{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "5f91f58f-ef53-484d-9951-045829872567",
"metadata": {},
"outputs": [],
"source": [
"from pyiron import Project\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0dde0404-8501-4649-ae81-3f008d4a76e3",
"metadata": {},
"outputs": [],
"source": [
"pr = Project(\".\")"
]
},
{
"cell_type": "markdown",
"id": "97f1ec7a-4eb6-404d-afdb-0d2739255ead",
"metadata": {},
"source": [
"### Training data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "af1bbd9f-c61b-4bc8-b122-5848537620fd",
"metadata": {},
"outputs": [
{
"data": {
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" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
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" <th>4</th>\n",
" <td>8382</td>\n",
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" <td>Al4</td>\n",
" <td>AlMurn_0_94</td>\n",
" <td>/AlMurn_0_94</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
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" <td>2022-06-03 08:06:01.135409</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>8384</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_0_98</td>\n",
" <td>/AlMurn_0_98</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
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" <td>8387</td>\n",
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" <td>AlMurn_1_04</td>\n",
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" <td>2022-06-03 08:06:03.225259</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>8388</td>\n",
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" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>8390</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_1_1</td>\n",
" <td>/AlMurn_1_1</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
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" <th>13</th>\n",
" <td>8394</td>\n",
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" <td>/AlEnergyBasedWeight</td>\n",
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" <td>potentials/01-EAM/</td>\n",
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" <td>2022-06-03 08:51:38.771963</td>\n",
" <td>100.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Atomicrex</td>\n",
" <td>0.4</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>14</th>\n",
" <td>8395</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>FitAl</td>\n",
" <td>/FitAl</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/</td>\n",
" <td>2022-06-03 09:31:10.010365</td>\n",
" <td>2022-06-03 09:34:38.961298</td>\n",
" <td>208.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Atomicrex</td>\n",
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],
"text/plain": [
" id status chemicalformula job subjob \\\n",
"0 8378 finished Al4 Al /Al \n",
"1 8379 finished Al4 AlMurn /AlMurn \n",
"2 8380 finished Al4 AlMurn_0_9 /AlMurn_0_9 \n",
"3 8381 finished Al4 AlMurn_0_92 /AlMurn_0_92 \n",
"4 8382 finished Al4 AlMurn_0_94 /AlMurn_0_94 \n",
"5 8383 finished Al4 AlMurn_0_96 /AlMurn_0_96 \n",
"6 8384 finished Al4 AlMurn_0_98 /AlMurn_0_98 \n",
"7 8385 finished Al4 AlMurn_1_0 /AlMurn_1_0 \n",
"8 8386 finished Al4 AlMurn_1_02 /AlMurn_1_02 \n",
"9 8387 finished Al4 AlMurn_1_04 /AlMurn_1_04 \n",
"10 8388 finished Al4 AlMurn_1_06 /AlMurn_1_06 \n",
"11 8389 finished Al4 AlMurn_1_08 /AlMurn_1_08 \n",
"12 8390 finished Al4 AlMurn_1_1 /AlMurn_1_1 \n",
"13 8394 finished None AlEnergyBasedWeight /AlEnergyBasedWeight \n",
"14 8395 finished None FitAl /FitAl \n",
"\n",
" projectpath \\\n",
"0 /home/niklas/git_projects/workshop_preparation/ \n",
"1 /home/niklas/git_projects/workshop_preparation/ \n",
"2 /home/niklas/git_projects/workshop_preparation/ \n",
"3 /home/niklas/git_projects/workshop_preparation/ \n",
"4 /home/niklas/git_projects/workshop_preparation/ \n",
"5 /home/niklas/git_projects/workshop_preparation/ \n",
"6 /home/niklas/git_projects/workshop_preparation/ \n",
"7 /home/niklas/git_projects/workshop_preparation/ \n",
"8 /home/niklas/git_projects/workshop_preparation/ \n",
"9 /home/niklas/git_projects/workshop_preparation/ \n",
"10 /home/niklas/git_projects/workshop_preparation/ \n",
"11 /home/niklas/git_projects/workshop_preparation/ \n",
"12 /home/niklas/git_projects/workshop_preparation/ \n",
"13 /home/niklas/git_projects/workshop_preparation/ \n",
"14 /home/niklas/git_projects/workshop_preparation/ \n",
"\n",
" project timestart \\\n",
"0 potentials/01-EAM/ 2022-06-03 08:05:58.456855 \n",
"1 potentials/01-EAM/ 2022-06-03 08:05:59.009067 \n",
"2 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:05:59.253803 \n",
"3 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:05:59.755490 \n",
"4 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:00.316664 \n",
"5 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:00.849265 \n",
"6 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:01.414279 \n",
"7 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:01.968950 \n",
"8 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:02.478959 \n",
"9 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:02.984933 \n",
"10 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:03.494921 \n",
"11 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:04.027781 \n",
"12 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:04.542172 \n",
"13 potentials/01-EAM/ 2022-06-03 08:49:58.419860 \n",
"14 potentials/01-EAM/ 2022-06-03 09:31:10.010365 \n",
"\n",
" timestop totalcputime computer hamilton \\\n",
"0 2022-06-03 08:05:58.777656 0.0 pyiron@niklas#1 Lammps \n",
"1 2022-06-03 08:06:05.000685 5.0 pyiron@niklas#1#11/11 Murnaghan \n",
"2 2022-06-03 08:05:59.484215 0.0 pyiron@niklas#1 Lammps \n",
"3 2022-06-03 08:06:00.034975 0.0 pyiron@niklas#1 Lammps \n",
"4 2022-06-03 08:06:00.569561 0.0 pyiron@niklas#1 Lammps \n",
"5 2022-06-03 08:06:01.135409 0.0 pyiron@niklas#1 Lammps \n",
"6 2022-06-03 08:06:01.691734 0.0 pyiron@niklas#1 Lammps \n",
"7 2022-06-03 08:06:02.203044 0.0 pyiron@niklas#1 Lammps \n",
"8 2022-06-03 08:06:02.708703 0.0 pyiron@niklas#1 Lammps \n",
"9 2022-06-03 08:06:03.225259 0.0 pyiron@niklas#1 Lammps \n",
"10 2022-06-03 08:06:03.753859 0.0 pyiron@niklas#1 Lammps \n",
"11 2022-06-03 08:06:04.268630 0.0 pyiron@niklas#1 Lammps \n",
"12 2022-06-03 08:06:04.828637 0.0 pyiron@niklas#1 Lammps \n",
"13 2022-06-03 08:51:38.771963 100.0 pyiron@niklas#1 Atomicrex \n",
"14 2022-06-03 09:34:38.961298 208.0 pyiron@niklas#1 Atomicrex \n",
"\n",
" hamversion parentid masterid \n",
"0 0.1 None NaN \n",
"1 0.3.0 None NaN \n",
"2 0.1 None 8379.0 \n",
"3 0.1 None 8379.0 \n",
"4 0.1 None 8379.0 \n",
"5 0.1 None 8379.0 \n",
"6 0.1 None 8379.0 \n",
"7 0.1 None 8379.0 \n",
"8 0.1 None 8379.0 \n",
"9 0.1 None 8379.0 \n",
"10 0.1 None 8379.0 \n",
"11 0.1 None 8379.0 \n",
"12 0.1 None 8379.0 \n",
"13 0.4 None NaN \n",
"14 0.4 None NaN "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pr.job_table()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9678bb53-b9e6-4d37-9ada-5331af8ecf69",
"metadata": {},
"outputs": [
{
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" <td>2022-06-03 08:06:05.000685</td>\n",
" <td>5.0</td>\n",
" <td>pyiron@niklas#1#11/11</td>\n",
" <td>Murnaghan</td>\n",
" <td>0.3.0</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8380</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_0_9</td>\n",
" <td>/AlMurn_0_9</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
" <td>2022-06-03 08:05:59.253803</td>\n",
" <td>2022-06-03 08:05:59.484215</td>\n",
" <td>0.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>8379.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8381</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_0_92</td>\n",
" <td>/AlMurn_0_92</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
" <td>2022-06-03 08:05:59.755490</td>\n",
" <td>2022-06-03 08:06:00.034975</td>\n",
" <td>0.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>8379.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8382</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_0_94</td>\n",
" <td>/AlMurn_0_94</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
" <td>2022-06-03 08:06:00.316664</td>\n",
" <td>2022-06-03 08:06:00.569561</td>\n",
" <td>0.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>8379.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>8383</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_0_96</td>\n",
" <td>/AlMurn_0_96</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
" <td>2022-06-03 08:06:00.849265</td>\n",
" <td>2022-06-03 08:06:01.135409</td>\n",
" <td>0.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>8379.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8384</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_0_98</td>\n",
" <td>/AlMurn_0_98</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
" <td>2022-06-03 08:06:01.414279</td>\n",
" <td>2022-06-03 08:06:01.691734</td>\n",
" <td>0.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>8379.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8385</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_1_0</td>\n",
" <td>/AlMurn_1_0</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
" <td>2022-06-03 08:06:01.968950</td>\n",
" <td>2022-06-03 08:06:02.203044</td>\n",
" <td>0.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>8379.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8386</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_1_02</td>\n",
" <td>/AlMurn_1_02</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
" <td>2022-06-03 08:06:02.478959</td>\n",
" <td>2022-06-03 08:06:02.708703</td>\n",
" <td>0.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>8379.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>8387</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_1_04</td>\n",
" <td>/AlMurn_1_04</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
" <td>2022-06-03 08:06:02.984933</td>\n",
" <td>2022-06-03 08:06:03.225259</td>\n",
" <td>0.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>8379.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8388</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_1_06</td>\n",
" <td>/AlMurn_1_06</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
" <td>2022-06-03 08:06:03.494921</td>\n",
" <td>2022-06-03 08:06:03.753859</td>\n",
" <td>0.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>8379.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>8389</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_1_08</td>\n",
" <td>/AlMurn_1_08</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
" <td>2022-06-03 08:06:04.027781</td>\n",
" <td>2022-06-03 08:06:04.268630</td>\n",
" <td>0.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>8379.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>8390</td>\n",
" <td>finished</td>\n",
" <td>Al4</td>\n",
" <td>AlMurn_1_1</td>\n",
" <td>/AlMurn_1_1</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/AlMurn_hdf5/</td>\n",
" <td>2022-06-03 08:06:04.542172</td>\n",
" <td>2022-06-03 08:06:04.828637</td>\n",
" <td>0.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>8379.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>8394</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>AlEnergyBasedWeight</td>\n",
" <td>/AlEnergyBasedWeight</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/</td>\n",
" <td>2022-06-03 08:49:58.419860</td>\n",
" <td>2022-06-03 08:51:38.771963</td>\n",
" <td>100.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Atomicrex</td>\n",
" <td>0.4</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>8395</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>FitAl</td>\n",
" <td>/FitAl</td>\n",
" <td>/home/niklas/git_projects/workshop_preparation/</td>\n",
" <td>potentials/01-EAM/</td>\n",
" <td>2022-06-03 09:31:10.010365</td>\n",
" <td>2022-06-03 09:34:38.961298</td>\n",
" <td>208.0</td>\n",
" <td>pyiron@niklas#1</td>\n",
" <td>Atomicrex</td>\n",
" <td>0.4</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id status chemicalformula job subjob \\\n",
"0 8378 finished Al4 Al /Al \n",
"1 8379 finished Al4 AlMurn /AlMurn \n",
"2 8380 finished Al4 AlMurn_0_9 /AlMurn_0_9 \n",
"3 8381 finished Al4 AlMurn_0_92 /AlMurn_0_92 \n",
"4 8382 finished Al4 AlMurn_0_94 /AlMurn_0_94 \n",
"5 8383 finished Al4 AlMurn_0_96 /AlMurn_0_96 \n",
"6 8384 finished Al4 AlMurn_0_98 /AlMurn_0_98 \n",
"7 8385 finished Al4 AlMurn_1_0 /AlMurn_1_0 \n",
"8 8386 finished Al4 AlMurn_1_02 /AlMurn_1_02 \n",
"9 8387 finished Al4 AlMurn_1_04 /AlMurn_1_04 \n",
"10 8388 finished Al4 AlMurn_1_06 /AlMurn_1_06 \n",
"11 8389 finished Al4 AlMurn_1_08 /AlMurn_1_08 \n",
"12 8390 finished Al4 AlMurn_1_1 /AlMurn_1_1 \n",
"13 8394 finished None AlEnergyBasedWeight /AlEnergyBasedWeight \n",
"14 8395 finished None FitAl /FitAl \n",
"\n",
" projectpath \\\n",
"0 /home/niklas/git_projects/workshop_preparation/ \n",
"1 /home/niklas/git_projects/workshop_preparation/ \n",
"2 /home/niklas/git_projects/workshop_preparation/ \n",
"3 /home/niklas/git_projects/workshop_preparation/ \n",
"4 /home/niklas/git_projects/workshop_preparation/ \n",
"5 /home/niklas/git_projects/workshop_preparation/ \n",
"6 /home/niklas/git_projects/workshop_preparation/ \n",
"7 /home/niklas/git_projects/workshop_preparation/ \n",
"8 /home/niklas/git_projects/workshop_preparation/ \n",
"9 /home/niklas/git_projects/workshop_preparation/ \n",
"10 /home/niklas/git_projects/workshop_preparation/ \n",
"11 /home/niklas/git_projects/workshop_preparation/ \n",
"12 /home/niklas/git_projects/workshop_preparation/ \n",
"13 /home/niklas/git_projects/workshop_preparation/ \n",
"14 /home/niklas/git_projects/workshop_preparation/ \n",
"\n",
" project timestart \\\n",
"0 potentials/01-EAM/ 2022-06-03 08:05:58.456855 \n",
"1 potentials/01-EAM/ 2022-06-03 08:05:59.009067 \n",
"2 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:05:59.253803 \n",
"3 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:05:59.755490 \n",
"4 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:00.316664 \n",
"5 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:00.849265 \n",
"6 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:01.414279 \n",
"7 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:01.968950 \n",
"8 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:02.478959 \n",
"9 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:02.984933 \n",
"10 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:03.494921 \n",
"11 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:04.027781 \n",
"12 potentials/01-EAM/AlMurn_hdf5/ 2022-06-03 08:06:04.542172 \n",
"13 potentials/01-EAM/ 2022-06-03 08:49:58.419860 \n",
"14 potentials/01-EAM/ 2022-06-03 09:31:10.010365 \n",
"\n",
" timestop totalcputime computer hamilton \\\n",
"0 2022-06-03 08:05:58.777656 0.0 pyiron@niklas#1 Lammps \n",
"1 2022-06-03 08:06:05.000685 5.0 pyiron@niklas#1#11/11 Murnaghan \n",
"2 2022-06-03 08:05:59.484215 0.0 pyiron@niklas#1 Lammps \n",
"3 2022-06-03 08:06:00.034975 0.0 pyiron@niklas#1 Lammps \n",
"4 2022-06-03 08:06:00.569561 0.0 pyiron@niklas#1 Lammps \n",
"5 2022-06-03 08:06:01.135409 0.0 pyiron@niklas#1 Lammps \n",
"6 2022-06-03 08:06:01.691734 0.0 pyiron@niklas#1 Lammps \n",
"7 2022-06-03 08:06:02.203044 0.0 pyiron@niklas#1 Lammps \n",
"8 2022-06-03 08:06:02.708703 0.0 pyiron@niklas#1 Lammps \n",
"9 2022-06-03 08:06:03.225259 0.0 pyiron@niklas#1 Lammps \n",
"10 2022-06-03 08:06:03.753859 0.0 pyiron@niklas#1 Lammps \n",
"11 2022-06-03 08:06:04.268630 0.0 pyiron@niklas#1 Lammps \n",
"12 2022-06-03 08:06:04.828637 0.0 pyiron@niklas#1 Lammps \n",
"13 2022-06-03 08:51:38.771963 100.0 pyiron@niklas#1 Atomicrex \n",
"14 2022-06-03 09:34:38.961298 208.0 pyiron@niklas#1 Atomicrex \n",
"\n",
" hamversion parentid masterid \n",
"0 0.1 None NaN \n",
"1 0.3.0 None NaN \n",
"2 0.1 None 8379.0 \n",
"3 0.1 None 8379.0 \n",
"4 0.1 None 8379.0 \n",
"5 0.1 None 8379.0 \n",
"6 0.1 None 8379.0 \n",
"7 0.1 None 8379.0 \n",
"8 0.1 None 8379.0 \n",
"9 0.1 None 8379.0 \n",
"10 0.1 None 8379.0 \n",
"11 0.1 None 8379.0 \n",
"12 0.1 None 8379.0 \n",
"13 0.4 None NaN \n",
"14 0.4 None NaN "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pr.job_table()"
]
},
{
"cell_type": "markdown",
"id": "512673b5-e606-40d8-8a1d-1a9932591f3b",
"metadata": {},
"source": [
"### Add training data\n",
"Instead of simply loading the training data weights are assigned to the structures based on their energy this time. Additionally the structure with a very high atomic volume is filtered out. \n",
"Structures with low energy get a higher weight than structures with high energy.\n",
"It can also help to increase the weights of special properties or important structures to put a focus on things that are important for the scope of the potential.\n",
"For a list of a all properties that can be fitted refer to the atomicrex website https://www.atomicrex.org/properties.html"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9a11b349-5094-4cbc-a03e-e929753e4a82",
"metadata": {},
"outputs": [],
"source": [
"def get_e_weight(E_S, Delta=1.0, N=2):\n",
" return 1 / (E_S + Delta) ** N"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ada4baf2-cede-4f31-915d-500b6bcb3518",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fbaa36da190>]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"E_S = np.arange(0, 3, 0.1)\n",
"plt.plot(E_S, get_e_weight(E_S))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5a59295b-112d-4f88-94f0-68ac14f7ac91",
"metadata": {},
"outputs": [],
"source": [
"tc = pr['../../introduction/training/Al_basic_atomicrex'] ## Load the TrainingContainer\n",
"job = pr.create.job.Atomicrex(\"AlEnergyBasedWeight\", delete_existing_job=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7126c3a1-87a7-4978-9241-de69bb609437",
"metadata": {},
"outputs": [],
"source": [
"i=0\n",
"for s, energy, forces, symbols in tc.iter(\"energy\", \"forces\", \"symbols\"):\n",
" natoms = len(s)\n",
" e = energy / natoms\n",
" weight = get_e_weight(e+3.5) # add approx. equilibrium energy of Al\n",
" job.structures.add_structure(structure=s, identifier=f\"{i}\", relative_weight=weight) # set weight for the structure\n",
" job.structures.add_scalar_fit_property(prop=\"atomic-energy\", target_val=e,)\n",
" job.structures.add_vector_fit_property(prop=\"atomic-forces\", target_val=forces)\n",
" i += 1"
]
},
{
"cell_type": "markdown",
"id": "7f655bcb-9926-4d92-ad86-7b84c312661a",
"metadata": {},
"source": [
"### Set the potential type"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3940e14a-77a0-4448-97cc-f5b5b8428c0c",
"metadata": {},
"outputs": [],
"source": [
"job.potential = job.factories.potentials.eam_potential()"
]
},
{
"cell_type": "markdown",
"id": "45ad4c4f-a496-467d-93e1-dda611625af3",
"metadata": {},
"source": [
"### Define functions"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "23f79603-b52b-44a7-a93d-d5dd0dbf97c9",
"metadata": {},
"outputs": [],
"source": [
"# morse A is the original rather simple morse potential\n",
"morseA = job.factories.functions.morse_A(\"MorseA\", D0=0.15, r0=3.0, alpha=2.0, species=[\"Al\", \"Al\"])\n",
"morseA.screening = job.factories.functions.x_pow_n_cutoff(\"morseScreen\", cutoff=7.6, species=[\"Al\", \"Al\"])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ad4399f1-26b2-4d99-b7b3-b12821cfb26d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<Figure size 720x504 with 1 Axes>,\n",
" <AxesSubplot:xlabel='r [$\\\\AA$]', ylabel='func(r)'>)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"morseA.plot()"
]
},
{
"cell_type": "markdown",
"id": "1ea68594-64f3-4728-94d5-dba8677cb9d7",
"metadata": {},
"source": [
"Electron density as used by Mishin in his Cu potential (https://doi.org/10.1103/PhysRevB.63.224106, Gaussian + exp(-$\\beta$r term)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "2171010d-9c36-42be-b9e5-27b0508aaa15",
"metadata": {},
"outputs": [],
"source": [
"rho = job.factories.functions.MishinCuRho(identifier=\"Rho\", a=1.0, r1=-1, r2=0.0, beta1=1.0, beta2=3.0, species=[\"Al\", \"Al\"])\n",
"rho.screening = job.factories.functions.x_pow_n_cutoff(\"RhoScreen\", cutoff=7.6, species=[\"Al\", \"Al\"])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "dd3dc2e1-94be-4b74-9adc-3666f417bc50",
"metadata": {},
"outputs": [],
"source": [
"F = job.factories.functions.user_function(identifier=\"F\", input_variable=\"rho\", species=[\"Al\"])\n",
"# User function for embedding term\n",
"F = job.factories.functions.user_function(identifier=\"F\", input_variable=\"r\")\n",
"F.expression = \"-A*sqrt(r)\"\n",
"F.derivative = \"-A/(2*sqrt(r))\"\n",
"F.parameters.add_parameter(\"A\", start_val=2.3, min_val=0.0)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "32e048b2-562d-4b9a-ba2a-2712450a70f0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job AlEnergyBasedWeight was saved and received the ID: 8396\n"
]
}
],
"source": [
"job.potential.pair_interactions[morseA.identifier] = morseA\n",
"job.potential.electron_densities[rho.identifier] = rho\n",
"job.potential.embedding_energies[F.identifier] = F\n",
"\n",
"job.input.fit_algorithm = job.factories.algorithms.ar_lbfgs(max_iter=500, gradient_epsilon=1e-9)\n",
"\n",
"job.run()"
]
},
{
"cell_type": "markdown",
"id": "abf288b4-a8e8-4695-9e37-c911711e501e",
"metadata": {},
"source": [
"### Examples for alternative minimizers\n",
"\n",
"Local:\n",
"```\n",
"job.input.fit_algorithm = job.factories.algorithms.ln_neldermead(max_iter=10000)\n",
"```\n",
"nelder mead is a bit more robust than the LBFGS implementation in atomicrex\n",
"It is gradient free, so each iteration is much quicker, but more iterations are needed\n",
"\n",
"Global:\n",
"```\n",
"job.input.fit_algorithm = job.factories.algorithms.gn_esch(max_iter=10000)\n",
"```\n",
"ESCH is an evolutionary algorithm. To use it all parameters need a min and a max value"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "156e4c38-5636-4719-852b-bce9cf45e24d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<Figure size 576x1296 with 3 Axes>,\n",
" array([[<AxesSubplot:title={'center':'Al F'}, xlabel='$\\\\rho $ [a.u.]'>],\n",
" [<AxesSubplot:title={'center':'Al rho_AlAl'}, xlabel='r [$\\\\AA$]'>],\n",
" [<AxesSubplot:title={'center':'Al V_AlAl'}, xlabel='r [$\\\\AA$]'>]],\n",
" dtype=object))"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 576x1296 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"job.plot_final_potential()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "b74ef18b-e4c1-4e91-a82a-16340c8869ac",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"499.0580978786983"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(job.output.iterations, job.output.residual)\n",
"job.output.residual[-1]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "2ca61e19-d5a3-47b2-af15-339d9ffa6675",
"metadata": {},
"outputs": [],
"source": [
"plots = job.plot"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "c4b28c40-9e63-4805-aeb8-805abc044478",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1008x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(14,7))\n",
"plots.energy_scatter_histogram()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "774a4082-7a72-4062-8371-3953f9c826b0",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plots.force_scatter_histogram()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "caee172a-24a9-43cd-a122-eb09120d2684",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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wC69l1E5RrDeVx1KYda6qk0VEXAn8HUmCWEeyrOo1WQYlaY6kNZKelLQgy2OPN339A8z/wcOZjtKeP3u6x1KYjTO19Ia6DPjfiLgwIv4F+F9Jl2QVUFrFdSFwNLA3cLKkvbM6/nhz1pJVDI91qbtR5s7s9VgKs3Gmlgbu/dLBeMDmBu6Z2YXEwcCTEfE/AJKuBo4DHsvwHB2tr3+As3+0ipc2Dmd+7J0ndgN4LIXZOFNLm8UESTvnnkiaTG1Jp5he4Lm852vTbVuQNE/SMknL1q93L5ycvv4B5l/7cF0SRXeX+PKxMzI/rpm1vlr+yX8duFfStSQ9oj4KnJNhTIU622xVjxIRFwMXA8yaNateS0i3ncVL19RtwN1ZH5nhqwmzcaqqZCFJwB3AMuBIkn/sJ0REllVEa4E9857vATyf4fE7Rv6aFLtP6mH+7Ol16766/XbbOFGYjWNVJYuICEl9EXEQ9WtD+Cmwl6S3AwPAScCf1OlcbSu3JkVuqvHcinQ93RPYOJz9sBePoTAb32pps7hf0rszjyQVEa8Df00ys+1q4JqIWFWv87WrQmtSDA2P1CVRgMdQmI13tbRZHAGcJulp4BWSqqiIiP2yCioibgZuzup4naiRJX2PoTCzWpLF0ZlHYVXbfVIPA3VMGL2TerZoC3F7hdn4Vst6Fs9I2h94b7rpvyMim0mHrGLzZ0/fos0iSx8/dCpfnbtv5sc1s/ZVywjuzwJXArumtysk/U3WgVlp+aOos+REYWaF1FINdSrJNOWvAEg6D7gP+GaWgVl5uaqhLNenmPW2yZkdy8w6Ry3JQkB+3ccIxWettjrIH1+R9U9+4XUrAdxGYWZbqCVZ/DvwgKTr0+dzge9mFpGVNHp8xdZj28dmaHiExUvXOFmY2RZqaeA+X9J/AYeRlGs/GRH9mUdmBRUaX5E1D8Azs9EqThaSpkbEswARsRxYXreobLPRU3rUs7tsjgfgmdlo1fSG6ss9kPTD7EOx0XJVTgODQwQ0JFF4AJ6ZFVJNNVR+U+rvZh2Iba0RVU47T+wmAl4eGvYAPDMrqppkEUUeW53Us+1AwDc+doATg5lVpJpksb+kX5P8n+lJH8Mbc0PtmHl041w92ygCd481s8pV3GYREV0RsWNE7BAR26SPc8+dKOpg/uzp9HR31eXYWY/8NrPOVssU5dZA222T/a/IjdiVe+655wD+j6TVklal090gabKkWyU9kd7nLzW8UNKTktZImp23/SBJK9PXLkgXEzNrC04WLSrXE2pwKNu1tHsn9bDohH1dBVWhbbbZBmBtRLwLOBQ4XdLewALgtojYC7gtfU762knADGAOcJGk3OXht4B5wF7pbU4DP4rZmNQygtvqrK9/gM9f8zAjkW0/gkk93dyz4MhMj9npdtttN4CNABGxQdJqoBc4Dnh/uttlwJ3AF9PtV0fEq8DPJT0JHJyu/7JjRNwHIOlyktkPftygj2I2Jr6yaDG5K4qsE0X3BHHWR2ZkeszxRtI0YCbwAPDWiFgHkN7vmu7WCzyX97a16bbe9PHo7aPPMU/SMknL1q9fn/lnMKtVNSO4P1fq9Yg4f+zhWL3GViw+cX9XPY2BpDcDPwTOiIhfl2huKPRClNi+5YaIi4GLAWbNmuUu6tYyqqmG2iG9nw68G1iSPj8WuCvLoMazeoytmNTT7UQxNiJJFFdGxHXpthck7RYR6yTtBryYbl8L7Jn33j2A59PtexTYbtYWquk6e3ZEnA3sAhwYEZ+PiM8DB7HlH4GNQdbzMglc/TQGkVQHvg1YPerqeQlwSvr4FOCGvO0nSdpO0ttJGrIfTKuqNkg6NO0F9Ym895i1vFraLKYCr+U9fw2Ylkk0lmmX1u22meBR2mN0zz33ALwFOFLSivR2DHAu8EFJTwAfTJ8TEauAa4DHgFuA0yMiV6/4aeA7wJPAU7hx29pILb2h/gN4MF3PIoDjgcszjcrGzMujZuPwww8HWB4Rswq8fFSh90TEOcA5BbYvA/bJNECzBqllPYtzJP0YeG+6yetZZGjx0jWZHMfLo5pZlqquhkrrW/cGdoqIfwZ+KengzCMbp7KaCyqrpGNmBrVVQ10EbAKOBL4CbCDpKfLuDOPqaKMXNMpNC97XP5DZObzanZllqZZkcUhEHCipHyAiXpK0bcZxdazRa2gPDA6x8LqVLHvmV1x5/7OZncer3ZlZlmpJFsPpXDcBIGkKyZWGVaDQoLuh4RGuyDBReKJAM8taLV1nLwCuB3aVdA5wN7Ao06g6WL2rhyb1dHuiQDPLXC29oa6UtJyk26CAuRGxOvPIOlTWCxpNEETgJVHNrK6qThaSzouILwKPF9hmZRzxzimZVTl1T5DnfDKzhqilGuqDBbYdPdZAxos7Hs9mJlHhyQHNrHGqmXX208BfAe+Q9EjeSzsA92YdWKfKss3CicLMGqWaaqjvkcxls4h0VbDUhoj4VaZRdbCs2iwmTezOIBozs8pUM+vsyxHxNMnEgS9HxDMR8QwQki6pV4CdZv7s6QUXNqhWxmsjmZmVVEubxX4RMZh7EhEvkaweNmaSTpS0StImSYUmbmt7c2f2br3iTQ1eznhtbjOzUmpJFhMk7Zx7Imky2a3l/ShwAh2+mFJvBqOrPULbzBqpln/yXwfulXQtySjuj1JgOuZa5MZrlFiysu319Q/wq1deHdMxPELbzBqtlkF5l6eD8o4g6cF5QkQ8lnlkZUiaB8wDmDp1aqNPX5Mz+1bWPMZi+2272PjaiAffmVlT1FR9lK4GtqqW90r6T+B3Crz0pYioeJnJdlvYvq9/oOZE8U9e7c7MmqyacRZ3R8ThkjbAFm20AiIidqzkOBHxgSpjbDu5KcgHBofokhgZQ9elid0TnCjMrOkqThYRcXh6v0P9wml/o6cgH0uimCD42gn7ZRWamVnNqrmy+Fyp1yPi/LEGI+l44JvAFOAmSSsiYvZYj9tIhaYgr0Wv2ybMrIVU02aRu6KYTrIq3pL0+bFk1NU1Iq4nmf68bY11Oo/D3jGZK//iPRlFY2aWjWqqoc4GkPQT4MCI2JA+Pwv4QV2ia0Njnc7j6V96OVSzaQtuanYINkotg/Kmkkz5kfMaMC2TaDrA/NnT6enuqvn9XjvbzFpRLV1n/wN4UNL1JL2ijgcuzzSqNpZrYzhrySoGa5iSY6ceTxBoZq2n6iuLiDgH+CTwEjAIfDIivpZxXG3v1ddrW5b8lddep69/IONozMzGppaV8gTsDewUEV+RNFXSwRHxYPbhtZcz+1Zy1QPPjam77PBIsHjpGveCMrOWUkubxUXAe4CT0+cbgAszi6hN5abyGEuiyHG7hZm1mlraLA6JiAMl9UMyRbmkbTOOq+1874Fs1tUGzyhrZq2nliuLYUldpFN+SJoC1FZB30E2ZTQ7lWeUNbNWVEuyuIBk4Nyuks4B7gbGbQN3X/8Ah517e03v7e4SHz90Kr2TehDJqO1FJ+zr9gozazlVVUOljdt3AcuBo0gmEZybW4divBk9D1Q53RPEm9+0DYMbhz3VuJm1laqSRUSEpL6IOAh4vE4xtY1q5oHqklh84v5ODmbWlmqphrpf0rszj6QNVdNraVOEE4WZta1aksURJAnjKUmPSFop6ZGsA2sH1fRacg8nM2tntXSdPTrzKFpcbjGj5weHtmhrmD97OvN/8DDDZbpCCdzDyczaWjXrWbwJOA34PWAl8N2IeL1egbWK0Y3YA4NDLLxuJZDMA3X2j1bx0sbSc0AFuArKzNpaNdVQlwGzSBLF0cDX6xJRiynUiD00PMLipWsAGCyTKCDpEmtm1s6qqYbaOyL2BZD0XWBczAVVrBE7t73c+hUeZGdmnaCaK4vNRejxUP2UU6xhOre91PoVHmRnZp2immSxv6Rfp7cNwH65x5J+Xa8Am6mvf4CNr22dF/OvFubO7OWPDupFRfZxomhvn/rUpyD57j+a2yZpsqRbJT2R3u+c99pCSU9KWiNpdt72g9Keg09KuiAd4GrWNipOFhHRFRE7prcdImKbvMc71jPIZsg1bBdqvB4aHuHz1zzMmX1JQ/cdj69ndH+ooeERzlqyqgGRWj39+Z//OcATozYvAG6LiL2A29LnSNobOAmYAcwBLkrnUQP4FjAP2Cu9zal37GZZqmWcxbhQbnT2SARX3P8sZ/atLNpmMTg07IWM2tz73vc+gNGXl8eRdPggvZ+bt/3qiHg1In4OPAkcLGk3YMeIuC8igmRlybmYtREniyIqHZ191QPP0VWiRiHXa8o6ylsjYh1Aer9rur0XeC5vv7Xptt708ejtW5E0T9IyScvWr1+feeBmtXKyKKLSEdcjESUXPPJCRuNKoVJDlNi+9caIiyNiVkTMmjJlSqbBmY2Fk0URpXo55euSSo6j8DQfHemFtGqJ9P7FdPtaYM+8/fYAnk+371Fgu1nbcLIoYu7MXhadsC+TerpL7nfyIXsyf/Z0uidsXXjs7pLHWHSmJcAp6eNTgBvytp8kaTtJbydpyH4wraraIOnQtBfUJ/LeY9YWapkbatyYO7OXuTN7N88Nld+Q3SVx8iF78tW5+27edtaSVQwOJb2ndp7YzZePneGus23u5JNPBngnyXIua4EvA+cC10g6FXgWOBEgIlZJugZ4jKRR/PSIyPWS+DRwKdAD/Di9mbUNRYn69nYxa9asWLZsWebHPbNvJVc98BwjEQWTg40PkpZHxKxGn7de3+t2MG3BTQ0719Pnfqhh52o11Xy3fWVRxJl9K7ni/mc3P891lQWcMMxs3HGbRRFXPfBcVdvNzDqZk0URxbrDluoma2bWqZwsiig20K7UADwzs07lZFHEyYfsWdV2M7NO5gbuInKN2O4NZWbWYslC0mLgWOA14CngkxEx2Kx4vjp3XycHMzNarxrqVmCfiNgP+BmwsMnxmJkZLZYsIuIneavw3c+W8+mYmVmTtFSyGOVTlJgSwVM5m5k1TsPbLCT9J/A7BV76UkTckO7zJZK5da4sdpyIuBi4GJJpEeoQqpmZpRqeLCLiA6Vel3QK8GHgqOiEiavMzDpAq/WGmgN8EfiDiNjYjBhyM8w+PzjE7pN6mD97umeONbNxr6WSBfAvwHbArcm0/9wfEac16uR9/QMsvG7l5rW3BwaHWHjdSgAnDDMb11oqWUTE7zXz/IuXrtmcKHKGhkdYvHSNk4WZjWut3Buq4Yqtl+11tM1svHOyyFNsvWyvo21m452TRZ75s6fT0921xbae7i6vo21m415LtVk0W65dwr2hzMy25GQxytyZvU4OZmajuBrKzMzKcrIwM7OynCzMzKwsJwszMyvLycLMzMpysjAzs7KcLMzMrCwnCzMzK8vJwszMyurYEdxexMjMLDsdmSy8iJGZWbY6shqq1CJGZmZWvY68svAiRmZWqWkLbqrpfU+f+6GMI2ltHXll4UWMzMyy1ZHJwosYmZllqyOrobyIkZlZtjoyWYAXMTIzy1LHJgsza75aG4+t9XRkm4WZmWXLycKsgSTNkbRG0pOSFjQ7HrNKuRrKrEEkdQEXAh8E1gI/lbQkIh5rbmTluTppa+NtfIaThVnjHAw8GRH/AyDpauA4oGHJwv/0m69dk0xHJIvly5f/QtIzYzzMLsAvsoinzc493s9fybnfltG5eoHn8p6vBQ7J30HSPGBe+vQ3kkrNUdPs31u+VooFWiueTGLReRlEsnUsFX+3OyJZRMSUsR5D0rKImJVFPO107vF+/gafWwW2xRZPIi4GLq7oYE3+veVrpVigteLplFjcwG3WOGuBPfOe7wE836RYzKriZGHWOD8F9pL0dknbAicBS5ock1lFOqIaKiMVXfp34LnH+/kbdu6IeF3SXwNLgS7gkohYNYZDNvv3lq+VYoHWiqcjYlFElN/LzMzGNVdDmZlZWU4WZmZWlpNFHkmLJT0u6RFJ10ua1MBznyhplaRNkhrWza6Z009IukTSi5IebeR503PvKekOSavTn/tnGx1DtSRNlnSrpCfS+51L7NslqV/Sjc2Kpd4/43LfXSUuSF9/RNKBWZ6/hnj+NI3jEUn3Stq/WbHk7fduSSOS/rjsQSPCt/QG/CGwTfr4POC8Bp77XcB04E5gVoPO2QU8BfwusC3wMLB3Az/z+4ADgUeb8LveDTgwfbwD8LNGfvYaY/5/wIL08YJS30/gc8D3gBubFUs9f8aVfHeBY4Afk4xvORR4oI6/m0ri+X1g5/Tx0fWKp9K/63S/24GbgT8ud1xfWeSJiJ9ExOvp0/tJ+sE36tyrI6LUaN162Dz9RES8BuSmn2iIiLgL+FWjzjfq3Osi4qH08QZgNckI61Z2HHBZ+vgyYG6hnSTtAXwI+E4zY6nzz7iS7+5xwOWRuB+YJGm3jM5fdTwRcW9EvJQ+ref/l0r/rv8G+CHwYiUHdbIo7lMkpZJOVmj6iVb/h5k5SdOAmcADTQ6lnLdGxDpI/hEDuxbZ75+AvwM2tUAsQF1+xpV8dxv5/a72XKdSv/8vZWOR1AscD/xrpQcdd+MsJP0n8DsFXvpSRNyQ7vMl4HXgykafu8HKTj/R6SS9maR0dUZE/LoF4in6Hanw/R8GXoyI5ZLe38xY8o5Tj59xJd/dRn6/Kz6XpCNIksXhTYzln4AvRsSIVGj3rY27ZBERHyj1uqRTgA8DR0VasdeoczfBuJ5+QlI3yT+xKyPiumbHA6W/I5JekLRbRKxLq1MKVR8cBnxE0jHAm4AdJV0RER9vQiz1/BlX8t1t5Pe7onNJ2o+kevDoiPhlE2OZBVydJopdgGMkvR4RfcUO6mqoPJLmAF8EPhIRG5sdTwOM2+knlPyVfBdYHRHnNzueCi0BTkkfnwJsdTUaEQsjYo+ImEby+7y9lkSRRSx1/hlX8t1dAnwi7RV1KPByruqsDsrGI2kqcB3wZxHxszrFUVEsEfH2iJiWfk+uBf6qVKLIvcm3N3oHPElS17civf1rA899PEmJ4FXgBWBpg857DEkvladIqsMa+fO+ClgHDKef/dQGnvtwkkvzR/J+38c08vPXEPNbgNuAJ9L7yen23YGbC+z/furXG6psLPX+GRf67gKnAaelj0Wy2NRTwErq3Muwgni+A7yU97NY1qxYRu17KRX0hvJ0H2ZmVparoczMrCwnCzMzK8vJwszMynKyMDOzspwszMysLCcLMzMry8nCmk7SNyU9JOndzY7FzApzsrCmkrQ9ySR0f0kyzYrZuNBuhSQnizGQ9BZJK9Lb/0oayHu+bcbnGsk79op0Fs+mkTRN0pCkFWX2u1PS7FHbzpB0EUBEvEKy7sGdwAXp6z3pZ3xN0i51+QBmTdSOhSQnizGIiF9GxAERcQDJVL/fyD2PZB55YPOKXWP9WQ/lHfuAiHi6kjdldO5inko/eylXkcxNk++kdDuS3gJMBDYAIwARMZQed9xMatgMWRV2JN2bxT5VnG90waluKzxmVShKn39b0mHQnoUkJ4s6Sb9kq9Mvy0PAe5W3fKikL0g6K338cUkPpl+Ub0vqquI8n5P0aHo7o8i595T0CSXLOT4s6T/y3r/VuSVtL+mmdN9HJX2sgjiKfYZrgQ9L2i4XG8n8QXenr58J/COwCti70s9tY5dVYScifr+Cc5XdpwqjC07n5r84Ot5KC0wl9htzoSh1CMmiR21ZSHKyqK/pJCt1zQSeKbSDpHcBHwMOS78oI8CfFtg1V+pYIen69L0HAZ8k+RIeCvyFpJkFzv1mkjUIjoyI/YHPljn3HOD5iNg/IvYBbin1IUt9hkimYX4wPSYkf0Dfj4hIE8fvA98nWUVtRqnzWOMUKXD0SVquZD3teXn7/mbUe/4t3ecnknqq2OfvJT2uZE3vqyR9YQzxvrdA/BUVrCo4V6GCUclCUfo38rOIGEkP03aFJCeL+nomkuUcSzkKOAj4aXqpexTJ2rmj5Zemjk+3HQ5cHxGvRMRvSKY/fm+Bcx8JXBsRvwCIiNxSpsXOvRL4gKTzJL03Il4e42fIL3Xll7a+CnwlktksnSxaz+YCR0Q8A3wqIg4iWQvhM2npeLS9gAsjYgYwCPxRJftImpXuOxM4IT1HMfkFpxV5V76jC2f5z3ehgoJV+jmLKlYwKlUoSp8fTVroatdC0rhb/KjBXsl7/DpbJuc3pfcCLouIhTUcv9QSV/nnFoVX7Sp67vSq5RhgkaSfRMRXysRR6jP0AedLOhDoiYiHJB1A8k/hcEkXkvw8VpY4hzXe6MLOZyTlCip7kvzTH72Az88jYkX6eDkwrcBxC+2zC3BDRAwBSPpRibhy1TWbpf+AR8eb/3xzwSrdP1ewWlLgfaXkF4wAenhj4adcoeiG9P5Tee+bTZKsIK+QJKltkoWvLBrnBWBXJY2K2/FGD4jbgD+WtCuApMmS3lbhMe8C5kqaqKR3xfHAfxfY7zbgo7mSoKTJpc4taXdgY0RcQXKpfGCZOEp+hvSq507gEt64qjgPODbeWIBlf9rkj2Yc2VzgULJE6weA96RVmf28UeDJ92re4xEKF0gL7VPZ2p6lvVLieaUFq3JyBaPcVf70iDgrfa0POCq/UAQgaSIwKSKezyskXSjpaeDvaZPvvZNFg0TEMPAVkgXrbwQeT7c/RlJ/+RNJjwC3kvSSqOSYD5EsXPJgetzvRER/gf1WAecA/yXpYeD8MufeF3gwrVL6EklJqFQclXyGq0gSwtWSjgS2j4jb8o7xArB9XiKz1rIT8FJEbJT0TpKqnCzdDRwr6U1K1uz+UMbHr7RgVU7RglGRQhHAEcAd6eO2LSS5GiojeaWL3POngX1GbbuAtJvcqO3fJ6m/LHX8NxfZfj7pP/8y574MuKyKcy8tFU8Vx8m9fj1vlO4eB24vsM9O1ZzTGuoW4LS0MLCGtFdPViLip5KWAA+TtDcsA4q1lfVoy66st5D05ip1/IckXUpSsIK0YKUqxytFxGOScgWjCSSrPJ7OGx1YriJpO8zvGXU0cG2xQpKS3oeT89oSW5JXyrOaSNoTuBf4ZQXdCqs9dg9wHzAF2LfV/4gsG5LeHBG/Satt7gLm5apymhjTNJKlafcpt2+JYzwEHJLWLlSy/9MkS8D+otZz1oOvLKwmEfEcFXQzrPHYQ8AB9Ti2tbSLJe1N0hZyWbMTRWoE2EnSiloLRRFRrs0P2KKQ1A1squVc9eQrCzMzK8sN3GZmVpaThZmZleVkYWZmZTlZmJlZWU4WZmZWlpOFmZmV5WRhZmZl/X83olvE85TeAgAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plots.force_scatter_histogram(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "67951e44-0b40-493e-ab36-b96053966b5b",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plots.force_scatter_histogram(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "0c32e69e-9f2b-48b4-b8e8-8d1e39db0062",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plots.force_scatter_histogram(axis=2)"
]
},
{
"cell_type": "markdown",
"id": "21cf9787-4b08-49d6-a0c8-638dff820788",
"metadata": {},
"source": [
"# Appendix\n",
"\n",
"For the potential used in the validation and phase diagram calculations a different functional form was used.\n",
"\n",
"The Al potential followed the same formalism as used by Mishin in his Cu potential https://doi.org/10.1103/PhysRevB.63.224106.\n",
"For Li and AlLi terms the pair interaction was described using a morse-C potential, while cubic splines were used for the electron densities.\n",
"The Li embedding term was a modified version of that used by Mishin that is implemented in the atomicrex code.\n",
"\n",
"To be able to guarantee certain properties the parameters of these functions were constrained.\n",
"EAM potentials are invariant under the transformations: \n",
"$\\rho(r)\\rightarrow s\\rho(r)$, $F(\\bar\\rho)\\rightarrow F(\\bar\\rho / s)$ and $F(\\bar\\rho)\\rightarrow F(\\bar\\rho) - g\\bar\\rho$, $V(r)\\rightarrow V(r)+2g\\rho(r)$\n",
"\n",
"To uniquely define them the condition:\n",
"$\\sum_m N_m \\rho(r_m) = 1$ was applied. Here $N_m$ is the number of neighbors of the equilibrium structure in shell m.\n",
"\n",
"\n",
"A perfect lattice constant can be obtained by applying the mechanical equilibrium condition: \n",
"$\\frac{1}{2} \\sum_m N_m V(r_m)' r_m + F_0' \\sum_m N_m \\rho(r_m)' r_m$ where $F0$ is the value of the embedding function at the equilibrium structures electron density, i.e. $F(1)$ if the above condition is applied.\n",
"\n",
"The correct equilibrium energy $E_0$ can be guaranteed with: \n",
"$F_0 = E_0-\\frac{1}{2}\\sum_m N_m V(r_m)$\n",
"\n",
"An exact fit to the bulk modulus can be achieved by applying: \n",
"$\\frac{1}{2}\\sum_m N_mV(r_m)''r_m^2 + F_0' \\sum_m N_m \\rho(r_m)'' R_m^2 + F_0''(\\sum_m N_m \\rho(r_m)' r_m)^2 $\n",
"\n",
"These (or similar equations) have to be solved with respect to some parameter in the function. A simple example is the electron density which can be constrained to 1 by multiplying the whole function with some factor c after its evaluation for the equilibrium structure.\n",
"This then looks f.e. like this in the atomicrex input file (xml format):\n",
"\n",
"```\n",
" <constraint id=\"one_constrain_rhoSpline_LiLi\" dependent-dof=\"EAM.rho_LiLi.cfactor_rhoSpline_LiLi.const\">\n",
" <expression>1 / ( + 8 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.2.966650281143688} + 6 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.3.425592676819574} + 12 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.4.844519622724197} + 24 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.5.6807027967998565} + 8 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.5.933300562287376} + 6 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.6.851185353639148} + 24 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.7.465906149994755})</expression>\n",
" </constraint>\n",
"```\n",
"The block contains an identifier, the \"path\" to the parameter that is constrained and the expression that is applied to constrain it.\n",
"In the pyiron interface such constraints can be added using as follows:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "0f10fa0c-e266-4064-99c7-94db3d739d7a",
"metadata": {},
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"source": [
"job.input.parameter_constraints.add_constraint(\n",
" identifier=\"Identifier\",\n",
" dependent_dof=\"The parameter or degree of freedom to constrain\",\n",
" expression=\"Expression that is used to determine the value of the parameter, parsed using muparser\"\n",
")"
]
},
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"source": [
"If any questions regarding the interface/feature requests/problems occur after the workshop feel free to send me a mail leimeroth@mm.tu-darmstadt.de"
]
}
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