From 10da158b900477243dcc7f45524431e1ee626f02 Mon Sep 17 00:00:00 2001
From: Shyue Ping Ong <sp@ong.ai>
Date: Fri, 11 Aug 2023 08:02:56 -0700
Subject: [PATCH] Set default fmax for everythng to 0.1.

---
 examples/Calculating MLIP properties.ipynb | 382 +++++++++++----------
 matcalc/elasticity.py                      |   5 +-
 matcalc/eos.py                             |  12 +-
 matcalc/phonon.py                          |  12 +-
 matcalc/relaxation.py                      |   6 +-
 tests/test_elasticity.py                   |  10 +-
 tests/test_eos.py                          |   4 +-
 tests/test_phonon.py                       |   8 +-
 8 files changed, 229 insertions(+), 210 deletions(-)

diff --git a/examples/Calculating MLIP properties.ipynb b/examples/Calculating MLIP properties.ipynb
index 4662659..ed19f3a 100644
--- a/examples/Calculating MLIP properties.ipynb	
+++ b/examples/Calculating MLIP properties.ipynb	
@@ -45,7 +45,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5cccfbc159394505965d38757faa455a",
+       "model_id": "d51679e3658e4bc5ba8f0c598e73b9f5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -66,16 +66,7 @@
    "execution_count": 4,
    "id": "cb64eb7b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/shyue/repos/matgl/matgl/utils/io.py:125: UserWarning: Incompatible model version detected! The code will continue to load the model but it is recommended that you provide a path to an updated model, increment your @model_version in model.json if you are confident that the changes are not problematic, or clear your ~/.matgl cache using `python -c \"import matgl; matgl.clear_cache()\"`\n",
-      "  _check_ver(cls_, v)  # Check version of any subclasses too.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "up = get_universal_calculator(\"M3GNet\")"
    ]
@@ -87,10 +78,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fmax = 0.01\n",
-    "# Note that the rest of the parameters require a more stringent fmax criterion.\n",
+    "fmax = 0.1\n",
     "# The general principle is to do a relaxation first and just reuse the same structure.\n",
-    "\n",
     "prop_calcs = [\n",
     "    (\"relax\", RelaxCalc(up, fmax=fmax)),\n",
     "    (\"elastic\", ElasticityCalc(up, fmax=fmax, relax_structure=False)),\n",
@@ -110,7 +99,9 @@
      "output_type": "stream",
      "text": [
       "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/TensorShape.cpp:3484.)\n",
-      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
+      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
+      "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/phonopy/structure/cells.py:1396: UserWarning: Crystal structure is distorted in a tricky way so that phonopy could not handle the crystal symmetry properly. It is recommended to symmetrize crystal structure well and then re-start phonon calculation from scratch.\n",
+      "  warnings.warn(msg)\n"
      ]
     }
    ],
@@ -138,19 +129,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "c8137638",
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "\n",
-    "df = pd.DataFrame(data)"
+    "df = pd.DataFrame(data)\n",
+    "df[\"time_total\"] = df[\"time_relax\"] + df[\"time_elastic\"] + df[\"time_phonon\"] + df[\"time_eos\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "551ff93b",
    "metadata": {},
    "outputs": [
@@ -183,6 +175,7 @@
        "      <th>time_eos</th>\n",
        "      <th>time_phonon</th>\n",
        "      <th>properties</th>\n",
+       "      <th>time_total</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -191,285 +184,306 @@
        "      <td>mp-1106268</td>\n",
        "      <td>Pr14 Pd6</td>\n",
        "      <td>20</td>\n",
-       "      <td>62.569973</td>\n",
-       "      <td>0.388854</td>\n",
-       "      <td>16.062428</td>\n",
-       "      <td>21.788832</td>\n",
-       "      <td>{'relax': {'final_structure': [[-5.19303374  2...</td>\n",
+       "      <td>3.491403</td>\n",
+       "      <td>0.380318</td>\n",
+       "      <td>7.322077</td>\n",
+       "      <td>5.697545</td>\n",
+       "      <td>{'relax': {'final_structure': [[-5.16210807  2...</td>\n",
+       "      <td>16.891343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>mp-974315</td>\n",
        "      <td>Ru2 I2</td>\n",
        "      <td>4</td>\n",
-       "      <td>1.416487</td>\n",
-       "      <td>0.205097</td>\n",
-       "      <td>4.295082</td>\n",
-       "      <td>0.839284</td>\n",
-       "      <td>{'relax': {'final_structure': [[ 1.83881118 -1...</td>\n",
+       "      <td>1.186046</td>\n",
+       "      <td>0.186275</td>\n",
+       "      <td>2.222045</td>\n",
+       "      <td>0.772150</td>\n",
+       "      <td>{'relax': {'final_structure': [[ 1.84401577 -1...</td>\n",
+       "      <td>4.366516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>mp-1206714</td>\n",
        "      <td>Al2 Sn1</td>\n",
        "      <td>3</td>\n",
-       "      <td>0.470579</td>\n",
-       "      <td>0.117568</td>\n",
-       "      <td>0.229159</td>\n",
-       "      <td>0.251379</td>\n",
-       "      <td>{'relax': {'final_structure': [[1.59917846 1.5...</td>\n",
+       "      <td>0.206165</td>\n",
+       "      <td>0.115411</td>\n",
+       "      <td>0.214473</td>\n",
+       "      <td>0.243003</td>\n",
+       "      <td>{'relax': {'final_structure': [[1.59992902 1.5...</td>\n",
+       "      <td>0.779052</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>mp-30339</td>\n",
        "      <td>Er1 Ag2</td>\n",
        "      <td>3</td>\n",
-       "      <td>0.732216</td>\n",
-       "      <td>0.187762</td>\n",
-       "      <td>2.538878</td>\n",
-       "      <td>0.458349</td>\n",
-       "      <td>{'relax': {'final_structure': [[4.48158802e-07...</td>\n",
+       "      <td>0.107142</td>\n",
+       "      <td>0.157860</td>\n",
+       "      <td>0.851162</td>\n",
+       "      <td>0.448134</td>\n",
+       "      <td>{'relax': {'final_structure': [[1.76298403e-08...</td>\n",
+       "      <td>1.564298</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>mp-696</td>\n",
        "      <td>Si4 Pt4</td>\n",
        "      <td>8</td>\n",
-       "      <td>14.738731</td>\n",
-       "      <td>0.417027</td>\n",
-       "      <td>23.731089</td>\n",
-       "      <td>8.059876</td>\n",
-       "      <td>{'relax': {'final_structure': [[0.88065869 3.9...</td>\n",
+       "      <td>1.267966</td>\n",
+       "      <td>0.328979</td>\n",
+       "      <td>9.390587</td>\n",
+       "      <td>4.059527</td>\n",
+       "      <td>{'relax': {'final_structure': [[0.90113486 3.8...</td>\n",
+       "      <td>15.047059</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>mp-866222</td>\n",
        "      <td>Ac2 Si6</td>\n",
        "      <td>8</td>\n",
-       "      <td>22.017402</td>\n",
-       "      <td>0.237975</td>\n",
-       "      <td>5.265661</td>\n",
-       "      <td>2.303969</td>\n",
-       "      <td>{'relax': {'final_structure': [[-2.05389424e-0...</td>\n",
+       "      <td>4.062110</td>\n",
+       "      <td>0.232349</td>\n",
+       "      <td>2.209232</td>\n",
+       "      <td>3.439701</td>\n",
+       "      <td>{'relax': {'final_structure': [[6.28503045e-07...</td>\n",
+       "      <td>9.943392</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>mp-11422</td>\n",
        "      <td>Gd1 Hg1</td>\n",
        "      <td>2</td>\n",
-       "      <td>0.287373</td>\n",
-       "      <td>0.109842</td>\n",
-       "      <td>0.213693</td>\n",
-       "      <td>0.168733</td>\n",
-       "      <td>{'relax': {'final_structure': [[5.55001412e-20...</td>\n",
+       "      <td>0.107994</td>\n",
+       "      <td>0.101253</td>\n",
+       "      <td>0.158592</td>\n",
+       "      <td>0.164619</td>\n",
+       "      <td>{'relax': {'final_structure': [[-6.87059911e-2...</td>\n",
+       "      <td>0.532458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>mp-21238</td>\n",
        "      <td>Th4 Si4</td>\n",
        "      <td>8</td>\n",
-       "      <td>5.828830</td>\n",
-       "      <td>0.271988</td>\n",
-       "      <td>9.576225</td>\n",
-       "      <td>1.018305</td>\n",
-       "      <td>{'relax': {'final_structure': [[1.08998567 0.7...</td>\n",
+       "      <td>1.754500</td>\n",
+       "      <td>0.213880</td>\n",
+       "      <td>4.111858</td>\n",
+       "      <td>0.959658</td>\n",
+       "      <td>{'relax': {'final_structure': [[1.08682613 0.7...</td>\n",
+       "      <td>7.039896</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>mp-1215363</td>\n",
        "      <td>Zr4 Pd1</td>\n",
        "      <td>5</td>\n",
-       "      <td>1.707482</td>\n",
-       "      <td>0.200783</td>\n",
-       "      <td>7.705117</td>\n",
-       "      <td>1.222322</td>\n",
-       "      <td>{'relax': {'final_structure': [[ 3.93732368e-0...</td>\n",
+       "      <td>0.716570</td>\n",
+       "      <td>0.192944</td>\n",
+       "      <td>2.823358</td>\n",
+       "      <td>1.189626</td>\n",
+       "      <td>{'relax': {'final_structure': [[ 6.81788689e-0...</td>\n",
+       "      <td>4.922498</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>mp-1212658</td>\n",
        "      <td>Ga1 C6</td>\n",
        "      <td>7</td>\n",
-       "      <td>2.543793</td>\n",
-       "      <td>0.235257</td>\n",
-       "      <td>4.211858</td>\n",
-       "      <td>1.364123</td>\n",
-       "      <td>{'relax': {'final_structure': [[-9.88259495e-0...</td>\n",
+       "      <td>1.590856</td>\n",
+       "      <td>0.213355</td>\n",
+       "      <td>2.257039</td>\n",
+       "      <td>0.505926</td>\n",
+       "      <td>{'relax': {'final_structure': [[-1.24073501e-0...</td>\n",
+       "      <td>4.567176</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>mp-1101922</td>\n",
        "      <td>Eu4 Fe8</td>\n",
        "      <td>12</td>\n",
-       "      <td>11.783266</td>\n",
-       "      <td>0.404428</td>\n",
-       "      <td>35.907554</td>\n",
-       "      <td>6.212728</td>\n",
-       "      <td>{'relax': {'final_structure': [[-1.81771003e-0...</td>\n",
+       "      <td>3.292877</td>\n",
+       "      <td>0.394121</td>\n",
+       "      <td>3.696801</td>\n",
+       "      <td>2.027789</td>\n",
+       "      <td>{'relax': {'final_structure': [[-2.40978836e-0...</td>\n",
+       "      <td>9.411588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>mp-1183610</td>\n",
        "      <td>Ca2 Sm6</td>\n",
        "      <td>8</td>\n",
-       "      <td>1.549179</td>\n",
-       "      <td>0.197124</td>\n",
-       "      <td>3.451078</td>\n",
-       "      <td>0.369508</td>\n",
-       "      <td>{'relax': {'final_structure': [[3.63712318 2.0...</td>\n",
+       "      <td>0.567700</td>\n",
+       "      <td>0.208955</td>\n",
+       "      <td>1.655389</td>\n",
+       "      <td>0.839671</td>\n",
+       "      <td>{'relax': {'final_structure': [[3.64054947 2.1...</td>\n",
+       "      <td>3.271715</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>mp-1187980</td>\n",
        "      <td>Yb6 Pb2</td>\n",
        "      <td>8</td>\n",
-       "      <td>3.342118</td>\n",
-       "      <td>0.260559</td>\n",
-       "      <td>3.343180</td>\n",
-       "      <td>0.918367</td>\n",
-       "      <td>{'relax': {'final_structure': [[1.99653039 1.1...</td>\n",
+       "      <td>1.449158</td>\n",
+       "      <td>0.217161</td>\n",
+       "      <td>1.386437</td>\n",
+       "      <td>0.845664</td>\n",
+       "      <td>{'relax': {'final_structure': [[1.93660796 1.1...</td>\n",
+       "      <td>3.898420</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>mp-1187953</td>\n",
        "      <td>Yb3 Pb1</td>\n",
        "      <td>4</td>\n",
-       "      <td>0.692982</td>\n",
-       "      <td>0.133875</td>\n",
-       "      <td>0.204186</td>\n",
-       "      <td>0.211850</td>\n",
-       "      <td>{'relax': {'final_structure': [[-1.32354508e-0...</td>\n",
+       "      <td>0.414266</td>\n",
+       "      <td>0.137839</td>\n",
+       "      <td>0.237347</td>\n",
+       "      <td>0.192977</td>\n",
+       "      <td>{'relax': {'final_structure': [[-1.19091051e-0...</td>\n",
+       "      <td>0.982429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>mp-1185577</td>\n",
        "      <td>Cs2 Hg6</td>\n",
        "      <td>8</td>\n",
-       "      <td>1.416159</td>\n",
-       "      <td>0.222880</td>\n",
-       "      <td>2.779173</td>\n",
-       "      <td>2.014464</td>\n",
-       "      <td>{'relax': {'final_structure': [[1.82452846e-05...</td>\n",
+       "      <td>0.539796</td>\n",
+       "      <td>0.196562</td>\n",
+       "      <td>0.619738</td>\n",
+       "      <td>1.417854</td>\n",
+       "      <td>{'relax': {'final_structure': [[2.13330065e-05...</td>\n",
+       "      <td>2.773950</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>mp-570436</td>\n",
        "      <td>Ca2 Ir4</td>\n",
        "      <td>6</td>\n",
-       "      <td>0.524104</td>\n",
-       "      <td>0.298356</td>\n",
-       "      <td>0.487528</td>\n",
-       "      <td>0.344077</td>\n",
+       "      <td>0.436900</td>\n",
+       "      <td>0.263059</td>\n",
+       "      <td>0.411296</td>\n",
+       "      <td>0.325980</td>\n",
        "      <td>{'relax': {'final_structure': [[2.33200465 1.6...</td>\n",
+       "      <td>1.437235</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>mp-1184183</td>\n",
        "      <td>Cu1 Ge3</td>\n",
        "      <td>4</td>\n",
-       "      <td>0.983168</td>\n",
-       "      <td>0.177530</td>\n",
-       "      <td>0.339162</td>\n",
-       "      <td>0.576755</td>\n",
-       "      <td>{'relax': {'final_structure': [[-1.54866517e-0...</td>\n",
+       "      <td>0.913073</td>\n",
+       "      <td>0.181196</td>\n",
+       "      <td>0.338336</td>\n",
+       "      <td>0.554278</td>\n",
+       "      <td>{'relax': {'final_structure': [[-9.06711170e-0...</td>\n",
+       "      <td>1.986883</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>mp-1025440</td>\n",
        "      <td>Cu2 Ge6</td>\n",
        "      <td>8</td>\n",
-       "      <td>16.191052</td>\n",
-       "      <td>0.258944</td>\n",
-       "      <td>6.878546</td>\n",
-       "      <td>6.582832</td>\n",
-       "      <td>{'relax': {'final_structure': [[2.16285319 1.2...</td>\n",
+       "      <td>0.287793</td>\n",
+       "      <td>0.217949</td>\n",
+       "      <td>1.763743</td>\n",
+       "      <td>0.572771</td>\n",
+       "      <td>{'relax': {'final_structure': [[2.17255392 1.2...</td>\n",
+       "      <td>2.842256</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>mp-1184147</td>\n",
        "      <td>Cu2 Ge6</td>\n",
        "      <td>8</td>\n",
-       "      <td>5.373700</td>\n",
-       "      <td>0.390118</td>\n",
-       "      <td>6.704795</td>\n",
-       "      <td>4.064360</td>\n",
-       "      <td>{'relax': {'final_structure': [[ 3.42616346 -1...</td>\n",
+       "      <td>1.524006</td>\n",
+       "      <td>0.300755</td>\n",
+       "      <td>0.859215</td>\n",
+       "      <td>1.305486</td>\n",
+       "      <td>{'relax': {'final_structure': [[ 3.36096815 -1...</td>\n",
+       "      <td>3.989462</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>mp-1187368</td>\n",
        "      <td>Tb2 Mn6</td>\n",
        "      <td>8</td>\n",
-       "      <td>6.511008</td>\n",
-       "      <td>0.360979</td>\n",
-       "      <td>8.616223</td>\n",
-       "      <td>4.884994</td>\n",
-       "      <td>{'relax': {'final_structure': [[3.13320948 1.8...</td>\n",
+       "      <td>1.859193</td>\n",
+       "      <td>0.257700</td>\n",
+       "      <td>2.159064</td>\n",
+       "      <td>1.281379</td>\n",
+       "      <td>{'relax': {'final_structure': [[2.93975996 1.6...</td>\n",
+       "      <td>5.557336</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>mp-1184679</td>\n",
        "      <td>Hg1 Bi3</td>\n",
        "      <td>4</td>\n",
-       "      <td>0.652660</td>\n",
-       "      <td>0.167195</td>\n",
-       "      <td>0.234439</td>\n",
-       "      <td>0.465418</td>\n",
-       "      <td>{'relax': {'final_structure': [[ 7.96830248e-0...</td>\n",
+       "      <td>0.372904</td>\n",
+       "      <td>0.169251</td>\n",
+       "      <td>0.245386</td>\n",
+       "      <td>0.457257</td>\n",
+       "      <td>{'relax': {'final_structure': [[ 1.92100391e-0...</td>\n",
+       "      <td>1.244798</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "           mid composition  nsites  time_relax  time_elastic   time_eos  \\\n",
-       "0   mp-1106268    Pr14 Pd6      20   62.569973      0.388854  16.062428   \n",
-       "1    mp-974315      Ru2 I2       4    1.416487      0.205097   4.295082   \n",
-       "2   mp-1206714     Al2 Sn1       3    0.470579      0.117568   0.229159   \n",
-       "3     mp-30339     Er1 Ag2       3    0.732216      0.187762   2.538878   \n",
-       "4       mp-696     Si4 Pt4       8   14.738731      0.417027  23.731089   \n",
-       "5    mp-866222     Ac2 Si6       8   22.017402      0.237975   5.265661   \n",
-       "6     mp-11422     Gd1 Hg1       2    0.287373      0.109842   0.213693   \n",
-       "7     mp-21238     Th4 Si4       8    5.828830      0.271988   9.576225   \n",
-       "8   mp-1215363     Zr4 Pd1       5    1.707482      0.200783   7.705117   \n",
-       "9   mp-1212658      Ga1 C6       7    2.543793      0.235257   4.211858   \n",
-       "10  mp-1101922     Eu4 Fe8      12   11.783266      0.404428  35.907554   \n",
-       "11  mp-1183610     Ca2 Sm6       8    1.549179      0.197124   3.451078   \n",
-       "12  mp-1187980     Yb6 Pb2       8    3.342118      0.260559   3.343180   \n",
-       "13  mp-1187953     Yb3 Pb1       4    0.692982      0.133875   0.204186   \n",
-       "14  mp-1185577     Cs2 Hg6       8    1.416159      0.222880   2.779173   \n",
-       "15   mp-570436     Ca2 Ir4       6    0.524104      0.298356   0.487528   \n",
-       "16  mp-1184183     Cu1 Ge3       4    0.983168      0.177530   0.339162   \n",
-       "17  mp-1025440     Cu2 Ge6       8   16.191052      0.258944   6.878546   \n",
-       "18  mp-1184147     Cu2 Ge6       8    5.373700      0.390118   6.704795   \n",
-       "19  mp-1187368     Tb2 Mn6       8    6.511008      0.360979   8.616223   \n",
-       "20  mp-1184679     Hg1 Bi3       4    0.652660      0.167195   0.234439   \n",
+       "           mid composition  nsites  time_relax  time_elastic  time_eos  \\\n",
+       "0   mp-1106268    Pr14 Pd6      20    3.491403      0.380318  7.322077   \n",
+       "1    mp-974315      Ru2 I2       4    1.186046      0.186275  2.222045   \n",
+       "2   mp-1206714     Al2 Sn1       3    0.206165      0.115411  0.214473   \n",
+       "3     mp-30339     Er1 Ag2       3    0.107142      0.157860  0.851162   \n",
+       "4       mp-696     Si4 Pt4       8    1.267966      0.328979  9.390587   \n",
+       "5    mp-866222     Ac2 Si6       8    4.062110      0.232349  2.209232   \n",
+       "6     mp-11422     Gd1 Hg1       2    0.107994      0.101253  0.158592   \n",
+       "7     mp-21238     Th4 Si4       8    1.754500      0.213880  4.111858   \n",
+       "8   mp-1215363     Zr4 Pd1       5    0.716570      0.192944  2.823358   \n",
+       "9   mp-1212658      Ga1 C6       7    1.590856      0.213355  2.257039   \n",
+       "10  mp-1101922     Eu4 Fe8      12    3.292877      0.394121  3.696801   \n",
+       "11  mp-1183610     Ca2 Sm6       8    0.567700      0.208955  1.655389   \n",
+       "12  mp-1187980     Yb6 Pb2       8    1.449158      0.217161  1.386437   \n",
+       "13  mp-1187953     Yb3 Pb1       4    0.414266      0.137839  0.237347   \n",
+       "14  mp-1185577     Cs2 Hg6       8    0.539796      0.196562  0.619738   \n",
+       "15   mp-570436     Ca2 Ir4       6    0.436900      0.263059  0.411296   \n",
+       "16  mp-1184183     Cu1 Ge3       4    0.913073      0.181196  0.338336   \n",
+       "17  mp-1025440     Cu2 Ge6       8    0.287793      0.217949  1.763743   \n",
+       "18  mp-1184147     Cu2 Ge6       8    1.524006      0.300755  0.859215   \n",
+       "19  mp-1187368     Tb2 Mn6       8    1.859193      0.257700  2.159064   \n",
+       "20  mp-1184679     Hg1 Bi3       4    0.372904      0.169251  0.245386   \n",
        "\n",
-       "    time_phonon                                         properties  \n",
-       "0     21.788832  {'relax': {'final_structure': [[-5.19303374  2...  \n",
-       "1      0.839284  {'relax': {'final_structure': [[ 1.83881118 -1...  \n",
-       "2      0.251379  {'relax': {'final_structure': [[1.59917846 1.5...  \n",
-       "3      0.458349  {'relax': {'final_structure': [[4.48158802e-07...  \n",
-       "4      8.059876  {'relax': {'final_structure': [[0.88065869 3.9...  \n",
-       "5      2.303969  {'relax': {'final_structure': [[-2.05389424e-0...  \n",
-       "6      0.168733  {'relax': {'final_structure': [[5.55001412e-20...  \n",
-       "7      1.018305  {'relax': {'final_structure': [[1.08998567 0.7...  \n",
-       "8      1.222322  {'relax': {'final_structure': [[ 3.93732368e-0...  \n",
-       "9      1.364123  {'relax': {'final_structure': [[-9.88259495e-0...  \n",
-       "10     6.212728  {'relax': {'final_structure': [[-1.81771003e-0...  \n",
-       "11     0.369508  {'relax': {'final_structure': [[3.63712318 2.0...  \n",
-       "12     0.918367  {'relax': {'final_structure': [[1.99653039 1.1...  \n",
-       "13     0.211850  {'relax': {'final_structure': [[-1.32354508e-0...  \n",
-       "14     2.014464  {'relax': {'final_structure': [[1.82452846e-05...  \n",
-       "15     0.344077  {'relax': {'final_structure': [[2.33200465 1.6...  \n",
-       "16     0.576755  {'relax': {'final_structure': [[-1.54866517e-0...  \n",
-       "17     6.582832  {'relax': {'final_structure': [[2.16285319 1.2...  \n",
-       "18     4.064360  {'relax': {'final_structure': [[ 3.42616346 -1...  \n",
-       "19     4.884994  {'relax': {'final_structure': [[3.13320948 1.8...  \n",
-       "20     0.465418  {'relax': {'final_structure': [[ 7.96830248e-0...  "
+       "    time_phonon                                         properties  time_total  \n",
+       "0      5.697545  {'relax': {'final_structure': [[-5.16210807  2...   16.891343  \n",
+       "1      0.772150  {'relax': {'final_structure': [[ 1.84401577 -1...    4.366516  \n",
+       "2      0.243003  {'relax': {'final_structure': [[1.59992902 1.5...    0.779052  \n",
+       "3      0.448134  {'relax': {'final_structure': [[1.76298403e-08...    1.564298  \n",
+       "4      4.059527  {'relax': {'final_structure': [[0.90113486 3.8...   15.047059  \n",
+       "5      3.439701  {'relax': {'final_structure': [[6.28503045e-07...    9.943392  \n",
+       "6      0.164619  {'relax': {'final_structure': [[-6.87059911e-2...    0.532458  \n",
+       "7      0.959658  {'relax': {'final_structure': [[1.08682613 0.7...    7.039896  \n",
+       "8      1.189626  {'relax': {'final_structure': [[ 6.81788689e-0...    4.922498  \n",
+       "9      0.505926  {'relax': {'final_structure': [[-1.24073501e-0...    4.567176  \n",
+       "10     2.027789  {'relax': {'final_structure': [[-2.40978836e-0...    9.411588  \n",
+       "11     0.839671  {'relax': {'final_structure': [[3.64054947 2.1...    3.271715  \n",
+       "12     0.845664  {'relax': {'final_structure': [[1.93660796 1.1...    3.898420  \n",
+       "13     0.192977  {'relax': {'final_structure': [[-1.19091051e-0...    0.982429  \n",
+       "14     1.417854  {'relax': {'final_structure': [[2.13330065e-05...    2.773950  \n",
+       "15     0.325980  {'relax': {'final_structure': [[2.33200465 1.6...    1.437235  \n",
+       "16     0.554278  {'relax': {'final_structure': [[-9.06711170e-0...    1.986883  \n",
+       "17     0.572771  {'relax': {'final_structure': [[2.17255392 1.2...    2.842256  \n",
+       "18     1.305486  {'relax': {'final_structure': [[ 3.36096815 -1...    3.989462  \n",
+       "19     1.281379  {'relax': {'final_structure': [[2.93975996 1.6...    5.557336  \n",
+       "20     0.457257  {'relax': {'final_structure': [[ 1.92100391e-0...    1.244798  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -480,33 +494,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "id": "4611ebd4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<Axes: xlabel='nsites', ylabel='time_relax'>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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fmv3SSy/5shkAAIDf+RRmZs6c2ar1li9frtOnT7sv3wYAAPA3n+bMtNbSpUuDPswEAABCW7uGmRB/UgIAALCBdg0zl5KXl6dJkyYpISFBDodD77zzjsdyy7KUlZWlhIQEhYeHa9y4cfrss8+CUywAALCloIaZmpoaDR8+XKtWrWp2+XPPPacVK1Zo1apV2rNnj+Li4nTrrbfqzJkzAa4UAADY1WU9aPJyTZw4URMnTmx2mWVZeumll/TTn/5UU6dOlSS9/vrrio2N1ZtvvqmHHnqo2e3q6upUV1fnfl9VVeX/wgEAgG0E9cyMN8XFxSopKdGECRPcbU6nU2PHjtWOHTta3G7ZsmVyuVzuFw+9BAAgtLVrmLnpppsUHh7u07YlJSWSpNjYWI/22NhY97LmPPXUU6qsrHS/jh8/7tPnAwAAM/g8zHTkyBGtWbNGR44c0csvv6yYmBj98Y9/VGJioq655hpJ0vvvv3/ZBTocDo/3lmU1afs2p9Mpp9N52Z8LAADM4NOZmdzcXA0bNky7d+/Whg0bVF1dLUn69NNPtWTJEr8UFhcXJ0lNzsKUlpY2OVsDAAA6Lp/CzJNPPqlf/OIXysnJUdeuXd3t48eP186dO/1SWEpKiuLi4pSTk+NuO3funHJzczVmzBi/fAYAADCfT8NMBw4c0JtvvtmkPTo6WhUVFa3eT3V1tQ4fPux+X1xcrIKCAvXq1Uv9+vXTwoULtXTpUqWmpio1NVVLly5VRESE7r33Xl/KBgAAIcinMNOjRw+dOHFCKSkpHu35+fm68sorW72fvXv3avz48e73ixYtkvTNs5/Wrl2rxYsX6+zZs3rkkUd06tQpjRo1Sh988IEiIyN9KRsAAIQgh+XDMwcWL16snTt36ve//72uuuoq7d+/X3/72980Y8YMzZgxw2/zZvyhLY8QBwAA9tCW32+f5sz867/+q/r166crr7xS1dXVGjJkiDIyMjRmzBj98z//s09FAwAA+MKnMzONjhw5ovz8fDU0NCgtLU2pqan+rM0vODMDAIB52vL7fVmPMxgwYIAGDBhwObsAAAC4LD6FGcuy9NZbb2nr1q0qLS1VQ0ODx/INGzb4pTgAAIBL8SnMPProo3r11Vc1fvx4xcbGer0jLwAAQHvyKcy88cYb2rBhg7773e/6ux4AAIA28elqJpfLpf79+/u7FgAAgDbzKcxkZWXpmWee0dmzZ/1dDwAAQJv4NMx01113KTs7WzExMUpOTlZYWJjH8v379/ulOAAAgEvxKczMmjVL+/bt0/33388EYAAAEFQ+hZlNmzZp8+bNuvHGG/1dDwAAQJv4NGcmMTGRu+kCAABb8CnMvPDCC1q8eLGOHj3q53IAAADaxqdhpvvvv1+1tbUaMGCAIiIimkwAPnnypF+KAwAAuBSfwsxLL73k5zIAAAB841OYmTlzpr/rAAAA8Emrw0xVVZV70m9VVZXXdZkcDAAAAqXVYaZnz546ceKEYmJi1KNHj2bvLWNZlhwOh+rr6/1aJAAAQEtaHWY++ugj9erVS5K0Zs0aJSYmqnPnzh7rNDQ06IsvvvBvhUAHU1RWrWMna5Xcu7tS+nQPdjkAYHsOy7Kstm7UuXNn91mab6uoqFBMTIytzsxUVVXJ5XKpsrKS4S/Y2unac1qQXaC8wjJ3W0ZqtFZmpskVEeZlSwAIPW35/fbpPjONw0kXq66uVrdu3XzZJdDhLcgu0PbD5R5t2w+Xa352fpAqAgAztOlqpkWLFkmSHA6Hnn76aUVERLiX1dfXa/fu3fq7v/s7vxYIdARFZdUeZ2Qa1VuW8grLVFxew5ATALSgTWEmP/+bfyFalqUDBw6oa9eu7mVdu3bV8OHD9fjjj/u3QqADOHay1uvyoxWEGQBoSZvCzNatWyVJP/zhD/Xyyy8zBwXwk6ReEV6XJ/cmyABAS3yaM7NmzRqCDOBH/aOvUEZqtDpfNBets8OhjNRozsoAgBc+hRkA/rcyM03pA/t4tKUP7KOVmWlBqggAzODT4wwA+J8rIkzr5lyv4vIaHa2o4T4zANBKhBnAZlL6EGIAoC0YZgIAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNJ6aDdhMUVm1jp2sVXJvnp4NAK1BmAFs4nTtOS3ILlBeYZm7LSM1Wisz0+SKCAtiZQBgbwwzATaxILtA2w+Xe7RtP1yu+dn5QaoIAMxAmAFsoKisWnmFZaq3LI/2estSXmGZistrglQZANgfYQawgWMna70uP1pBmAGAlhBmABtI6hXhdXlybyYCA0BLCDOADfSPvkIZqdHq7HB4tHd2OJSRGs1VTUFUVFatrYdKGeoDbIyrmQCbWJmZpvnZ+R5XM6UP7KOVmWlBrKrj4uoywBwOy7poxmGIqaqqksvlUmVlpaKiooJdDnBJxeU1OlpRw31mgmzGa59o++Fyj0nZnR0OpQ/so3Vzrg9iZUDH0Jbfb87MADaT0ocQE2yNV5dd7NtXl3GMAPtgzgwAXISrywCzEGYA4CJcXQaYhTADABfh6jLALIQZAGjGysw0pQ/s49HG1WWAPTEBGACa4YoI07o513N1GWAAwgwAeMHVZYD9McwEAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxm+zCTlZUlh8Ph8YqLiwt2WQAAwCaMeJzBNddcow8//ND9vnPnzkGsBgAA2IkRYaZLly6cjUGHUVRWrWMna3mwIQC0khFhprCwUAkJCXI6nRo1apSWLl2q/v37N7tuXV2d6urq3O+rqqoCVSZwWU7XntOC7ALlFZa52zJSo7UyM02uiLAgVgYA9mb7OTOjRo3SunXrtHnzZq1evVolJSUaM2aMKioqml1/2bJlcrlc7ldiYmKAKwZ8syC7QNsPl3u0bT9crvnZ+UGqCADM4LAsywp2EW1RU1OjAQMGaPHixVq0aFGT5c2dmUlMTFRlZaWioqICWSrQakVl1br5hdwWl299fBxDTgA6lKqqKrlcrlb9fhsxzPRt3bt317Bhw1RYWNjscqfTKafTGeCqgMtz7GSt1+VHK2oIMwDQAtsPM12srq5OBw8eVHx8fLBLAfwmqVeE1+XJvQkyANAS24eZxx9/XLm5uSouLtbu3bv1D//wD6qqqtLMmTODXRrgN/2jr1BGarQ6Oxwe7Z0dDmWkRnNWBgC8sH2Y+fLLL5WZmalBgwZp6tSp6tq1q3bt2qWkpKRglwb41crMNKUP7OPRlj6wj1ZmpgWpIgAwg3ETgNuqLROIADsoLq/R0Yoa7jMDoEML6QnAQKhL6UOIAYC2sP0wEwAAgDeEGQAAYDSGmQCb4dlMANA2hBnAJng2EwD4hmEmwCZ4NhMA+IYwA9hAUVm18grLVH/RnRLqLUt5hWUqLq8JUmUAYH+EGcAGWvNsJgBA8wgzgA3wbCYA8B1hBrABns0EAL4jzAA2wbOZAMA3XJoN2IQrIkzr5lzPs5kAoI0IM4DN8GwmAGgbhpkAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaF2CXQAAT0Vl1Tp2slbJvbsrpU/3YJcDALZHmAFs4nTtOS3ILlBeYZm7LSM1Wisz0+SKCAtiZQBgbwwzATaxILtA2w+Xe7RtP1yu+dn5QaoIAMxAmAFsoKisWnmFZaq3LI/2estSXmGZistrglQZANgfYQawgWMna70uP1pBmAGAlhBmABtI6hXhdXlybyYCA0BLCDOADfSPvkI9W5jk2zMijKuaAMALwgxgA0Vl1TpVe77ZZadqzzNnBgC8IMwANsCcGQDwHWEGsAHmzACA7wgzgA30j75CGanR6uxweLR3djiUkRrNnBkA8IIwA9jEysw0pQ/s49GWPrCPVmamBakiADADjzMAbMIVEaZ1c65XcXmNjlbU8GwmAGglwgxgMyl9CDEA0BaEGaANeKI1ANgPYQZoBZ5oDQD2xQRgoBV4ojUA2BdhBgFRVFatrYdKjbyTLU+0BgB7Y5gJ7SoUhmdac3de5s8AQPBwZgbtKhSGZ7g7LwDYG2EG7SZUhmcCfXdek4fkACAYGGZCuwml4ZmVmWman53vMVzm77vzhsKQHAAEA2EG7SaUhmcCcXfeh9/Yr51FFR5teYVl+sc39in7R6P9+lkAEEoYZkK7DWuE4sMTU/p01/hBMe0ytHRxkGm0s6iCIScA8IIzMx1YIIY1AjE8Ewp2F5/0unxXUYWR4Q8AAoEw04F5u9Jo3Zzr/fIZPDyxtSyvSx1elwJAx0aY6aAarzS62LevNPJn6ODhid6NSuntfXl/78sBoCNjzkwH1ZorjRA4/aOv0JgBzQeWMQN6EwQBwAvCjM211+TcULrSKFS8ct8IZaRGe7RlpEbrlftGBKkiADADw0w21d6Tc/tHXyFXeBdVnr3QZJkrvAtnAoKA+UUA4BvOzNhUez8GoKisutkgI0mVZy8YeSlwqNw517K8TwYGAHjizIwNBWJybihdChwqd84NlX4AQKBxZsaGAjM5N3QuBQ6Fh1lKodMPAAg0wsxlMHlybqhcChwqD7MMlX4AQDAwzOSDQEzO7RkRplO155ss6xkR5pfhn28mAIep8mzTz3CF++czAiFUHmYZKv0AgGDgzIwPAjE5t7kgI0mnas/75V/p30wAbv4zKs/65zMCIVQuMQ+VfgBAMBgRZn71q18pJSVF3bp104gRI/Txxx8HrZZADAcEYs5MqNw0L1QeZhkq/QCAYLB9mFm/fr0WLlyon/70p8rPz9dNN92kiRMn6osvvghKPYEIAYH4V3oonQlYmZmm9IF9PNpMfJhlqPQDAALNYdn8phajRo3Stddeq1deecXddvXVV2vKlClatmzZJbevqqqSy+VSZWWloqKiLrueorJq3fxCbovLtz4+zi//ip7x2ifafrjc4wxQZ4dD6QP7+O0hkIH4jEAKlZvNhUo/AOBytOX329ZnZs6dO6d9+/ZpwoQJHu0TJkzQjh07mt2mrq5OVVVVHi9/CtRwQCD+lR5qZwJS+nTX+EExxgeAUOkHAASKra9mKi8vV319vWJjYz3aY2NjVVJS0uw2y5Yt0zPPPNOuda3MTNP87HyPq5n8HQICcWt7bp8PAAgFtg4zjRwXnQWxLKtJW6OnnnpKixYtcr+vqqpSYmKiX+sJZAhI6dP+ASMQnwEAQHuxdZjp06ePOnfu3OQsTGlpaZOzNY2cTqecTmcgyiMEAABgA7aeM9O1a1eNGDFCOTk5Hu05OTkaM2ZMkKoCAAB2YuszM5K0aNEiTZ8+XSNHjtQNN9ygV199VV988YX+8R//MdilAQAAG7B9mJk2bZoqKir085//XCdOnNDQoUP1/vvvKykpKdilAQAAG7D9fWYul7/vMwMAANpfyNxnBgAA4FIIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjGb7+8xcrsYrz/399GwAANB+Gn+3W3MHmZAPM2fOnJEkvz9sEgAAtL8zZ87I5XJ5XSfkb5rX0NCgr776SpGRkS0+aTtQGp/gffz48Q53A7+O2veO2m+JvnfEvnfUfkv0vT36blmWzpw5o4SEBHXq5H1WTMifmenUqZP69u0b7DI8REVFdbg/9kYdte8dtd8Sfe+Ife+o/Zbou7/7fqkzMo2YAAwAAIxGmAEAAEYjzASQ0+nUkiVL5HQ6g11KwHXUvnfUfkv0vSP2vaP2W6Lvwe57yE8ABgAAoY0zMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0w4yfLli3Tddddp8jISMXExGjKlCk6dOiQ1222bdsmh8PR5PW///u/AaraP7Kyspr0IS4uzus2ubm5GjFihLp166b+/fvr17/+dYCq9a/k5ORmj+HcuXObXd/UY56Xl6dJkyYpISFBDodD77zzjsdyy7KUlZWlhIQEhYeHa9y4cfrss88uud+3335bQ4YMkdPp1JAhQ7Rx48Z26oHvvPX9/PnzeuKJJzRs2DB1795dCQkJmjFjhr766iuv+1y7dm2zfwdff/11O/em9S51zGfNmtWk/tGjR19yv6Yfc0nNHjuHw6F/+7d/a3GfJhzz1vyO2fW7Tpjxk9zcXM2dO1e7du1STk6OLly4oAkTJqimpuaS2x46dEgnTpxwv1JTUwNQsX9dc801Hn04cOBAi+sWFxfru9/9rm666Sbl5+frJz/5iRYsWKC33347gBX7x549ezz6nZOTI0m66667vG5n2jGvqanR8OHDtWrVqmaXP/fcc1qxYoVWrVqlPXv2KC4uTrfeeqv72WjN2blzp6ZNm6bp06frz3/+s6ZPn667775bu3fvbq9u+MRb32tra7V//349/fTT2r9/vzZs2KC//vWvmjx58iX3GxUV5fE3cOLECXXr1q09uuCTSx1zSbr99ts96n///fe97jMUjrmkJsftt7/9rRwOh37wgx943a/dj3lrfsds+1230C5KS0stSVZubm6L62zdutWSZJ06dSpwhbWDJUuWWMOHD2/1+osXL7YGDx7s0fbQQw9Zo0eP9nNlgffoo49aAwYMsBoaGppdHgrHXJK1ceNG9/uGhgYrLi7OWr58ubvt66+/tlwul/XrX/+6xf3cfffd1u233+7Rdtttt1n33HOP32v2l4v73pxPPvnEkmQdO3asxXXWrFljuVwu/xbXjprr98yZM60777yzTfsJ1WN+5513WjfffLPXdUw75pbV9HfMzt91zsy0k8rKSklSr169LrluWlqa4uPjdcstt2jr1q3tXVq7KCwsVEJCglJSUnTPPfeoqKioxXV37typCRMmeLTddttt2rt3r86fP9/epbabc+fO6Y033tDs2bMv+VDTUDjmjYqLi1VSUuJxTJ1Op8aOHasdO3a0uF1LfwfetjFBZWWlHA6HevTo4XW96upqJSUlqW/fvrrjjjuUn58fmAL9aNu2bYqJidFVV12lBx98UKWlpV7XD8Vj/re//U2bNm3SnDlzLrmuacf84t8xO3/XCTPtwLIsLVq0SDfeeKOGDh3a4nrx8fF69dVX9fbbb2vDhg0aNGiQbrnlFuXl5QWw2ss3atQorVu3Tps3b9bq1atVUlKiMWPGqKKiotn1S0pKFBsb69EWGxurCxcuqLy8PBAlt4t33nlHp0+f1qxZs1pcJ1SO+beVlJRIUrPHtHFZS9u1dRu7+/rrr/Xkk0/q3nvv9frAvcGDB2vt2rV69913lZ2drW7duik9PV2FhYUBrPbyTJw4Uf/5n/+pjz76SC+88IL27Nmjm2++WXV1dS1uE4rH/PXXX1dkZKSmTp3qdT3Tjnlzv2N2/q6H/FOzg2HevHn69NNP9ac//cnreoMGDdKgQYPc72+44QYdP35czz//vDIyMtq7TL+ZOHGi+7+HDRumG264QQMGDNDrr7+uRYsWNbvNxWcurP9/I+pLndGws9dee00TJ05UQkJCi+uEyjFvTnPH9FLH05dt7Or8+fO655571NDQoF/96lde1x09erTHZNn09HRde+21WrlypX75y1+2d6l+MW3aNPd/Dx06VCNHjlRSUpI2bdrk9Yc9lI65JP32t7/Vfffdd8m5L6Ydc2+/Y3b8rnNmxs/mz5+vd999V1u3blXfvn3bvP3o0aNtm9Rbq3v37ho2bFiL/YiLi2uSyEtLS9WlSxf17t07ECX63bFjx/Thhx/qgQceaPO2ph/zxivXmjumF/9r7OLt2rqNXZ0/f1533323iouLlZOT4/WsTHM6deqk6667zui/g/j4eCUlJXntQygdc0n6+OOPdejQIZ++93Y+5i39jtn5u06Y8RPLsjRv3jxt2LBBH330kVJSUnzaT35+vuLj4/1cXWDV1dXp4MGDLfbjhhtucF/10+iDDz7QyJEjFRYWFogS/W7NmjWKiYnR9773vTZva/oxT0lJUVxcnMcxPXfunHJzczVmzJgWt2vp78DbNnbUGGQKCwv14Ycf+hTILctSQUGB0X8HFRUVOn78uNc+hMoxb/Taa69pxIgRGj58eJu3teMxv9TvmK2/636bStzBPfzww5bL5bK2bdtmnThxwv2qra11r/Pkk09a06dPd79/8cUXrY0bN1p//etfrb/85S/Wk08+aUmy3n777WB0wWePPfaYtW3bNquoqMjatWuXdccdd1iRkZHW0aNHLctq2u+ioiIrIiLC+vGPf2x9/vnn1muvvWaFhYVZb731VrC6cFnq6+utfv36WU888USTZaFyzM+cOWPl5+db+fn5liRrxYoVVn5+vvuKneXLl1sul8vasGGDdeDAASszM9OKj4+3qqqq3PuYPn269eSTT7rfb9++3ercubO1fPly6+DBg9by5cutLl26WLt27Qp4/7zx1vfz589bkydPtvr27WsVFBR4fPfr6urc+7i471lZWdYf//hH68iRI1Z+fr71wx/+0OrSpYu1e/fuYHSxWd76febMGeuxxx6zduzYYRUXF1tbt261brjhBuvKK68M+WPeqLKy0oqIiLBeeeWVZvdh4jFvze+YXb/rhBk/kdTsa82aNe51Zs6caY0dO9b9/tlnn7UGDBhgdevWzerZs6d14403Wps2bQp88Zdp2rRpVnx8vBUWFmYlJCRYU6dOtT777DP38ov7bVmWtW3bNistLc3q2rWrlZyc3OL/IZhg8+bNliTr0KFDTZaFyjFvvKT84tfMmTMty/rmks0lS5ZYcXFxltPptDIyMqwDBw547GPs2LHu9Rv9/ve/twYNGmSFhYVZgwcPtmWo89b34uLiFr/7W7dude/j4r4vXLjQ6tevn9W1a1crOjramjBhgrVjx47Ad84Lb/2ura21JkyYYEVHR1thYWFWv379rJkzZ1pffPGFxz5C8Zg3+s1vfmOFh4dbp0+fbnYfJh7z1vyO2fW77vj/HQAAADASc2YAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAYJTk5WS+99FKwywBgI9wBGIBRysrK1L17d0VEREiSHA6HNm7cqClTpgS3MABB0yXYBQBAW0RHRwe7BAA2wzATgIAbN26cFixYoMWLF6tXr16Ki4tTVlaWe3lWVpb69esnp9OphIQELViwwL3s28NMycnJkqTvf//7cjgc7veS9N5772nEiBHq1q2b+vfvr2eeeUYXLlxo1WcAMAtnZgAExeuvv65FixZp9+7d2rlzp2bNmqX09HRVVlbqxRdf1O9+9ztdc801Kikp0Z///Odm97Fnzx7FxMRozZo1uv3229W5c2dJ0ubNm3X//ffrl7/8pW666SYdOXJEP/rRjyRJS5Ys0VtvvdXqzwBgf8yZARBw48aNU319vT7++GN32/XXX6+bb75ZMTEx+s1vfqO//OUvCgsLa7JtcnKyFi5cqIULF0pqfs5MRkaGJk6cqKeeesrd9sYbb2jx4sX66quvtGLFCq+fAcAsDDMBCIrvfOc7Hu/j4+NVWlqqu+66S2fPnlX//v314IMPauPGjR7DQ62xb98+/fznP9cVV1zhfj344IM6ceKEamtr/fIZAOyDMAMgKC4+I+JwONTQ0KDExEQdOnRI//7v/67w8HA98sgjysjI0Pnz51u974aGBj3zzDMqKChwvw4cOKDCwkJ169bNL58BwD6YMwPAdsLDwzV58mRNnjxZc+fO1eDBg3XgwAFde+21TdYNCwtTfX29R9u1116rQ4cOaeDAgX75DAD2RpgBYCtr165VfX29Ro0apYiICP3Hf/yHwsPDlZSU1Oz6ycnJ2rJli9LT0+V0OtWzZ0/97Gc/0x133KHExETddddd6tSpkz799FMdOHBAv/jFL9r8GQDsjWEmALbSo0cPrV69Wunp6frOd76jLVu26L333lPv3r2bXf+FF15QTk6OEhMTlZaWJkm67bbb9Ic//EE5OTm67rrrNHr0aK1YscIdVtr6GQDsjauZAACA0TgzAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACj/T/xUch45p36EQAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "ax = df.plot(x=\"nsites\", y=\"time_relax\", kind=\"scatter\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "acb20c91",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -516,14 +531,13 @@
     }
    ],
    "source": [
-    "df.plot(x=\"nsites\", y=\"time_phonon\", kind=\"scatter\")\n",
-    "df.plot(x=\"nsites\", y=\"time_relax\", kind=\"scatter\")"
+    "ax = df[\"time_total\"].hist()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "acb20c91",
+   "id": "9f3bf24e",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/matcalc/elasticity.py b/matcalc/elasticity.py
index 80a03d7..ef08336 100644
--- a/matcalc/elasticity.py
+++ b/matcalc/elasticity.py
@@ -23,16 +23,17 @@ def __init__(
         calculator: Calculator,
         norm_strains: float = 0.01,
         shear_strains: float = 0.01,
-        relax_structure: bool = True,
         fmax: float = 0.1,
+        relax_structure: bool = True,
     ):
         """
         Args:
             calculator: ASE Calculator to use.
+
+            fmax: maximum force in the relaxed structure (if relax_structure).
             norm_strains: strain value to apply to each normal mode.
             shear_strains: strain value to apply to each shear mode.
             relax_structure: whether to relax the provided structure with the given calculator.
-            fmax: maximum force in the relaxed structure (if relax_structure).
         """
         self.calculator = calculator
         self.norm_strains = norm_strains
diff --git a/matcalc/eos.py b/matcalc/eos.py
index f7b3143..921533d 100644
--- a/matcalc/eos.py
+++ b/matcalc/eos.py
@@ -21,22 +21,24 @@ class EOSCalc(PropCalc):
     def __init__(
         self,
         calculator: Calculator,
-        relax_structure: bool = True,
-        fmax: float = 0.01,
         steps: int = 500,
         max_abs_strain: float = 0.1,
         n_points: int = 11,
+        fmax: float = 0.1,
+        relax_structure: bool = True,
     ):
         """
         Args:
             calculator: ASE Calculator to use.
-            relax_structure: Whether to first relax the structure. Set to False if structures provided are pre-relaxed
-                with the same calculator.
-            fmax (float): Max force for relaxation (of structure as well as atoms).
+
             steps (int): Max number of steps for relaxation.
             max_abs_strain (float): The maximum absolute strain applied to the structure. Defaults to 0.1, i.e.,
                 10% strain.
             n_points (int): Number of points in which to compute the EOS. Defaults to 11.
+
+            fmax (float): Max force for relaxation (of structure as well as atoms).
+            relax_structure: Whether to first relax the structure. Set to False if structures provided are pre-relaxed
+                with the same calculator.
         """
         self.calculator = calculator
         self.relax_structure = relax_structure
diff --git a/matcalc/phonon.py b/matcalc/phonon.py
index d7a1634..b5b35e6 100644
--- a/matcalc/phonon.py
+++ b/matcalc/phonon.py
@@ -24,23 +24,25 @@ def __init__(
         calculator: Calculator,
         atom_disp=0.015,
         supercell_matrix=((2, 0, 0), (0, 2, 0), (0, 0, 2)),
-        fmax=0.01,
-        relax_structure=True,
         t_step=10,
         t_max=1000,
         t_min=0,
+        fmax=0.1,
+        relax_structure=True,
     ):
         """
         Args:
             calculator: ASE Calculator to use.
+            fmax: Max forces. This criterion is more stringent than for simple relaxation.
             atom_disp: Atomic displacement
             supercell_matrix: Supercell matrix to use. Defaults to 2x2x2 supercell.
-            fmax: Max forces. This criterion is more stringent than for simple relaxation.
-            relax_structure: Whether to first relax the structure. Set to False if structures provided are pre-relaxed
-                with the same calculator.
+
             t_step: Temperature step.
             t_max: Max temperature.
             t_min: Min temperature.
+            relax_structure: Whether to first relax the structure. Set to False if structures provided are pre-relaxed
+                with the same calculator.
+
         """
         self.calculator = calculator
         self.atom_disp = atom_disp
diff --git a/matcalc/relaxation.py b/matcalc/relaxation.py
index dd202e8..07fe094 100644
--- a/matcalc/relaxation.py
+++ b/matcalc/relaxation.py
@@ -89,10 +89,10 @@ def __init__(
         self,
         calculator: Calculator,
         optimizer: Optimizer | str = "FIRE",
-        fmax: float = 0.1,
         steps: int = 500,
         traj_file: str | None = None,
         interval=1,
+        fmax: float = 0.1,
         relax_cell=True,
     ):
         """
@@ -100,11 +100,11 @@ def __init__(
             calculator: ASE Calculator to use.
             optimizer (str or ase Optimizer): the optimization algorithm.
                 Defaults to "FIRE"
-            fmax (float): Total force tolerance for relaxation convergence. fmax is a sum of force and stress forces.
             steps (int): Max number of steps for relaxation.
             traj_file (str): The trajectory file for saving
             interval (int): The step interval for saving the trajectories.
-            relax_cell (bool): Whether to relax the cell.
+            fmax (float): Total force tolerance for relaxation convergence. fmax is a sum of force and stress forces.
+            relax_cell (bool): Whether to relax the cell (or just atoms).
         """
         self.calculator = calculator
         self.optimizer: Optimizer = OPTIMIZERS[optimizer] if isinstance(optimizer, str) else optimizer
diff --git a/tests/test_elasticity.py b/tests/test_elasticity.py
index 85072d6..db24ab6 100644
--- a/tests/test_elasticity.py
+++ b/tests/test_elasticity.py
@@ -9,12 +9,12 @@
 def test_ElasticCalc(LiFePO4, M3GNetUPCalc):
     """Tests for ElasticCalc class"""
     calculator = M3GNetUPCalc
-    ecalc = ElasticityCalc(calculator, norm_strains=0.02, shear_strains=0.04, fmax=0.01)
+    ecalc = ElasticityCalc(calculator, norm_strains=0.02, shear_strains=0.04, fmax=0.1)
 
     # Test LiFePO4 with relaxation
     results = ecalc.calc(LiFePO4)
     assert results["elastic_tensor"].shape == (3, 3, 3, 3)
-    assert results["elastic_tensor"][0][1][1][0] == pytest.approx(0.6441543434291928, rel=0.0001)
-    assert results["bulk_modulus_vrh"] == pytest.approx(1.109278785217532, rel=0.0001)
-    assert results["shear_modulus_vrh"] == pytest.approx(0.5946891263210372, rel=0.0001)
-    assert results["youngs_modulus"] == pytest.approx(1513587180.4865916, rel=0.0001)
+    assert results["elastic_tensor"][0][1][1][0] == pytest.approx(0.646330702693376, rel=0.1)
+    assert results["bulk_modulus_vrh"] == pytest.approx(1.109278785217532, rel=0.1)
+    assert results["shear_modulus_vrh"] == pytest.approx(0.5946891263210372, rel=0.1)
+    assert results["youngs_modulus"] == pytest.approx(1513587180.4865916, rel=0.1)
diff --git a/tests/test_eos.py b/tests/test_eos.py
index 4545f8c..612ad35 100644
--- a/tests/test_eos.py
+++ b/tests/test_eos.py
@@ -10,11 +10,11 @@ def test_PhononCalc(Li2O, LiFePO4, M3GNetUPCalc):
     """Tests for PhononCalc class"""
     calculator = M3GNetUPCalc
     # Note that the fmax is probably too high. This is for testing purposes only.
-    pcalc = EOSCalc(calculator, fmax=0.01)
+    pcalc = EOSCalc(calculator, fmax=0.1)
     results = pcalc.calc(Li2O)
 
     assert results["bulk_modulus"] == pytest.approx(69.86879801931632, rel=0.1)
 
     results = list(pcalc.calc_many([Li2O, LiFePO4]))
     assert len(results) == 2
-    assert results[1]["bulk_modulus"] == pytest.approx(53.791519828150925, rel=0.1)
+    assert results[1]["bulk_modulus"] == pytest.approx(60.083102790525366, rel=0.1)
diff --git a/tests/test_phonon.py b/tests/test_phonon.py
index a9d297a..33dccf5 100644
--- a/tests/test_phonon.py
+++ b/tests/test_phonon.py
@@ -15,10 +15,10 @@ def test_PhononCalc(Li2O, LiFePO4, M3GNetUPCalc):
 
     # Test values at 100 K
     ind = results["temperatures"].tolist().index(300)
-    assert results["heat_capacity"][ind] == pytest.approx(59.918928933451305, abs=0.01)
-    assert results["entropy"][ind] == pytest.approx(51.9081928335805, abs=0.01)
-    assert results["free_energy"][ind] == pytest.approx(11.892105644441045, abs=0.01)
+    assert results["heat_capacity"][ind] == pytest.approx(59.91894069664282, rel=0.1)
+    assert results["entropy"][ind] == pytest.approx(51.9081928335805, rel=0.1)
+    assert results["free_energy"][ind] == pytest.approx(11.892105644441045, rel=0.1)
 
     results = list(pcalc.calc_many([Li2O, LiFePO4]))
     assert len(results) == 2
-    assert results[-1]["heat_capacity"][ind] == pytest.approx(550.6419940551511, abs=0.01)
+    assert results[-1]["heat_capacity"][ind] == pytest.approx(550.6419940551511, rel=0.1)