 3, \"n_blocks\": 3, \"units\": 64, \"cutoff\": 5.0, \"threebody_cutoff\": 4.0, \"include_states\": false, \"readout\": \"weighted_atom\", \"n_atom_types\": 94, \"task_type\": \"regression\", \"is_intensive\": false, \"mean\": 0.0, \"std\": 32.17109834917642, \"element_refs\": [0.0, -3.4843261911561028, -0.7929797687431355, -3.4573625172171165, -4.707765870771016, -8.011108016668768, -8.394581462422844, -7.750094492197886, -7.387677478973862, -4.946528796048113, -0.029288391672177685, -2.466164931685727, -2.0318155603403856, -5.122487716146491, -7.875463903751272, -6.908196189228034, -4.611218804437086, -3.027152379064371, -0.06279713634418577, -2.3532963902733193, -4.778241420229052, -8.066454839808204, -11.401003587685803, -8.927171971061405, -8.508408434441172, -8.170571408379823, -6.59277195296443, -5.234141518172957, -4.465330755509164, -3.296725580820661, -1.3526280480990915, -3.545950773762615, -4.6323977966864, -4.138259405833041, -3.6809167595262324, -2.840390787871695, 6.470248022224444, -2.230039686972397, -4.28467191958041, -10.288419163253234, -11.646994569088832, -11.897257027375439, -8.629010559078154, -9.868963036382208, -7.529920274034659, -5.566108785366125, -4.989987934464926, -1.8907720720017154, -0.7166445477628792, -2.7783827902612814, -3.8250237734025108, -3.3872574311610446, -2.540821374472112, -1.9316853365228492, 9.327723590340101, -2.631982538856448, -4.863763303187234, -8.941340884330346, -9.007528604842154, -7.925494962422752, -8.063533560475317, -6.535237679123891, -8.225441352062779, -12.392082038520687, -17.33760321554992, -7.479752317415126, -8.264982305581707, -8.248225450889509, -7.0921249481053525, -8.415686698628432, -3.419624756529247, -7.574452251703786, -12.67851552979807, -13.501372548858408, -9.474060607688976, -11.789707376091997, -9.746879324605413, -7.55581267880792, -5.675347011209681, -2.470084079873828, 0.3532971230155427, -2.3394321085720198, -3.5180003513248814, -3.1732941043540257, 1.0547455480334031e-16, -1.8536706833505882e-16, -5.78493996176134e-16, 1.3591855871025166e-15, 2.991253288696013e-28, -5.688410264859977, -10.404066708232437, -11.41133300856, -14.291848396754993, -14.633848842020615, -15.398398562127586]}, \"keras_version\": \"2.7.0\", \"backend\": \"tensorflow\"}" diff --git a/pyproject.toml b/pyproject.toml index 5ffadd6..f2e7d3e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,4 +29,4 @@ exclude = ''' | test_files )/ ) -''' \ No newline at end of file +''' diff --git a/requirements-ci.txt b/requirements-ci.txt index 1ee547f..4afd345 100644 --- a/requirements-ci.txt +++ b/requirements-ci.txt @@ -8,4 +8,4 @@ pydocstyle black flake8 types-tabulate==0.8.3 -pylint \ No newline at end of file +pylint diff --git a/setup.cfg b/setup.cfg index 10ef400..1d7994d 100644 --- a/setup.cfg +++ b/setup.cfg @@ -16,4 +16,4 @@ ignore = BLK999, W503, BLK100 [mypy] ignore_missing_imports = True -exclude = pretrained, m3gnet.data-*, *.index, *.json \ No newline at end of file +exclude = pretrained, m3gnet.data-*, *.index, *.json diff --git a/tasks.py b/tasks.py index c478624..2df5ff1 100644 --- a/tasks.py +++ b/tasks.py @@ -7,7 +7,6 @@ import json import os import re -import subprocess import requests from invoke import task @@ -40,9 +39,7 @@ def make_doc(ctx): else: if not clean.endswith("tests"): suboutput.append(line) - if clean.startswith("m3gnet") and not clean.endswith( - "tests" - ): + if clean.startswith("m3gnet") and not clean.endswith("tests"): newoutput.extend(suboutput) subpackage = False suboutput = [] @@ -91,23 +88,6 @@ def release_github(ctx): print(response.text) -@task -def update_changelog(ctx): - output = subprocess.check_output( - ["git", "log", "--pretty=format:%s", "v%s..HEAD" % CURRENT_VER] - ) - lines = ["* " + l for l in output.decode("utf-8").strip().split("\n")] - with open("CHANGES.rst") as f: - contents = f.read() - l = "==========" - toks = contents.split(l) - head = "\n\nv%s\n" % NEW_VER + "-" * (len(NEW_VER) + 1) + "\n" - toks.insert(-1, head + "\n".join(lines)) - with open("CHANGES.rst", "w") as f: - f.write(toks[0] + l + "".join(toks[1:])) - ctx.run("open CHANGES.rst") - - @task def release(ctx, notest=False): ctx.run("rm -r dist build m3gnet.egg-info", warn=True)