{
"cells": [
{
"cell_type": "markdown",
"id": "25d5b0d5-f330-4dcb-9b7c-f57c4bea9596",
"metadata": {},
"source": [
"# **Workshop: From electrons to phase diagrams**\n",
"\n",
"# Day 2: Validation of the potentials\n",
"\n",
"Once we have the fitted potentials, it is necessary to validate them in order to assess their quality with respect to applications.\n",
"\n",
"In this exercise, we use the fitted potentials and perform some basic calculations."
]
},
{
"cell_type": "markdown",
"id": "4756d4c9-304a-4ccc-b772-ba67d008c5a4",
"metadata": {},
"source": [
"## Import the fitted potentials for Li-Al (from earlier excercise)\n",
"\n",
"The same directory contains a `helper.py` file which among other things, also contains the necessary specifications of each of the potentials that we will use today. Individual potentials are descrbed in the LAMMPS format as:\n",
"```\n",
"pot_eam = pd.DataFrame({\n",
" 'Name': ['LiAl_eam'],\n",
" 'Filename': [[\"../potentials/AlLi.eam.fs\")]],\n",
" 'Model': [\"EAM\"],\n",
" 'Species': [['Li', 'Al']],\n",
" 'Config': [['pair_style eam/fs\\n', 'pair_coeff * * AlLi.eam.fs Li Al\\n']]\n",
"})\n",
"\n",
"```\n",
"A list of such DataFrames describing the potentials is saved in a list called `potentials_list`. We import the list as:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b90e0ac0",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Filename</th>\n",
" <th>Model</th>\n",
" <th>Species</th>\n",
" <th>Config</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LiAl_eam</td>\n",
" <td>[/home/jovyan/workshop_preparation/potentials/...</td>\n",
" <td>EAM</td>\n",
" <td>[Li, Al]</td>\n",
" <td>[pair_style eam/fs\\n, pair_coeff * * AlLi.eam....</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Filename Model \\\n",
"0 LiAl_eam [/home/jovyan/workshop_preparation/potentials/... EAM \n",
"\n",
" Species Config \n",
"0 [Li, Al] [pair_style eam/fs\\n, pair_coeff * * AlLi.eam.... "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from helper import potentials_list\n",
"\n",
"# potentials_list = [potentials_list[1]]\n",
"\n",
"# display the first element in the list\n",
"# which is an EAM potential\n",
"potentials_list[0]"
]
},
{
"cell_type": "markdown",
"id": "4c84560c",
"metadata": {},
"source": [
"### Import other important modules"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "83f7a2c9-d45a-4987-9e35-59badd754d4f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1654601899.1397257"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pylab as plt\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"import time\n",
"from helper import get_clean_project_name\n",
"from pyiron_atomistics import Project\n",
"from pyiron import pyiron_to_ase\n",
"import pyiron_gpl\n",
"\n",
"# save start time to record runtime of the notebook\n",
"time_start = time.time()\n",
"time_start"
]
},
{
"cell_type": "markdown",
"id": "acc0ee8f",
"metadata": {},
"source": [
"### Create a new project to perform validation calculations\n",
"\n",
"It is useful to create a new project directory for every kind of calculation. Pyiron will automatically create subdirectories for each potential and property we calculate. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "706be2a9-5f94-4eb5-8e4f-6c349fe216b3",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e51d75e54818412eb80fb490eb51d974",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/551 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pr = Project(\"validation_LiAl\")\n",
"\n",
"# remove earlier jobs\n",
"pr.remove_jobs(silently=True, recursive=True)"
]
},
{
"cell_type": "markdown",
"id": "3b84ed62-e841-4526-893e-dc4f61477c88",
"metadata": {},
"source": [
"### Define the important pases to consider for validation\n",
"\n",
"We construct a python dictionary `struct_dict` which contains a description of all the important phases that we want to consider for this exercise. The descriptions given in the dictionary will be later used by Pyiron to generate or read the structural configurations for the respective phases.\n",
"\n",
"For unary phases, we provide an initial guess for the lattice parameter and use pyiron to generate the structural prototype.\n",
"\n",
"For binary phases, we provide a phase name and an additional dictionary `fl_dict` which maps the phase name to a `.cif` file saved in a subdirectory. Pyiron will use this information to read the respective configurations from the file."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "28778cef-2a07-4794-888f-7239500e7b5a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Al': {'s_murn': ['fcc', 'bcc'], 'a': 4.04},\n",
" 'Li': {'s_murn': ['bcc', 'fcc'], 'a': 3.5},\n",
" 'Li2Al2': {'s_murn': ['Li2Al2_cubic']},\n",
" 'LiAl3': {'s_murn': ['LiAl3_cubic']},\n",
" 'Li9Al4': {'s_murn': ['Li9Al4_monoclinic']},\n",
" 'Li3Al2': {'s_murn': ['Li3Al2_trigonal']},\n",
" 'Li4Al4': {'s_murn': ['Li4Al4_cubic']}}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"struct_dict = dict()\n",
"struct_dict[\"Al\"] = dict()\n",
"struct_dict[\"Al\"][\"s_murn\"] = [\"fcc\",\"bcc\"]\n",
"struct_dict[\"Al\"][\"a\"] = 4.04\n",
"\n",
"struct_dict[\"Li\"] = dict()\n",
"struct_dict[\"Li\"][\"s_murn\"] = [\"bcc\",\"fcc\"]\n",
"struct_dict[\"Li\"][\"a\"] = 3.5\n",
"\n",
"\n",
"\n",
"\n",
"struct_dict[\"Li2Al2\"] = dict()\n",
"struct_dict[\"Li2Al2\"][\"s_murn\"] = [\"Li2Al2_cubic\"]\n",
"# struct_dict[\"Li2Al2\"][\"a\"] = 3.7\n",
"\n",
"struct_dict[\"LiAl3\"] = dict()\n",
"struct_dict[\"LiAl3\"][\"s_murn\"] = [\"LiAl3_tetragonal\"]\n",
"# struct_dict[\"LiAl3\"][\"a\"] = 3.7\n",
"\n",
"struct_dict[\"LiAl3\"] = dict()\n",
"struct_dict[\"LiAl3\"][\"s_murn\"] = [\"LiAl3_cubic\"]\n",
"# struct_dict[\"LiAl3\"][\"a\"] = 3.7\n",
"\n",
"struct_dict[\"Li9Al4\"] = dict()\n",
"struct_dict[\"Li9Al4\"][\"s_murn\"] = [\"Li9Al4_monoclinic\"]\n",
"# struct_dict[\"Li9Al4\"][\"a\"] = 3.7\n",
"\n",
"struct_dict[\"Li3Al2\"] = dict()\n",
"struct_dict[\"Li3Al2\"][\"s_murn\"] = [\"Li3Al2_trigonal\"]\n",
"# struct_dict[\"Li3Al2\"][\"a\"] = 3.7\n",
"\n",
"struct_dict[\"Li4Al4\"] = dict()\n",
"struct_dict[\"Li4Al4\"][\"s_murn\"] = [\"Li4Al4_cubic\"]\n",
"\n",
"struct_dict"
]
},
{
"cell_type": "markdown",
"id": "23b2e6d9",
"metadata": {},
"source": [
"a dictionary is described to map the binary phases to their file locations"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c1820db7",
"metadata": {},
"outputs": [],
"source": [
"fl_dict = {\"Li2Al2_cubic\": \"mp_structures/LiAl_mp-1067_primitive.cif\",\n",
" \"LiAl3_tetragonal\":\"mp_structures/LiAl3_mp-975906_primitive.cif\",\n",
" \"LiAl3_cubic\":\"mp_structures/LiAl3_mp-10890_primitive.cif\",\n",
" \"Li9Al4_monoclinic\":\"mp_structures/Li9Al4_mp-568404_primitive.cif\",\n",
" \"Li3Al2_trigonal\":\"mp_structures/Al2Li3-6021.cif\",\n",
" \"Li4Al4_cubic\":\"mp_structures/LiAl_mp-1079240_primitive.cif\"}"
]
},
{
"cell_type": "markdown",
"id": "198e9745-734a-4502-8f1b-0330ba8c8fca",
"metadata": {},
"source": [
"## (a) Ground state: E-V curves\n",
"\n",
"Using a series of nested `for` loops, we calculate the murnaghan EV-curves using all three potentials for all the defined structures.\n",
"\n",
"We loop over:\n",
" - All the potentials defined in `potentials_list` and name the project according to the potential\n",
" - All the chemical formulae defined in the keys of `struct_dict`\n",
" - All phases defined for a given chemical formula\n",
" \n",
"Within the loops, the first step is to get the structure basis on which we will perform the calculations. \n",
"\n",
"- For unary phases, we use the pyiron function `pr_pot.create_ase_bulk(compound, crys_structure, a=compound_dict[\"a\"])` \n",
"- For binary structures, we read the basis using `pr.create.structure.ase.read(fl_path)` with the `fl_path` given by `fl_dict` defined earlier.\n",
"\n",
"Once the structure and potential is defined as part of the pr_job, we run two calculations:\n",
"- `job_relax` to relax the structure to the ground state\n",
"- `murn_job` to calculate the energies in a small volume range around the equilibrium\n",
"\n",
"As the calculations are being performed, the status(s) of each calculation is printed. If a job is already calculated, the calculations are not re-run but rather re-read from the saved data."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "13f095d2-44d7-4711-b9a5-d58a95af42f6",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job Al_fcc_relax was saved and received the ID: 1\n",
"The job murn_job_Al_fcc was saved and received the ID: 2\n",
"The job murn_job_Al_fcc_0_9 was saved and received the ID: 3\n",
"The job murn_job_Al_fcc_0_92 was saved and received the ID: 4\n",
"The job murn_job_Al_fcc_0_94 was saved and received the ID: 5\n",
"The job murn_job_Al_fcc_0_96 was saved and received the ID: 6\n",
"The job murn_job_Al_fcc_0_98 was saved and received the ID: 7\n",
"The job murn_job_Al_fcc_1_0 was saved and received the ID: 8\n",
"The job murn_job_Al_fcc_1_02 was saved and received the ID: 9\n",
"The job murn_job_Al_fcc_1_04 was saved and received the ID: 10\n",
"The job murn_job_Al_fcc_1_06 was saved and received the ID: 11\n",
"The job murn_job_Al_fcc_1_08 was saved and received the ID: 12\n",
"The job murn_job_Al_fcc_1_1 was saved and received the ID: 13\n",
"The job Al_bcc_relax was saved and received the ID: 14\n",
"The job murn_job_Al_bcc was saved and received the ID: 15\n",
"The job murn_job_Al_bcc_0_9 was saved and received the ID: 16\n",
"The job murn_job_Al_bcc_0_92 was saved and received the ID: 17\n",
"The job murn_job_Al_bcc_0_94 was saved and received the ID: 18\n",
"The job murn_job_Al_bcc_0_96 was saved and received the ID: 19\n",
"The job murn_job_Al_bcc_0_98 was saved and received the ID: 20\n",
"The job murn_job_Al_bcc_1_0 was saved and received the ID: 21\n",
"The job murn_job_Al_bcc_1_02 was saved and received the ID: 22\n",
"The job murn_job_Al_bcc_1_04 was saved and received the ID: 23\n",
"The job murn_job_Al_bcc_1_06 was saved and received the ID: 24\n",
"The job murn_job_Al_bcc_1_08 was saved and received the ID: 25\n",
"The job murn_job_Al_bcc_1_1 was saved and received the ID: 26\n",
"The job Li_bcc_relax was saved and received the ID: 27\n",
"The job murn_job_Li_bcc was saved and received the ID: 28\n",
"The job murn_job_Li_bcc_0_9 was saved and received the ID: 29\n",
"The job murn_job_Li_bcc_0_92 was saved and received the ID: 30\n",
"The job murn_job_Li_bcc_0_94 was saved and received the ID: 31\n",
"The job murn_job_Li_bcc_0_96 was saved and received the ID: 32\n",
"The job murn_job_Li_bcc_0_98 was saved and received the ID: 33\n",
"The job murn_job_Li_bcc_1_0 was saved and received the ID: 34\n",
"The job murn_job_Li_bcc_1_02 was saved and received the ID: 35\n",
"The job murn_job_Li_bcc_1_04 was saved and received the ID: 36\n",
"The job murn_job_Li_bcc_1_06 was saved and received the ID: 37\n",
"The job murn_job_Li_bcc_1_08 was saved and received the ID: 38\n",
"The job murn_job_Li_bcc_1_1 was saved and received the ID: 39\n",
"The job Li_fcc_relax was saved and received the ID: 40\n",
"The job murn_job_Li_fcc was saved and received the ID: 41\n",
"The job murn_job_Li_fcc_0_9 was saved and received the ID: 42\n",
"The job murn_job_Li_fcc_0_92 was saved and received the ID: 43\n",
"The job murn_job_Li_fcc_0_94 was saved and received the ID: 44\n",
"The job murn_job_Li_fcc_0_96 was saved and received the ID: 45\n",
"The job murn_job_Li_fcc_0_98 was saved and received the ID: 46\n",
"The job murn_job_Li_fcc_1_0 was saved and received the ID: 47\n",
"The job murn_job_Li_fcc_1_02 was saved and received the ID: 48\n",
"The job murn_job_Li_fcc_1_04 was saved and received the ID: 49\n",
"The job murn_job_Li_fcc_1_06 was saved and received the ID: 50\n",
"The job murn_job_Li_fcc_1_08 was saved and received the ID: 51\n",
"The job murn_job_Li_fcc_1_1 was saved and received the ID: 52\n",
"The job Li2Al2_Li2Al2_cubic_relax was saved and received the ID: 53\n",
"The job murn_job_Li2Al2_Li2Al2_cubic was saved and received the ID: 54\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_9 was saved and received the ID: 55\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_92 was saved and received the ID: 56\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_94 was saved and received the ID: 57\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_96 was saved and received the ID: 58\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_98 was saved and received the ID: 59\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_0 was saved and received the ID: 60\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_02 was saved and received the ID: 61\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_04 was saved and received the ID: 62\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_06 was saved and received the ID: 63\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_08 was saved and received the ID: 64\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_1 was saved and received the ID: 65\n",
"The job LiAl3_LiAl3_cubic_relax was saved and received the ID: 66\n",
"The job murn_job_LiAl3_LiAl3_cubic was saved and received the ID: 67\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_9 was saved and received the ID: 68\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_92 was saved and received the ID: 69\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_94 was saved and received the ID: 70\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_96 was saved and received the ID: 71\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_98 was saved and received the ID: 72\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_0 was saved and received the ID: 73\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_02 was saved and received the ID: 74\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_04 was saved and received the ID: 75\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_06 was saved and received the ID: 76\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_08 was saved and received the ID: 77\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_1 was saved and received the ID: 78\n",
"The job Li9Al4_Li9Al4_monoclinic_relax was saved and received the ID: 79\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic was saved and received the ID: 80\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_9 was saved and received the ID: 81\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_92 was saved and received the ID: 82\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_94 was saved and received the ID: 83\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_96 was saved and received the ID: 84\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_98 was saved and received the ID: 85\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_0 was saved and received the ID: 86\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_02 was saved and received the ID: 87\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_04 was saved and received the ID: 88\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_06 was saved and received the ID: 89\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_08 was saved and received the ID: 90\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_1 was saved and received the ID: 91\n",
"The job Li3Al2_Li3Al2_trigonal_relax was saved and received the ID: 92\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal was saved and received the ID: 93\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_9 was saved and received the ID: 94\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_92 was saved and received the ID: 95\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_94 was saved and received the ID: 96\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_96 was saved and received the ID: 97\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_98 was saved and received the ID: 98\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_0 was saved and received the ID: 99\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_02 was saved and received the ID: 100\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_04 was saved and received the ID: 101\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_06 was saved and received the ID: 102\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_08 was saved and received the ID: 103\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_1 was saved and received the ID: 104\n",
"The job Li4Al4_Li4Al4_cubic_relax was saved and received the ID: 105\n",
"The job murn_job_Li4Al4_Li4Al4_cubic was saved and received the ID: 106\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_9 was saved and received the ID: 107\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_92 was saved and received the ID: 108\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_94 was saved and received the ID: 109\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_96 was saved and received the ID: 110\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_98 was saved and received the ID: 111\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_0 was saved and received the ID: 112\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_02 was saved and received the ID: 113\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_04 was saved and received the ID: 114\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_06 was saved and received the ID: 115\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_08 was saved and received the ID: 116\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_1 was saved and received the ID: 117\n",
"The job Al_fcc_relax was saved and received the ID: 118\n",
"The job murn_job_Al_fcc was saved and received the ID: 119\n",
"The job murn_job_Al_fcc_0_9 was saved and received the ID: 120\n",
"The job murn_job_Al_fcc_0_92 was saved and received the ID: 121\n",
"The job murn_job_Al_fcc_0_94 was saved and received the ID: 122\n",
"The job murn_job_Al_fcc_0_96 was saved and received the ID: 123\n",
"The job murn_job_Al_fcc_0_98 was saved and received the ID: 124\n",
"The job murn_job_Al_fcc_1_0 was saved and received the ID: 125\n",
"The job murn_job_Al_fcc_1_02 was saved and received the ID: 126\n",
"The job murn_job_Al_fcc_1_04 was saved and received the ID: 127\n",
"The job murn_job_Al_fcc_1_06 was saved and received the ID: 128\n",
"The job murn_job_Al_fcc_1_08 was saved and received the ID: 129\n",
"The job murn_job_Al_fcc_1_1 was saved and received the ID: 130\n",
"The job Al_bcc_relax was saved and received the ID: 131\n",
"The job murn_job_Al_bcc was saved and received the ID: 132\n",
"The job murn_job_Al_bcc_0_9 was saved and received the ID: 133\n",
"The job murn_job_Al_bcc_0_92 was saved and received the ID: 134\n",
"The job murn_job_Al_bcc_0_94 was saved and received the ID: 135\n",
"The job murn_job_Al_bcc_0_96 was saved and received the ID: 136\n",
"The job murn_job_Al_bcc_0_98 was saved and received the ID: 137\n",
"The job murn_job_Al_bcc_1_0 was saved and received the ID: 138\n",
"The job murn_job_Al_bcc_1_02 was saved and received the ID: 139\n",
"The job murn_job_Al_bcc_1_04 was saved and received the ID: 140\n",
"The job murn_job_Al_bcc_1_06 was saved and received the ID: 141\n",
"The job murn_job_Al_bcc_1_08 was saved and received the ID: 142\n",
"The job murn_job_Al_bcc_1_1 was saved and received the ID: 143\n",
"The job Li_bcc_relax was saved and received the ID: 144\n",
"The job murn_job_Li_bcc was saved and received the ID: 145\n",
"The job murn_job_Li_bcc_0_9 was saved and received the ID: 146\n",
"The job murn_job_Li_bcc_0_92 was saved and received the ID: 147\n",
"The job murn_job_Li_bcc_0_94 was saved and received the ID: 148\n",
"The job murn_job_Li_bcc_0_96 was saved and received the ID: 149\n",
"The job murn_job_Li_bcc_0_98 was saved and received the ID: 150\n",
"The job murn_job_Li_bcc_1_0 was saved and received the ID: 151\n",
"The job murn_job_Li_bcc_1_02 was saved and received the ID: 152\n",
"The job murn_job_Li_bcc_1_04 was saved and received the ID: 153\n",
"The job murn_job_Li_bcc_1_06 was saved and received the ID: 154\n",
"The job murn_job_Li_bcc_1_08 was saved and received the ID: 155\n",
"The job murn_job_Li_bcc_1_1 was saved and received the ID: 156\n",
"The job Li_fcc_relax was saved and received the ID: 157\n",
"The job murn_job_Li_fcc was saved and received the ID: 158\n",
"The job murn_job_Li_fcc_0_9 was saved and received the ID: 159\n",
"The job murn_job_Li_fcc_0_92 was saved and received the ID: 160\n",
"The job murn_job_Li_fcc_0_94 was saved and received the ID: 161\n",
"The job murn_job_Li_fcc_0_96 was saved and received the ID: 162\n",
"The job murn_job_Li_fcc_0_98 was saved and received the ID: 163\n",
"The job murn_job_Li_fcc_1_0 was saved and received the ID: 164\n",
"The job murn_job_Li_fcc_1_02 was saved and received the ID: 165\n",
"The job murn_job_Li_fcc_1_04 was saved and received the ID: 166\n",
"The job murn_job_Li_fcc_1_06 was saved and received the ID: 167\n",
"The job murn_job_Li_fcc_1_08 was saved and received the ID: 168\n",
"The job murn_job_Li_fcc_1_1 was saved and received the ID: 169\n",
"The job Li2Al2_Li2Al2_cubic_relax was saved and received the ID: 170\n",
"The job murn_job_Li2Al2_Li2Al2_cubic was saved and received the ID: 171\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_9 was saved and received the ID: 172\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_92 was saved and received the ID: 173\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_94 was saved and received the ID: 174\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_96 was saved and received the ID: 175\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_98 was saved and received the ID: 176\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_0 was saved and received the ID: 177\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_02 was saved and received the ID: 178\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_04 was saved and received the ID: 179\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_06 was saved and received the ID: 180\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_08 was saved and received the ID: 181\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_1 was saved and received the ID: 182\n",
"The job LiAl3_LiAl3_cubic_relax was saved and received the ID: 183\n",
"The job murn_job_LiAl3_LiAl3_cubic was saved and received the ID: 184\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_9 was saved and received the ID: 185\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_92 was saved and received the ID: 186\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_94 was saved and received the ID: 187\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_96 was saved and received the ID: 188\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_98 was saved and received the ID: 189\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_0 was saved and received the ID: 190\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_02 was saved and received the ID: 191\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_04 was saved and received the ID: 192\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_06 was saved and received the ID: 193\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_08 was saved and received the ID: 194\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_1 was saved and received the ID: 195\n",
"The job Li9Al4_Li9Al4_monoclinic_relax was saved and received the ID: 196\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic was saved and received the ID: 197\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_9 was saved and received the ID: 198\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_92 was saved and received the ID: 199\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_94 was saved and received the ID: 200\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_96 was saved and received the ID: 201\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_98 was saved and received the ID: 202\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_0 was saved and received the ID: 203\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_02 was saved and received the ID: 204\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_04 was saved and received the ID: 205\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_06 was saved and received the ID: 206\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_08 was saved and received the ID: 207\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_1 was saved and received the ID: 208\n",
"The job Li3Al2_Li3Al2_trigonal_relax was saved and received the ID: 209\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal was saved and received the ID: 210\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_9 was saved and received the ID: 211\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_92 was saved and received the ID: 212\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_94 was saved and received the ID: 213\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_96 was saved and received the ID: 214\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_98 was saved and received the ID: 215\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_0 was saved and received the ID: 216\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_02 was saved and received the ID: 217\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_04 was saved and received the ID: 218\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_06 was saved and received the ID: 219\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_08 was saved and received the ID: 220\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_1 was saved and received the ID: 221\n",
"The job Li4Al4_Li4Al4_cubic_relax was saved and received the ID: 222\n",
"The job murn_job_Li4Al4_Li4Al4_cubic was saved and received the ID: 223\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_9 was saved and received the ID: 224\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_92 was saved and received the ID: 225\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_94 was saved and received the ID: 226\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_96 was saved and received the ID: 227\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_98 was saved and received the ID: 228\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_0 was saved and received the ID: 229\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_02 was saved and received the ID: 230\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_04 was saved and received the ID: 231\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_06 was saved and received the ID: 232\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_08 was saved and received the ID: 233\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_1 was saved and received the ID: 234\n",
"The job Al_fcc_relax was saved and received the ID: 235\n",
"The job murn_job_Al_fcc was saved and received the ID: 236\n",
"The job murn_job_Al_fcc_0_9 was saved and received the ID: 237\n",
"The job murn_job_Al_fcc_0_92 was saved and received the ID: 238\n",
"The job murn_job_Al_fcc_0_94 was saved and received the ID: 239\n",
"The job murn_job_Al_fcc_0_96 was saved and received the ID: 240\n",
"The job murn_job_Al_fcc_0_98 was saved and received the ID: 241\n",
"The job murn_job_Al_fcc_1_0 was saved and received the ID: 242\n",
"The job murn_job_Al_fcc_1_02 was saved and received the ID: 243\n",
"The job murn_job_Al_fcc_1_04 was saved and received the ID: 244\n",
"The job murn_job_Al_fcc_1_06 was saved and received the ID: 245\n",
"The job murn_job_Al_fcc_1_08 was saved and received the ID: 246\n",
"The job murn_job_Al_fcc_1_1 was saved and received the ID: 247\n",
"The job Al_bcc_relax was saved and received the ID: 248\n",
"The job murn_job_Al_bcc was saved and received the ID: 249\n",
"The job murn_job_Al_bcc_0_9 was saved and received the ID: 250\n",
"The job murn_job_Al_bcc_0_92 was saved and received the ID: 251\n",
"The job murn_job_Al_bcc_0_94 was saved and received the ID: 252\n",
"The job murn_job_Al_bcc_0_96 was saved and received the ID: 253\n",
"The job murn_job_Al_bcc_0_98 was saved and received the ID: 254\n",
"The job murn_job_Al_bcc_1_0 was saved and received the ID: 255\n",
"The job murn_job_Al_bcc_1_02 was saved and received the ID: 256\n",
"The job murn_job_Al_bcc_1_04 was saved and received the ID: 257\n",
"The job murn_job_Al_bcc_1_06 was saved and received the ID: 258\n",
"The job murn_job_Al_bcc_1_08 was saved and received the ID: 259\n",
"The job murn_job_Al_bcc_1_1 was saved and received the ID: 260\n",
"The job Li_bcc_relax was saved and received the ID: 261\n",
"The job murn_job_Li_bcc was saved and received the ID: 262\n",
"The job murn_job_Li_bcc_0_9 was saved and received the ID: 263\n",
"The job murn_job_Li_bcc_0_92 was saved and received the ID: 264\n",
"The job murn_job_Li_bcc_0_94 was saved and received the ID: 265\n",
"The job murn_job_Li_bcc_0_96 was saved and received the ID: 266\n",
"The job murn_job_Li_bcc_0_98 was saved and received the ID: 267\n",
"The job murn_job_Li_bcc_1_0 was saved and received the ID: 268\n",
"The job murn_job_Li_bcc_1_02 was saved and received the ID: 269\n",
"The job murn_job_Li_bcc_1_04 was saved and received the ID: 270\n",
"The job murn_job_Li_bcc_1_06 was saved and received the ID: 271\n",
"The job murn_job_Li_bcc_1_08 was saved and received the ID: 272\n",
"The job murn_job_Li_bcc_1_1 was saved and received the ID: 273\n",
"The job Li_fcc_relax was saved and received the ID: 274\n",
"The job murn_job_Li_fcc was saved and received the ID: 275\n",
"The job murn_job_Li_fcc_0_9 was saved and received the ID: 276\n",
"The job murn_job_Li_fcc_0_92 was saved and received the ID: 277\n",
"The job murn_job_Li_fcc_0_94 was saved and received the ID: 278\n",
"The job murn_job_Li_fcc_0_96 was saved and received the ID: 279\n",
"The job murn_job_Li_fcc_0_98 was saved and received the ID: 280\n",
"The job murn_job_Li_fcc_1_0 was saved and received the ID: 281\n",
"The job murn_job_Li_fcc_1_02 was saved and received the ID: 282\n",
"The job murn_job_Li_fcc_1_04 was saved and received the ID: 283\n",
"The job murn_job_Li_fcc_1_06 was saved and received the ID: 284\n",
"The job murn_job_Li_fcc_1_08 was saved and received the ID: 285\n",
"The job murn_job_Li_fcc_1_1 was saved and received the ID: 286\n",
"The job Li2Al2_Li2Al2_cubic_relax was saved and received the ID: 287\n",
"The job murn_job_Li2Al2_Li2Al2_cubic was saved and received the ID: 288\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_9 was saved and received the ID: 289\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_92 was saved and received the ID: 290\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_94 was saved and received the ID: 291\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_96 was saved and received the ID: 292\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_0_98 was saved and received the ID: 293\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_0 was saved and received the ID: 294\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_02 was saved and received the ID: 295\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_04 was saved and received the ID: 296\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_06 was saved and received the ID: 297\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_08 was saved and received the ID: 298\n",
"The job murn_job_Li2Al2_Li2Al2_cubic_1_1 was saved and received the ID: 299\n",
"The job LiAl3_LiAl3_cubic_relax was saved and received the ID: 300\n",
"The job murn_job_LiAl3_LiAl3_cubic was saved and received the ID: 301\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_9 was saved and received the ID: 302\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_92 was saved and received the ID: 303\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_94 was saved and received the ID: 304\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_96 was saved and received the ID: 305\n",
"The job murn_job_LiAl3_LiAl3_cubic_0_98 was saved and received the ID: 306\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_0 was saved and received the ID: 307\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_02 was saved and received the ID: 308\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_04 was saved and received the ID: 309\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_06 was saved and received the ID: 310\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_08 was saved and received the ID: 311\n",
"The job murn_job_LiAl3_LiAl3_cubic_1_1 was saved and received the ID: 312\n",
"The job Li9Al4_Li9Al4_monoclinic_relax was saved and received the ID: 313\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic was saved and received the ID: 314\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_9 was saved and received the ID: 315\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_92 was saved and received the ID: 316\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_94 was saved and received the ID: 317\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_96 was saved and received the ID: 318\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_0_98 was saved and received the ID: 319\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_0 was saved and received the ID: 320\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_02 was saved and received the ID: 321\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_04 was saved and received the ID: 322\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_06 was saved and received the ID: 323\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_08 was saved and received the ID: 324\n",
"The job murn_job_Li9Al4_Li9Al4_monoclinic_1_1 was saved and received the ID: 325\n",
"The job Li3Al2_Li3Al2_trigonal_relax was saved and received the ID: 326\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal was saved and received the ID: 327\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_9 was saved and received the ID: 328\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_92 was saved and received the ID: 329\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_94 was saved and received the ID: 330\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_96 was saved and received the ID: 331\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_0_98 was saved and received the ID: 332\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_0 was saved and received the ID: 333\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_02 was saved and received the ID: 334\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_04 was saved and received the ID: 335\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_06 was saved and received the ID: 336\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_08 was saved and received the ID: 337\n",
"The job murn_job_Li3Al2_Li3Al2_trigonal_1_1 was saved and received the ID: 338\n",
"The job Li4Al4_Li4Al4_cubic_relax was saved and received the ID: 339\n",
"The job murn_job_Li4Al4_Li4Al4_cubic was saved and received the ID: 340\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_9 was saved and received the ID: 341\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_92 was saved and received the ID: 342\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_94 was saved and received the ID: 343\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_96 was saved and received the ID: 344\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_98 was saved and received the ID: 345\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_0 was saved and received the ID: 346\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_02 was saved and received the ID: 347\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_04 was saved and received the ID: 348\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_06 was saved and received the ID: 349\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_08 was saved and received the ID: 350\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_1 was saved and received the ID: 351\n"
]
}
],
"source": [
"for pot in potentials_list:\n",
" with pr.open(get_clean_project_name(pot)) as pr_pot:\n",
" for compound, compound_dict in struct_dict.items():\n",
" for crys_structure in compound_dict[\"s_murn\"]:\n",
" \n",
" # Relax structure\n",
" if crys_structure in [\"fcc\",\"bcc\"]:\n",
" basis = pr_pot.create_ase_bulk(compound, crys_structure, a=compound_dict[\"a\"])\n",
" else:\n",
" basis = pr.create.structure.ase.read(fl_dict[crys_structure])\n",
" job_relax = pr_pot.create_job(pr_pot.job_type.Lammps, f\"{compound}_{crys_structure}_relax\", delete_existing_job=True)\n",
"\n",
" job_relax.structure = basis\n",
" job_relax.potential = pot\n",
" job_relax.calc_minimize(pressure=0)\n",
" job_relax.run()\n",
" \n",
" # Murnaghan\n",
" job_ref = pr_pot.create_job(pr_pot.job_type.Lammps, f\"ref_job_{compound}_{crys_structure}\")\n",
" job_ref.structure = job_relax.get_structure(-1)\n",
" job_ref.potential = pot\n",
" job_ref.calc_minimize()\n",
" \n",
" murn_job = job_ref.create_job(pr_pot.job_type.Murnaghan, f\"murn_job_{compound}_{crys_structure}\")\n",
" murn_job.input[\"vol_range\"] = 0.1\n",
" murn_job.run()"
]
},
{
"cell_type": "markdown",
"id": "9d848f1a",
"metadata": {},
"source": [
"One can display the technical details of all submitted jobs using `pr.job_table()` below."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "fdc89ebb-3c2a-4315-8fe0-3ae470375223",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# pr.job_table()"
]
},
{
"cell_type": "markdown",
"id": "425dcaec",
"metadata": {},
"source": [
"In order to get read useful results from the completed calculations (eq_energy, eq_volume, etc), it is useful to define the following functions"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "ef2f414b-64b8-49aa-87e9-e204950da938",
"metadata": {},
"outputs": [],
"source": [
"# Only work with Murnaghan jobs\n",
"def get_only_murn(job_table):\n",
" return (job_table.hamilton == \"Murnaghan\") & (job_table.status == \"finished\") \n",
"\n",
"def get_eq_vol(job_path):\n",
" return job_path[\"output/equilibrium_volume\"]\n",
"\n",
"def get_eq_lp(job_path):\n",
" return np.linalg.norm(job_path[\"output/structure/cell/cell\"][0]) * np.sqrt(2)\n",
"\n",
"def get_eq_bm(job_path):\n",
" return job_path[\"output/equilibrium_bulk_modulus\"]\n",
"\n",
"def get_potential(job_path):\n",
" return job_path.project.path.split(\"/\")[-3]\n",
"\n",
"def get_eq_energy(job_path):\n",
" return job_path[\"output/equilibrium_energy\"]\n",
"\n",
"def get_n_atoms(job_path):\n",
" return len(job_path[\"output/structure/positions\"])\n",
"\n",
"def get_ase_atoms(job_path):\n",
" return pyiron_to_ase(job_path.structure).copy()\n",
"\n",
"\n",
"def get_potential(job_path):\n",
" return job_path.project.path.split(\"/\")[-2]\n",
"\n",
"def get_crystal_structure(job_path):\n",
" return job_path.job_name.split(\"_\")[-1]\n",
"\n",
"def get_compound(job_path):\n",
" return job_path.job_name.split(\"_\")[-2]"
]
},
{
"cell_type": "markdown",
"id": "2fe57b8b",
"metadata": {},
"source": [
"Using the functions defined above, one can now define a `pd.DataFrame` containing all useful results"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "255c28af-e4af-48c6-ae01-e90377c94e32",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job table_murn was saved and received the ID: 352\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5772b80f69474d2e843f6d1bcde29bdb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading and filtering jobs: 0%| | 0/27 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "03d5f86fe221412a929ee06cbb152783",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Processing jobs: 0%| | 0/27 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/srv/conda/envs/notebook/lib/python3.8/site-packages/pyiron_base/table/datamining.py:620: PerformanceWarning: \n",
"your performance may suffer as PyTables will pickle object types that it cannot\n",
"map directly to c-types [inferred_type->mixed,key->block2_values] [items->Index(['potential', 'ase_atoms', 'compound', 'crystal_structure'], dtype='object')]\n",
"\n",
" self.pyiron_table._df.to_hdf(\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
" <th>potential</th>\n",
" <th>ase_atoms</th>\n",
" <th>compound</th>\n",
" <th>crystal_structure</th>\n",
" <th>a</th>\n",
" <th>eq_vol</th>\n",
" <th>eq_bm</th>\n",
" <th>eq_energy</th>\n",
" <th>n_atoms</th>\n",
" <th>phase</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.039967</td>\n",
" <td>16.495612</td>\n",
" <td>85.876912</td>\n",
" <td>-3.483097</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.898853</td>\n",
" <td>16.147864</td>\n",
" <td>48.620841</td>\n",
" <td>-3.415312</td>\n",
" <td>1</td>\n",
" <td>Al_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.195477</td>\n",
" <td>20.114514</td>\n",
" <td>13.690609</td>\n",
" <td>-1.757011</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>41</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>4.253841</td>\n",
" <td>19.241330</td>\n",
" <td>13.985972</td>\n",
" <td>-1.758107</td>\n",
" <td>1</td>\n",
" <td>Li_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>54</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.165940</td>\n",
" <td>58.604895</td>\n",
" <td>100.347240</td>\n",
" <td>-11.074362</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>67</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.607502</td>\n",
" <td>62.227580</td>\n",
" <td>51.472656</td>\n",
" <td>-12.774590</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>80</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.9874611628416465, 1.0099045365192156, 0.8188840806477526], index=0), Atom('Li', [3.1237816780987666, 1.455730745331952, 2.673723152073369], index=1), Atom('Li', [-3.4421956688209843...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.023701</td>\n",
" <td>190.504374</td>\n",
" <td>53.125276</td>\n",
" <td>-28.970054</td>\n",
" <td>13</td>\n",
" <td>Li9Al4_monoclinic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>93</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [2.1548001975659234, 1.244075358781918, 1.861784175000869], index=0), Atom('Al', [-2.154798282819334, 3.732223313213554, 2.6646760238080542], index=1), Atom('Li', [8.560563403365654e-0...</td>\n",
" <td>Li3Al2</td>\n",
" <td>trigonal</td>\n",
" <td>6.094693</td>\n",
" <td>72.810229</td>\n",
" <td>69.231669</td>\n",
" <td>-12.413856</td>\n",
" <td>5</td>\n",
" <td>Li3Al2_trigonal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>106</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [2.142967147985671, 1.2372426587287435, 7.662120717536293], index=0), Atom('Li', [-8.783761113500244e-10, 2.4744853189563414, 0.5913679335098909], index=1), Atom('Li', [-8.783761113500...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.061226</td>\n",
" <td>131.389799</td>\n",
" <td>71.221355</td>\n",
" <td>-20.506570</td>\n",
" <td>8</td>\n",
" <td>Li4Al4_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>119</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.025259</td>\n",
" <td>16.355737</td>\n",
" <td>76.669339</td>\n",
" <td>-3.484016</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>132</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.958447</td>\n",
" <td>16.870137</td>\n",
" <td>51.052272</td>\n",
" <td>-3.432183</td>\n",
" <td>1</td>\n",
" <td>Al_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>145</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.211118</td>\n",
" <td>20.286595</td>\n",
" <td>8.517306</td>\n",
" <td>-1.755918</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>158</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>3.967043</td>\n",
" <td>15.678901</td>\n",
" <td>147.215464</td>\n",
" <td>-1.769260</td>\n",
" <td>1</td>\n",
" <td>Li_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>171</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.376805</td>\n",
" <td>64.816143</td>\n",
" <td>57.934650</td>\n",
" <td>-11.212634</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>184</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.700455</td>\n",
" <td>65.403086</td>\n",
" <td>59.308440</td>\n",
" <td>-12.574696</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>197</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [5.206051477294367, 1.0619663179427192, 0.8311820920214751], index=0), Atom('Li', [3.28638171437237, 1.5211864250363467, 2.7226207058417775], index=1), Atom('Li', [-3.6198784902055765,...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.640614</td>\n",
" <td>218.932018</td>\n",
" <td>33.874957</td>\n",
" <td>-31.820765</td>\n",
" <td>13</td>\n",
" <td>Li9Al4_monoclinic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>210</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Al', [2.2338755345732753, 1.289729472183878, 1.9126243306628208], index=0), Atom('Al', [-2.233873547699001, 3.869185551846968, 2.7799443936883206], index=1), Atom('Li', [9.007133262260959e-...</td>\n",
" <td>Li3Al2</td>\n",
" <td>trigonal</td>\n",
" <td>6.318351</td>\n",
" <td>81.143544</td>\n",
" <td>44.574696</td>\n",
" <td>-13.185198</td>\n",
" <td>5</td>\n",
" <td>Li3Al2_trigonal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>223</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [2.220260976080854, 1.2818682724036983, 7.872085429446316], index=0), Atom('Li', [1.722758777253687e-10, 2.5637365444716322, 0.6790950189344616], index=1), Atom('Li', [1.72275877725368...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.279846</td>\n",
" <td>146.014891</td>\n",
" <td>37.664442</td>\n",
" <td>-21.680919</td>\n",
" <td>8</td>\n",
" <td>Li4Al4_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>236</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.044553</td>\n",
" <td>16.541594</td>\n",
" <td>87.130427</td>\n",
" <td>-3.478909</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>249</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.953036</td>\n",
" <td>16.811334</td>\n",
" <td>72.667242</td>\n",
" <td>-3.388831</td>\n",
" <td>1</td>\n",
" <td>Al_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>262</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.216389</td>\n",
" <td>20.403222</td>\n",
" <td>15.823747</td>\n",
" <td>-1.756104</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>275</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>4.331457</td>\n",
" <td>20.318983</td>\n",
" <td>14.231625</td>\n",
" <td>-1.755594</td>\n",
" <td>1</td>\n",
" <td>Li_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>288</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.367064</td>\n",
" <td>64.521799</td>\n",
" <td>46.107162</td>\n",
" <td>-11.185880</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>301</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.686989</td>\n",
" <td>65.028366</td>\n",
" <td>66.254925</td>\n",
" <td>-12.569153</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>314</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [5.141009159558869, 1.0571139195527752, 0.820249453790277], index=0), Atom('Li', [3.2705789348169056, 1.5045550288016276, 2.715159327393234], index=1), Atom('Li', [-3.601125467999465, ...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.519944</td>\n",
" <td>213.136118</td>\n",
" <td>33.963240</td>\n",
" <td>-31.796316</td>\n",
" <td>13</td>\n",
" <td>Li9Al4_monoclinic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>327</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Al', [2.2270976540671734, 1.2858164055924044, 1.9025646270076813], index=0), Atom('Al', [-2.227095628822777, 3.8574462424884515, 2.7757665665986657], index=1), Atom('Li', [8.407589514518869...</td>\n",
" <td>Li3Al2</td>\n",
" <td>trigonal</td>\n",
" <td>6.299181</td>\n",
" <td>80.375104</td>\n",
" <td>39.643133</td>\n",
" <td>-13.138303</td>\n",
" <td>5</td>\n",
" <td>Li3Al2_trigonal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>340</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [2.2269869888586107, 1.285751535686306, 7.864026721150146], index=0), Atom('Li', [-1.5554058443124377e-09, 2.571503074062492, 0.7130584901440213], index=1), Atom('Li', [-1.555405844312...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.298870</td>\n",
" <td>147.356944</td>\n",
" <td>46.701117</td>\n",
" <td>-21.607231</td>\n",
" <td>8</td>\n",
" <td>Li4Al4_cubic</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential \\\n",
"0 2 LiAl_eam \n",
"1 15 LiAl_eam \n",
"2 28 LiAl_eam \n",
"3 41 LiAl_eam \n",
"4 54 LiAl_eam \n",
"5 67 LiAl_eam \n",
"6 80 LiAl_eam \n",
"7 93 LiAl_eam \n",
"8 106 LiAl_eam \n",
"9 119 RuNNer-AlLi \n",
"10 132 RuNNer-AlLi \n",
"11 145 RuNNer-AlLi \n",
"12 158 RuNNer-AlLi \n",
"13 171 RuNNer-AlLi \n",
"14 184 RuNNer-AlLi \n",
"15 197 RuNNer-AlLi \n",
"16 210 RuNNer-AlLi \n",
"17 223 RuNNer-AlLi \n",
"18 236 LiAl_yace \n",
"19 249 LiAl_yace \n",
"20 262 LiAl_yace \n",
"21 275 LiAl_yace \n",
"22 288 LiAl_yace \n",
"23 301 LiAl_yace \n",
"24 314 LiAl_yace \n",
"25 327 LiAl_yace \n",
"26 340 LiAl_yace \n",
"\n",
" ase_atoms \\\n",
"0 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"1 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"2 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"3 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"4 (Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12... \n",
"5 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760... \n",
"6 (Atom('Li', [4.9874611628416465, 1.0099045365192156, 0.8188840806477526], index=0), Atom('Li', [3.1237816780987666, 1.455730745331952, 2.673723152073369], index=1), Atom('Li', [-3.4421956688209843... \n",
"7 (Atom('Al', [2.1548001975659234, 1.244075358781918, 1.861784175000869], index=0), Atom('Al', [-2.154798282819334, 3.732223313213554, 2.6646760238080542], index=1), Atom('Li', [8.560563403365654e-0... \n",
"8 (Atom('Li', [2.142967147985671, 1.2372426587287435, 7.662120717536293], index=0), Atom('Li', [-8.783761113500244e-10, 2.4744853189563414, 0.5913679335098909], index=1), Atom('Li', [-8.783761113500... \n",
"9 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"10 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"11 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"12 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"13 (Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12... \n",
"14 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998... \n",
"15 (Atom('Li', [5.206051477294367, 1.0619663179427192, 0.8311820920214751], index=0), Atom('Li', [3.28638171437237, 1.5211864250363467, 2.7226207058417775], index=1), Atom('Li', [-3.6198784902055765,... \n",
"16 (Atom('Al', [2.2338755345732753, 1.289729472183878, 1.9126243306628208], index=0), Atom('Al', [-2.233873547699001, 3.869185551846968, 2.7799443936883206], index=1), Atom('Li', [9.007133262260959e-... \n",
"17 (Atom('Li', [2.220260976080854, 1.2818682724036983, 7.872085429446316], index=0), Atom('Li', [1.722758777253687e-10, 2.5637365444716322, 0.6790950189344616], index=1), Atom('Li', [1.72275877725368... \n",
"18 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"19 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"20 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"21 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"22 (Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e... \n",
"23 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993... \n",
"24 (Atom('Li', [5.141009159558869, 1.0571139195527752, 0.820249453790277], index=0), Atom('Li', [3.2705789348169056, 1.5045550288016276, 2.715159327393234], index=1), Atom('Li', [-3.601125467999465, ... \n",
"25 (Atom('Al', [2.2270976540671734, 1.2858164055924044, 1.9025646270076813], index=0), Atom('Al', [-2.227095628822777, 3.8574462424884515, 2.7757665665986657], index=1), Atom('Li', [8.407589514518869... \n",
"26 (Atom('Li', [2.2269869888586107, 1.285751535686306, 7.864026721150146], index=0), Atom('Li', [-1.5554058443124377e-09, 2.571503074062492, 0.7130584901440213], index=1), Atom('Li', [-1.555405844312... \n",
"\n",
" compound crystal_structure a eq_vol eq_bm eq_energy \\\n",
"0 Al fcc 4.039967 16.495612 85.876912 -3.483097 \n",
"1 Al bcc 3.898853 16.147864 48.620841 -3.415312 \n",
"2 Li bcc 4.195477 20.114514 13.690609 -1.757011 \n",
"3 Li fcc 4.253841 19.241330 13.985972 -1.758107 \n",
"4 Li2Al2 cubic 6.165940 58.604895 100.347240 -11.074362 \n",
"5 LiAl3 cubic 5.607502 62.227580 51.472656 -12.774590 \n",
"6 Li9Al4 monoclinic 13.023701 190.504374 53.125276 -28.970054 \n",
"7 Li3Al2 trigonal 6.094693 72.810229 69.231669 -12.413856 \n",
"8 Li4Al4 cubic 6.061226 131.389799 71.221355 -20.506570 \n",
"9 Al fcc 4.025259 16.355737 76.669339 -3.484016 \n",
"10 Al bcc 3.958447 16.870137 51.052272 -3.432183 \n",
"11 Li bcc 4.211118 20.286595 8.517306 -1.755918 \n",
"12 Li fcc 3.967043 15.678901 147.215464 -1.769260 \n",
"13 Li2Al2 cubic 6.376805 64.816143 57.934650 -11.212634 \n",
"14 LiAl3 cubic 5.700455 65.403086 59.308440 -12.574696 \n",
"15 Li9Al4 monoclinic 13.640614 218.932018 33.874957 -31.820765 \n",
"16 Li3Al2 trigonal 6.318351 81.143544 44.574696 -13.185198 \n",
"17 Li4Al4 cubic 6.279846 146.014891 37.664442 -21.680919 \n",
"18 Al fcc 4.044553 16.541594 87.130427 -3.478909 \n",
"19 Al bcc 3.953036 16.811334 72.667242 -3.388831 \n",
"20 Li bcc 4.216389 20.403222 15.823747 -1.756104 \n",
"21 Li fcc 4.331457 20.318983 14.231625 -1.755594 \n",
"22 Li2Al2 cubic 6.367064 64.521799 46.107162 -11.185880 \n",
"23 LiAl3 cubic 5.686989 65.028366 66.254925 -12.569153 \n",
"24 Li9Al4 monoclinic 13.519944 213.136118 33.963240 -31.796316 \n",
"25 Li3Al2 trigonal 6.299181 80.375104 39.643133 -13.138303 \n",
"26 Li4Al4 cubic 6.298870 147.356944 46.701117 -21.607231 \n",
"\n",
" n_atoms phase \n",
"0 1 Al_fcc \n",
"1 1 Al_bcc \n",
"2 1 Li_bcc \n",
"3 1 Li_fcc \n",
"4 4 Li2Al2_cubic \n",
"5 4 LiAl3_cubic \n",
"6 13 Li9Al4_monoclinic \n",
"7 5 Li3Al2_trigonal \n",
"8 8 Li4Al4_cubic \n",
"9 1 Al_fcc \n",
"10 1 Al_bcc \n",
"11 1 Li_bcc \n",
"12 1 Li_fcc \n",
"13 4 Li2Al2_cubic \n",
"14 4 LiAl3_cubic \n",
"15 13 Li9Al4_monoclinic \n",
"16 5 Li3Al2_trigonal \n",
"17 8 Li4Al4_cubic \n",
"18 1 Al_fcc \n",
"19 1 Al_bcc \n",
"20 1 Li_bcc \n",
"21 1 Li_fcc \n",
"22 4 Li2Al2_cubic \n",
"23 4 LiAl3_cubic \n",
"24 13 Li9Al4_monoclinic \n",
"25 5 Li3Al2_trigonal \n",
"26 8 Li4Al4_cubic "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Compile data using pyiron tables\n",
"table = pr.create_table(\"table_murn\", delete_existing_job=True)\n",
"table.convert_to_object = True\n",
"table.db_filter_function = get_only_murn\n",
"table.add[\"potential\"] = get_potential\n",
"table.add[\"ase_atoms\"] = get_ase_atoms\n",
"table.add[\"compound\"] = get_compound\n",
"table.add[\"crystal_structure\"] = get_crystal_structure\n",
"table.add[\"a\"] = get_eq_lp\n",
"table.add[\"eq_vol\"] = get_eq_vol\n",
"table.add[\"eq_bm\"] = get_eq_bm\n",
"table.add[\"eq_energy\"] = get_eq_energy\n",
"table.add[\"n_atoms\"] = get_n_atoms\n",
"table.run()\n",
"\n",
"data_murn = table.get_dataframe()\n",
"data_murn[\"phase\"] = data_murn.compound + \"_\" + data_murn.crystal_structure\n",
"data_murn"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "30d27d75",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1728x1440 with 9 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax_list = plt.subplots(ncols=3, nrows=len(potentials_list), sharex=\"col\")\n",
"\n",
"fig.set_figwidth(24)\n",
"fig.set_figheight(20)\n",
"\n",
"color_palette = sns.color_palette(\"tab10\", n_colors=len(data_murn.phase.unique()))\n",
"\n",
"\n",
"for i, pot in enumerate(potentials_list):\n",
" \n",
" # if len(potentials_list) == 1:\n",
" # ax = ax_list\n",
" # else:\n",
" # ax = ax_list.flatten()[i]\n",
" \n",
"\n",
" \n",
" mask1 = data_murn[\"compound\"]==\"Al\"\n",
" data1 = data_murn[(data_murn.potential == get_clean_project_name(pot)) & (mask1)]\n",
" \n",
" mask2 = data_murn[\"compound\"]==\"Li\"\n",
" data2 = data_murn[(data_murn.potential == get_clean_project_name(pot)) & (mask2)]\n",
" \n",
" mask3 = data_murn[\"compound\"].isin([\"Al\",\"Li\"])\n",
" data3 = data_murn[(data_murn.potential == get_clean_project_name(pot)) & (~mask3)]\n",
"\n",
" for j,(_, row) in enumerate(data1.iterrows()):\n",
" murn_job = pr.load(row[\"job_id\"])\n",
" murn_df = murn_job.output_to_pandas()\n",
" n_atoms = row[\"n_atoms\"]\n",
"\n",
" ax_list[i,0].plot(murn_df[\"volume\"]/n_atoms, murn_df[\"energy\"]/n_atoms,\"o-\",\n",
" lw=4,\n",
" label= row[\"phase\"], \n",
" color=color_palette[j])\n",
" \n",
" ax_list[i,0].set_title(f\"{get_clean_project_name(pot)}\" + '_' + data1.iloc[0][\"compound\"],fontsize=22)\n",
" # ax_list[i,0].legend(prop={\"size\":16})\n",
" \n",
" for j,(_, row) in enumerate(data2.iterrows()):\n",
" murn_job = pr.load(row[\"job_id\"])\n",
" murn_df = murn_job.output_to_pandas()\n",
" n_atoms = row[\"n_atoms\"]\n",
" \n",
" ax_list[i,2].plot(murn_df[\"volume\"]/n_atoms, murn_df[\"energy\"]/n_atoms,\"o-\",\n",
" lw=4,\n",
" label= row[\"phase\"], \n",
" color=color_palette[j])\n",
" \n",
" ax_list[i,2].set_title(f\"{get_clean_project_name(pot)}\" + '_' + data2.iloc[0][\"compound\"],fontsize=22)\n",
" # ax_list[i,2].legend(prop={\"size\":16})\n",
" \n",
" for j,(_, row) in enumerate(data3.iterrows()):\n",
" murn_job = pr.load(row[\"job_id\"])\n",
" murn_df = murn_job.output_to_pandas()\n",
" n_atoms = row[\"n_atoms\"]\n",
" \n",
" ax_list[i,1].plot(murn_df[\"volume\"]/n_atoms, murn_df[\"energy\"]/n_atoms,\"o-\",\n",
" lw=4,\n",
" label= row[\"phase\"], \n",
" color=color_palette[j])\n",
" \n",
" ax_list[i,1].set_title(f\"{get_clean_project_name(pot)}\" + '_AlLi_mixed',fontsize=22)\n",
" # ax_list[i,1].legend(prop={\"size\":16})\n",
" \n",
" \n",
"for i in range(3):\n",
" ax_list[0,i].legend(prop={\"size\":16})\n",
" ax_list[-1,i].set_xlabel(\"Volume per atom, $\\mathrm{\\AA^3}$\",fontsize=20)\n",
" ax_list[-1,i].tick_params(axis=\"x\",labelsize=18)\n",
" \n",
"for i in range(len(potentials_list)):\n",
" ax_list[i,0].set_ylabel(\"Energy per atom, eV/atom\",fontsize=18)\n",
" \n",
" \n",
" \n",
"# ax.legend(prop={\"size\":16})\n",
"# ax.set_ylabel(\"Energy per atom, eV/atom\",fontsize=20)\n",
"#break\n",
"fig.subplots_adjust(wspace=0.1);"
]
},
{
"cell_type": "markdown",
"id": "fba90359-a2a5-4f83-9fa8-6dc4d87f5743",
"metadata": {},
"source": [
"## (b) Elastic constants and Phonons\n",
"\n",
"Pyiron also has job modules to calculate elastic constants and thermal properties using the quasi-harmonic approximation given by the `phonopy` package.\n",
"\n",
"As in the previous task, we again loop over the defined potentials and then over the given structures.\n",
"\n",
"Calculating elastic constants and thermal properties is considerably more expensive than calculating EV curves. Hence, it is useful to only calculate these properties for a subset of most important structures "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "7bf87f90",
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
" <th>potential</th>\n",
" <th>ase_atoms</th>\n",
" <th>compound</th>\n",
" <th>crystal_structure</th>\n",
" <th>a</th>\n",
" <th>eq_vol</th>\n",
" <th>eq_bm</th>\n",
" <th>eq_energy</th>\n",
" <th>n_atoms</th>\n",
" <th>phase</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.039967</td>\n",
" <td>16.495612</td>\n",
" <td>85.876912</td>\n",
" <td>-3.483097</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.195477</td>\n",
" <td>20.114514</td>\n",
" <td>13.690609</td>\n",
" <td>-1.757011</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>54</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.165940</td>\n",
" <td>58.604895</td>\n",
" <td>100.347240</td>\n",
" <td>-11.074362</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>67</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.607502</td>\n",
" <td>62.227580</td>\n",
" <td>51.472656</td>\n",
" <td>-12.774590</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>119</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.025259</td>\n",
" <td>16.355737</td>\n",
" <td>76.669339</td>\n",
" <td>-3.484016</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>145</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.211118</td>\n",
" <td>20.286595</td>\n",
" <td>8.517306</td>\n",
" <td>-1.755918</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>171</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.376805</td>\n",
" <td>64.816143</td>\n",
" <td>57.934650</td>\n",
" <td>-11.212634</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>184</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.700455</td>\n",
" <td>65.403086</td>\n",
" <td>59.308440</td>\n",
" <td>-12.574696</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>236</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.044553</td>\n",
" <td>16.541594</td>\n",
" <td>87.130427</td>\n",
" <td>-3.478909</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>262</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.216389</td>\n",
" <td>20.403222</td>\n",
" <td>15.823747</td>\n",
" <td>-1.756104</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>288</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.367064</td>\n",
" <td>64.521799</td>\n",
" <td>46.107162</td>\n",
" <td>-11.185880</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>301</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.686989</td>\n",
" <td>65.028366</td>\n",
" <td>66.254925</td>\n",
" <td>-12.569153</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential \\\n",
"0 2 LiAl_eam \n",
"2 28 LiAl_eam \n",
"4 54 LiAl_eam \n",
"5 67 LiAl_eam \n",
"9 119 RuNNer-AlLi \n",
"11 145 RuNNer-AlLi \n",
"13 171 RuNNer-AlLi \n",
"14 184 RuNNer-AlLi \n",
"18 236 LiAl_yace \n",
"20 262 LiAl_yace \n",
"22 288 LiAl_yace \n",
"23 301 LiAl_yace \n",
"\n",
" ase_atoms \\\n",
"0 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"2 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"4 (Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12... \n",
"5 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760... \n",
"9 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"11 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"13 (Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12... \n",
"14 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998... \n",
"18 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"20 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"22 (Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e... \n",
"23 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993... \n",
"\n",
" compound crystal_structure a eq_vol eq_bm eq_energy \\\n",
"0 Al fcc 4.039967 16.495612 85.876912 -3.483097 \n",
"2 Li bcc 4.195477 20.114514 13.690609 -1.757011 \n",
"4 Li2Al2 cubic 6.165940 58.604895 100.347240 -11.074362 \n",
"5 LiAl3 cubic 5.607502 62.227580 51.472656 -12.774590 \n",
"9 Al fcc 4.025259 16.355737 76.669339 -3.484016 \n",
"11 Li bcc 4.211118 20.286595 8.517306 -1.755918 \n",
"13 Li2Al2 cubic 6.376805 64.816143 57.934650 -11.212634 \n",
"14 LiAl3 cubic 5.700455 65.403086 59.308440 -12.574696 \n",
"18 Al fcc 4.044553 16.541594 87.130427 -3.478909 \n",
"20 Li bcc 4.216389 20.403222 15.823747 -1.756104 \n",
"22 Li2Al2 cubic 6.367064 64.521799 46.107162 -11.185880 \n",
"23 LiAl3 cubic 5.686989 65.028366 66.254925 -12.569153 \n",
"\n",
" n_atoms phase \n",
"0 1 Al_fcc \n",
"2 1 Li_bcc \n",
"4 4 Li2Al2_cubic \n",
"5 4 LiAl3_cubic \n",
"9 1 Al_fcc \n",
"11 1 Li_bcc \n",
"13 4 Li2Al2_cubic \n",
"14 4 LiAl3_cubic \n",
"18 1 Al_fcc \n",
"20 1 Li_bcc \n",
"22 4 Li2Al2_cubic \n",
"23 4 LiAl3_cubic "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_of_phases = [\"Al_fcc\",\"Li_bcc\",\"Li2Al2_cubic\",\"LiAl3_cubic\"]\n",
"\n",
"subset_murn = data_murn[data_murn[\"phase\"].isin(list_of_phases)]\n",
"subset_murn"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0d1c799c-f10b-462d-aaea-253cee4b4b3e",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LiAl_eam\n",
"The job elastic_job_Al_fcc was saved and received the ID: 353\n",
"The job s_e_0 was saved and received the ID: 354\n",
"The job s_01_e_m0_05000 was saved and received the ID: 355\n",
"The job s_01_e_m0_02500 was saved and received the ID: 356\n",
"The job s_01_e_0_02500 was saved and received the ID: 357\n",
"The job s_01_e_0_05000 was saved and received the ID: 358\n",
"The job s_08_e_m0_05000 was saved and received the ID: 359\n",
"The job s_08_e_m0_02500 was saved and received the ID: 360\n",
"The job s_08_e_0_02500 was saved and received the ID: 361\n",
"The job s_08_e_0_05000 was saved and received the ID: 362\n",
"The job s_23_e_m0_05000 was saved and received the ID: 363\n",
"The job s_23_e_m0_02500 was saved and received the ID: 364\n",
"The job s_23_e_0_02500 was saved and received the ID: 365\n",
"The job s_23_e_0_05000 was saved and received the ID: 366\n",
"The job phonopy_job_Al_fcc was saved and received the ID: 367\n",
"The job ref_job_Al_fcc_0 was saved and received the ID: 368\n",
"The job elastic_job_Li_bcc was saved and received the ID: 369\n",
"The job s_e_0 was saved and received the ID: 370\n",
"The job s_01_e_m0_05000 was saved and received the ID: 371\n",
"The job s_01_e_m0_02500 was saved and received the ID: 372\n",
"The job s_01_e_0_02500 was saved and received the ID: 373\n",
"The job s_01_e_0_05000 was saved and received the ID: 374\n",
"The job s_08_e_m0_05000 was saved and received the ID: 375\n",
"The job s_08_e_m0_02500 was saved and received the ID: 376\n",
"The job s_08_e_0_02500 was saved and received the ID: 377\n",
"The job s_08_e_0_05000 was saved and received the ID: 378\n",
"The job s_23_e_m0_05000 was saved and received the ID: 379\n",
"The job s_23_e_m0_02500 was saved and received the ID: 380\n",
"The job s_23_e_0_02500 was saved and received the ID: 381\n",
"The job s_23_e_0_05000 was saved and received the ID: 382\n",
"The job phonopy_job_Li_bcc was saved and received the ID: 383\n",
"The job ref_job_Li_bcc_0 was saved and received the ID: 384\n",
"The job elastic_job_Li2Al2_cubic was saved and received the ID: 385\n",
"The job s_e_0 was saved and received the ID: 386\n",
"The job s_01_e_m0_05000 was saved and received the ID: 387\n",
"The job s_01_e_m0_02500 was saved and received the ID: 388\n",
"The job s_01_e_0_02500 was saved and received the ID: 389\n",
"The job s_01_e_0_05000 was saved and received the ID: 390\n",
"The job s_08_e_m0_05000 was saved and received the ID: 391\n",
"The job s_08_e_m0_02500 was saved and received the ID: 392\n",
"The job s_08_e_0_02500 was saved and received the ID: 393\n",
"The job s_08_e_0_05000 was saved and received the ID: 394\n",
"The job s_23_e_m0_05000 was saved and received the ID: 395\n",
"The job s_23_e_m0_02500 was saved and received the ID: 396\n",
"The job s_23_e_0_02500 was saved and received the ID: 397\n",
"The job s_23_e_0_05000 was saved and received the ID: 398\n",
"The job phonopy_job_Li2Al2_cubic was saved and received the ID: 399\n",
"The job ref_job_Li2Al2_cubic_0 was saved and received the ID: 400\n",
"The job ref_job_Li2Al2_cubic_1 was saved and received the ID: 401\n",
"The job elastic_job_LiAl3_cubic was saved and received the ID: 402\n",
"The job s_e_0 was saved and received the ID: 403\n",
"The job s_01_e_m0_05000 was saved and received the ID: 404\n",
"The job s_01_e_m0_02500 was saved and received the ID: 405\n",
"The job s_01_e_0_02500 was saved and received the ID: 406\n",
"The job s_01_e_0_05000 was saved and received the ID: 407\n",
"The job s_08_e_m0_05000 was saved and received the ID: 408\n",
"The job s_08_e_m0_02500 was saved and received the ID: 409\n",
"The job s_08_e_0_02500 was saved and received the ID: 410\n",
"The job s_08_e_0_05000 was saved and received the ID: 411\n",
"The job s_23_e_m0_05000 was saved and received the ID: 412\n",
"The job s_23_e_m0_02500 was saved and received the ID: 413\n",
"The job s_23_e_0_02500 was saved and received the ID: 414\n",
"The job s_23_e_0_05000 was saved and received the ID: 415\n",
"The job phonopy_job_LiAl3_cubic was saved and received the ID: 416\n",
"The job ref_job_LiAl3_cubic_0 was saved and received the ID: 417\n",
"The job ref_job_LiAl3_cubic_1 was saved and received the ID: 418\n",
"RuNNer-AlLi\n",
"The job elastic_job_Al_fcc was saved and received the ID: 419\n",
"The job s_e_0 was saved and received the ID: 420\n",
"The job s_01_e_m0_05000 was saved and received the ID: 421\n",
"The job s_01_e_m0_02500 was saved and received the ID: 422\n",
"The job s_01_e_0_02500 was saved and received the ID: 423\n",
"The job s_01_e_0_05000 was saved and received the ID: 424\n",
"The job s_08_e_m0_05000 was saved and received the ID: 425\n",
"The job s_08_e_m0_02500 was saved and received the ID: 426\n",
"The job s_08_e_0_02500 was saved and received the ID: 427\n",
"The job s_08_e_0_05000 was saved and received the ID: 428\n",
"The job s_23_e_m0_05000 was saved and received the ID: 429\n",
"The job s_23_e_m0_02500 was saved and received the ID: 430\n",
"The job s_23_e_0_02500 was saved and received the ID: 431\n",
"The job s_23_e_0_05000 was saved and received the ID: 432\n",
"The job phonopy_job_Al_fcc was saved and received the ID: 433\n",
"The job ref_job_Al_fcc_0 was saved and received the ID: 434\n",
"The job elastic_job_Li_bcc was saved and received the ID: 435\n",
"The job s_e_0 was saved and received the ID: 436\n",
"The job s_01_e_m0_05000 was saved and received the ID: 437\n",
"The job s_01_e_m0_02500 was saved and received the ID: 438\n",
"The job s_01_e_0_02500 was saved and received the ID: 439\n",
"The job s_01_e_0_05000 was saved and received the ID: 440\n",
"The job s_08_e_m0_05000 was saved and received the ID: 441\n",
"The job s_08_e_m0_02500 was saved and received the ID: 442\n",
"The job s_08_e_0_02500 was saved and received the ID: 443\n",
"The job s_08_e_0_05000 was saved and received the ID: 444\n",
"The job s_23_e_m0_05000 was saved and received the ID: 445\n",
"The job s_23_e_m0_02500 was saved and received the ID: 446\n",
"The job s_23_e_0_02500 was saved and received the ID: 447\n",
"The job s_23_e_0_05000 was saved and received the ID: 448\n",
"The job phonopy_job_Li_bcc was saved and received the ID: 449\n",
"The job ref_job_Li_bcc_0 was saved and received the ID: 450\n",
"The job elastic_job_Li2Al2_cubic was saved and received the ID: 451\n",
"The job s_e_0 was saved and received the ID: 452\n",
"The job s_01_e_m0_05000 was saved and received the ID: 453\n",
"The job s_01_e_m0_02500 was saved and received the ID: 454\n",
"The job s_01_e_0_02500 was saved and received the ID: 455\n",
"The job s_01_e_0_05000 was saved and received the ID: 456\n",
"The job s_08_e_m0_05000 was saved and received the ID: 457\n",
"The job s_08_e_m0_02500 was saved and received the ID: 458\n",
"The job s_08_e_0_02500 was saved and received the ID: 459\n",
"The job s_08_e_0_05000 was saved and received the ID: 460\n",
"The job s_23_e_m0_05000 was saved and received the ID: 461\n",
"The job s_23_e_m0_02500 was saved and received the ID: 462\n",
"The job s_23_e_0_02500 was saved and received the ID: 463\n",
"The job s_23_e_0_05000 was saved and received the ID: 464\n",
"The job phonopy_job_Li2Al2_cubic was saved and received the ID: 465\n",
"The job ref_job_Li2Al2_cubic_0 was saved and received the ID: 466\n",
"The job ref_job_Li2Al2_cubic_1 was saved and received the ID: 467\n",
"The job elastic_job_LiAl3_cubic was saved and received the ID: 468\n",
"The job s_e_0 was saved and received the ID: 469\n",
"The job s_01_e_m0_05000 was saved and received the ID: 470\n",
"The job s_01_e_m0_02500 was saved and received the ID: 471\n",
"The job s_01_e_0_02500 was saved and received the ID: 472\n",
"The job s_01_e_0_05000 was saved and received the ID: 473\n",
"The job s_08_e_m0_05000 was saved and received the ID: 474\n",
"The job s_08_e_m0_02500 was saved and received the ID: 475\n",
"The job s_08_e_0_02500 was saved and received the ID: 476\n",
"The job s_08_e_0_05000 was saved and received the ID: 477\n",
"The job s_23_e_m0_05000 was saved and received the ID: 478\n",
"The job s_23_e_m0_02500 was saved and received the ID: 479\n",
"The job s_23_e_0_02500 was saved and received the ID: 480\n",
"The job s_23_e_0_05000 was saved and received the ID: 481\n",
"The job phonopy_job_LiAl3_cubic was saved and received the ID: 482\n",
"The job ref_job_LiAl3_cubic_0 was saved and received the ID: 483\n",
"The job ref_job_LiAl3_cubic_1 was saved and received the ID: 484\n",
"LiAl_yace\n",
"The job elastic_job_Al_fcc was saved and received the ID: 485\n",
"The job s_e_0 was saved and received the ID: 486\n",
"The job s_01_e_m0_05000 was saved and received the ID: 487\n",
"The job s_01_e_m0_02500 was saved and received the ID: 488\n",
"The job s_01_e_0_02500 was saved and received the ID: 489\n",
"The job s_01_e_0_05000 was saved and received the ID: 490\n",
"The job s_08_e_m0_05000 was saved and received the ID: 491\n",
"The job s_08_e_m0_02500 was saved and received the ID: 492\n",
"The job s_08_e_0_02500 was saved and received the ID: 493\n",
"The job s_08_e_0_05000 was saved and received the ID: 494\n",
"The job s_23_e_m0_05000 was saved and received the ID: 495\n",
"The job s_23_e_m0_02500 was saved and received the ID: 496\n",
"The job s_23_e_0_02500 was saved and received the ID: 497\n",
"The job s_23_e_0_05000 was saved and received the ID: 498\n",
"The job phonopy_job_Al_fcc was saved and received the ID: 499\n",
"The job ref_job_Al_fcc_0 was saved and received the ID: 500\n",
"The job elastic_job_Li_bcc was saved and received the ID: 501\n",
"The job s_e_0 was saved and received the ID: 502\n",
"The job s_01_e_m0_05000 was saved and received the ID: 503\n",
"The job s_01_e_m0_02500 was saved and received the ID: 504\n",
"The job s_01_e_0_02500 was saved and received the ID: 505\n",
"The job s_01_e_0_05000 was saved and received the ID: 506\n",
"The job s_08_e_m0_05000 was saved and received the ID: 507\n",
"The job s_08_e_m0_02500 was saved and received the ID: 508\n",
"The job s_08_e_0_02500 was saved and received the ID: 509\n",
"The job s_08_e_0_05000 was saved and received the ID: 510\n",
"The job s_23_e_m0_05000 was saved and received the ID: 511\n",
"The job s_23_e_m0_02500 was saved and received the ID: 512\n",
"The job s_23_e_0_02500 was saved and received the ID: 513\n",
"The job s_23_e_0_05000 was saved and received the ID: 514\n",
"The job phonopy_job_Li_bcc was saved and received the ID: 515\n",
"The job ref_job_Li_bcc_0 was saved and received the ID: 516\n",
"The job elastic_job_Li2Al2_cubic was saved and received the ID: 517\n",
"The job s_e_0 was saved and received the ID: 518\n",
"The job s_01_e_m0_05000 was saved and received the ID: 519\n",
"The job s_01_e_m0_02500 was saved and received the ID: 520\n",
"The job s_01_e_0_02500 was saved and received the ID: 521\n",
"The job s_01_e_0_05000 was saved and received the ID: 522\n",
"The job s_08_e_m0_05000 was saved and received the ID: 523\n",
"The job s_08_e_m0_02500 was saved and received the ID: 524\n",
"The job s_08_e_0_02500 was saved and received the ID: 525\n",
"The job s_08_e_0_05000 was saved and received the ID: 526\n",
"The job s_23_e_m0_05000 was saved and received the ID: 527\n",
"The job s_23_e_m0_02500 was saved and received the ID: 528\n",
"The job s_23_e_0_02500 was saved and received the ID: 529\n",
"The job s_23_e_0_05000 was saved and received the ID: 530\n",
"The job phonopy_job_Li2Al2_cubic was saved and received the ID: 531\n",
"The job ref_job_Li2Al2_cubic_0 was saved and received the ID: 532\n",
"The job ref_job_Li2Al2_cubic_1 was saved and received the ID: 533\n",
"The job elastic_job_LiAl3_cubic was saved and received the ID: 534\n",
"The job s_e_0 was saved and received the ID: 535\n",
"The job s_01_e_m0_05000 was saved and received the ID: 536\n",
"The job s_01_e_m0_02500 was saved and received the ID: 537\n",
"The job s_01_e_0_02500 was saved and received the ID: 538\n",
"The job s_01_e_0_05000 was saved and received the ID: 539\n",
"The job s_08_e_m0_05000 was saved and received the ID: 540\n",
"The job s_08_e_m0_02500 was saved and received the ID: 541\n",
"The job s_08_e_0_02500 was saved and received the ID: 542\n",
"The job s_08_e_0_05000 was saved and received the ID: 543\n",
"The job s_23_e_m0_05000 was saved and received the ID: 544\n",
"The job s_23_e_m0_02500 was saved and received the ID: 545\n",
"The job s_23_e_0_02500 was saved and received the ID: 546\n",
"The job s_23_e_0_05000 was saved and received the ID: 547\n",
"The job phonopy_job_LiAl3_cubic was saved and received the ID: 548\n",
"The job ref_job_LiAl3_cubic_0 was saved and received the ID: 549\n",
"The job ref_job_LiAl3_cubic_1 was saved and received the ID: 550\n"
]
}
],
"source": [
"for pot in potentials_list:\n",
" group_name = get_clean_project_name(pot)\n",
" pr_pot = pr.create_group(group_name)\n",
" print(group_name)\n",
" \n",
" for _, row in subset_murn[subset_murn.potential==group_name].iterrows():\n",
" job_id = row[\"job_id\"]\n",
" \n",
" job_ref = pr_pot.create_job(pr_pot.job_type.Lammps, f\"ref_job_{row.compound}_{row.crystal_structure}\")\n",
" ref = pr_pot.load(job_id)\n",
" job_ref.structure = ref.structure\n",
" job_ref.potential = pot\n",
" job_ref.calc_minimize()\n",
" elastic_job = job_ref.create_job(pr_pot.job_type.ElasticMatrixJob, f\"elastic_job_{row.compound}_{row.crystal_structure}\")\n",
" elastic_job.input[\"eps_range\"] = 0.05\n",
" elastic_job.run()\n",
" \n",
" \n",
" phonopy_job = job_ref.create_job(pr_pot.job_type.PhonopyJob, f\"phonopy_job_{row.compound}_{row.crystal_structure}\")\n",
" job_ref.calc_static()\n",
" phonopy_job.run()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a035813c-039d-4981-b3ba-516b40bb3c4d",
"metadata": {},
"outputs": [],
"source": [
"def filter_elastic(job_table):\n",
" return (job_table.hamilton == \"ElasticMatrixJob\") & (job_table.status == \"finished\")\n",
"\n",
"# Get corresponding elastic constants\n",
"def get_c11(job_path):\n",
" return job_path[\"output/elasticmatrix\"][\"C\"][0, 0]\n",
"\n",
"def get_c12(job_path):\n",
" return job_path[\"output/elasticmatrix\"][\"C\"][0, 1]\n",
"\n",
"def get_c44(job_path):\n",
" return job_path[\"output/elasticmatrix\"][\"C\"][3, 3]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ba95973a-a00f-41a9-b23f-2b4bcf629aaf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job table_elastic was saved and received the ID: 551\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "74f3e0d779604e9d8050634ec2f1248d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading and filtering jobs: 0%| | 0/12 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "36cf44f032c843239126338c8bc01cfe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Processing jobs: 0%| | 0/12 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
" <th>potential</th>\n",
" <th>C11</th>\n",
" <th>C12</th>\n",
" <th>C44</th>\n",
" <th>compound</th>\n",
" <th>crystal_structure</th>\n",
" <th>phase</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>353</td>\n",
" <td>LiAl_eam</td>\n",
" <td>120.339279</td>\n",
" <td>66.483631</td>\n",
" <td>45.515458</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>Al_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>369</td>\n",
" <td>LiAl_eam</td>\n",
" <td>16.740018</td>\n",
" <td>11.018163</td>\n",
" <td>12.688217</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>Li_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>385</td>\n",
" <td>LiAl_eam</td>\n",
" <td>179.464635</td>\n",
" <td>54.231219</td>\n",
" <td>47.889040</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>Li2Al2_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>402</td>\n",
" <td>LiAl_eam</td>\n",
" <td>65.443987</td>\n",
" <td>47.601166</td>\n",
" <td>28.002138</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>LiAl3_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>419</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>119.613688</td>\n",
" <td>59.261331</td>\n",
" <td>57.671025</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>Al_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>435</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>13.974565</td>\n",
" <td>4.476591</td>\n",
" <td>13.293350</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>Li_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>451</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>124.404880</td>\n",
" <td>20.665379</td>\n",
" <td>42.673693</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>Li2Al2_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>468</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>88.575923</td>\n",
" <td>50.190830</td>\n",
" <td>48.202184</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>LiAl3_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>485</td>\n",
" <td>LiAl_yace</td>\n",
" <td>133.807535</td>\n",
" <td>62.693651</td>\n",
" <td>40.423203</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>Al_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>501</td>\n",
" <td>LiAl_yace</td>\n",
" <td>18.307762</td>\n",
" <td>13.775557</td>\n",
" <td>12.106574</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>Li_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>517</td>\n",
" <td>LiAl_yace</td>\n",
" <td>114.275413</td>\n",
" <td>13.925574</td>\n",
" <td>42.537995</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>Li2Al2_cubic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>534</td>\n",
" <td>LiAl_yace</td>\n",
" <td>112.037951</td>\n",
" <td>42.770574</td>\n",
" <td>45.206508</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>LiAl3_cubic</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential C11 C12 C44 compound \\\n",
"0 353 LiAl_eam 120.339279 66.483631 45.515458 Al \n",
"1 369 LiAl_eam 16.740018 11.018163 12.688217 Li \n",
"2 385 LiAl_eam 179.464635 54.231219 47.889040 Li2Al2 \n",
"3 402 LiAl_eam 65.443987 47.601166 28.002138 LiAl3 \n",
"4 419 RuNNer-AlLi 119.613688 59.261331 57.671025 Al \n",
"5 435 RuNNer-AlLi 13.974565 4.476591 13.293350 Li \n",
"6 451 RuNNer-AlLi 124.404880 20.665379 42.673693 Li2Al2 \n",
"7 468 RuNNer-AlLi 88.575923 50.190830 48.202184 LiAl3 \n",
"8 485 LiAl_yace 133.807535 62.693651 40.423203 Al \n",
"9 501 LiAl_yace 18.307762 13.775557 12.106574 Li \n",
"10 517 LiAl_yace 114.275413 13.925574 42.537995 Li2Al2 \n",
"11 534 LiAl_yace 112.037951 42.770574 45.206508 LiAl3 \n",
"\n",
" crystal_structure phase \n",
"0 fcc Al_fcc \n",
"1 bcc Li_bcc \n",
"2 cubic Li2Al2_cubic \n",
"3 cubic LiAl3_cubic \n",
"4 fcc Al_fcc \n",
"5 bcc Li_bcc \n",
"6 cubic Li2Al2_cubic \n",
"7 cubic LiAl3_cubic \n",
"8 fcc Al_fcc \n",
"9 bcc Li_bcc \n",
"10 cubic Li2Al2_cubic \n",
"11 cubic LiAl3_cubic "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table = pr.create_table(\"table_elastic\", delete_existing_job=True)\n",
"table.db_filter_function = filter_elastic\n",
"table.add[\"potential\"] = get_potential\n",
"table.add[\"C11\"] = get_c11\n",
"table.add[\"C12\"] = get_c12\n",
"table.add[\"C44\"] = get_c44\n",
"table.add[\"compound\"] = get_compound\n",
"table.add[\"crystal_structure\"] = get_crystal_structure\n",
"\n",
"table.run()\n",
"data_elastic = table.get_dataframe()\n",
"data_elastic[\"phase\"] = data_elastic.compound + \"_\" + data_elastic.crystal_structure\n",
"data_elastic = data_elastic[data_elastic[\"phase\"].isin(list_of_phases)]\n",
"data_elastic"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b317b1d3-549b-4e0e-84bf-3cd02a92596d",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x360 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax_list = plt.subplots(ncols=len(data_elastic.phase.unique()), nrows=1, sharex=\"row\")\n",
"\n",
"fig.set_figwidth(20)\n",
"fig.set_figheight(5)\n",
"\n",
"color_palette = sns.color_palette(\"tab10\", n_colors=len(data_elastic.potential.unique()))\n",
"\n",
"pot = \"LiAl_yace\"\n",
"\n",
"\n",
"for i, phase in enumerate(data_elastic.phase.unique()):\n",
" \n",
" ax = ax_list[i]\n",
" # data = data_elastic[(data_elastic.phase == phase) & (data_elastic[\"potential\"]==\"pot\")]\n",
" data = data_elastic[(data_elastic.phase == phase)]\n",
" \n",
" \n",
" \n",
" for j, pot in enumerate(potentials_list):\n",
" \n",
" phonopy_job = pr[get_clean_project_name(pot) + f\"/phonopy_job_{phase}\"]\n",
" \n",
" thermo = phonopy_job.get_thermal_properties(t_min=0, t_max=800)\n",
" \n",
" ax.plot(phonopy_job[\"output/dos_energies\"], phonopy_job[\"output/dos_total\"], \n",
" lw=4,\n",
" color=color_palette[j], \n",
" label=get_clean_project_name(pot))\n",
" ax.set_xlabel(\"Frequency, THz\",fontsize=22)\n",
" ax.set_title(f\"{phase}\",fontsize=22)\n",
" ax.tick_params(labelsize=16)\n",
"ax_list[0].set_ylabel(\"DOS\",fontsize=22)\n",
"\n",
"ax_list[0].legend(prop={\"size\":16})\n",
"fig.subplots_adjust(wspace=0.1);"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d2f11623-e92b-4a71-8440-d84a1d48392e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x360 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax_list = plt.subplots(ncols=len(data_elastic.phase.unique()), nrows=1, sharex=\"row\", sharey=\"row\")\n",
"\n",
"fig.set_figwidth(20)\n",
"fig.set_figheight(5)\n",
"\n",
"color_palette = sns.color_palette(\"tab10\", n_colors=len(data_elastic.potential.unique()))\n",
"\n",
"\n",
"for i, phase in enumerate(data_elastic.phase.unique()):\n",
" \n",
" ax = ax_list[i]\n",
" data = data_elastic[data_elastic.phase == phase]\n",
" \n",
" n_atom = data_murn[data_murn[\"phase\"]==phase][\"n_atoms\"].iloc[0]\n",
" \n",
" \n",
" for j, pot in enumerate(potentials_list):\n",
" \n",
" phonopy_job = pr[get_clean_project_name(pot) + f\"/phonopy_job_{phase}\"]\n",
" \n",
" thermo = phonopy_job.get_thermal_properties(t_min=0, t_max=800)\n",
"\n",
" ax.plot(thermo.temperatures, thermo.cv/n_atom,\n",
" lw=4,\n",
" label=get_clean_project_name(pot), \n",
" color=color_palette[j])\n",
" ax.set_xlabel(\"Temperatures, K\",fontsize=18)\n",
" ax.set_title(f\"{phase}\",fontsize=22)\n",
" ax.tick_params(labelsize=16)\n",
"ax_list[0].set_ylabel(\"C$_v$\",fontsize=22)\n",
"\n",
"ax_list[0].legend(prop={\"size\":16})\n",
"fig.subplots_adjust(wspace=0.1);"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "7c036a6e-0a66-4adf-8a1e-6ce94ad91ee0",
"metadata": {},
"outputs": [],
"source": [
"# phonopy_job.plot_band_structure()"
]
},
{
"cell_type": "markdown",
"id": "60b72d0f",
"metadata": {},
"source": [
"## (c) Convex hull\n",
"\n",
"To assess the stability of the binary phases, we plot a convex hull for the considered phases. \n",
"\n",
"For this task we compute the formation energies of the mixed phases relative to ground state energies of equilibrium unary phases."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "2ecb02c3",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
" <th>potential</th>\n",
" <th>ase_atoms</th>\n",
" <th>compound</th>\n",
" <th>crystal_structure</th>\n",
" <th>a</th>\n",
" <th>eq_vol</th>\n",
" <th>eq_bm</th>\n",
" <th>eq_energy</th>\n",
" <th>n_atoms</th>\n",
" <th>phase</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.039967</td>\n",
" <td>16.495612</td>\n",
" <td>85.876912</td>\n",
" <td>-3.483097</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.898853</td>\n",
" <td>16.147864</td>\n",
" <td>48.620841</td>\n",
" <td>-3.415312</td>\n",
" <td>1</td>\n",
" <td>Al_bcc</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential ase_atoms compound \\\n",
"0 2 LiAl_eam (Atom('Al', [0.0, 0.0, 0.0], index=0)) Al \n",
"1 15 LiAl_eam (Atom('Al', [0.0, 0.0, 0.0], index=0)) Al \n",
"\n",
" crystal_structure a eq_vol eq_bm eq_energy n_atoms \\\n",
"0 fcc 4.039967 16.495612 85.876912 -3.483097 1 \n",
"1 bcc 3.898853 16.147864 48.620841 -3.415312 1 \n",
"\n",
" phase \n",
"0 Al_fcc \n",
"1 Al_bcc "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from collections import Counter\n",
"\n",
"# pot = \"LiAl_yace\"\n",
"\n",
"# data_convexhull = data_murn[data_murn[\"potential\"]==pot]\n",
"data_convexhull = data_murn.copy()\n",
"data_convexhull.head(2)"
]
},
{
"cell_type": "markdown",
"id": "3e1b8dd1",
"metadata": {},
"source": [
"Using `Collections.counter` we construct a composition dictionary for all the phases and from that dictionary, we can extract the relative concentrations of Al and Li in each structure"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "b0bba971",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
" <th>potential</th>\n",
" <th>ase_atoms</th>\n",
" <th>compound</th>\n",
" <th>crystal_structure</th>\n",
" <th>a</th>\n",
" <th>eq_vol</th>\n",
" <th>eq_bm</th>\n",
" <th>eq_energy</th>\n",
" <th>n_atoms</th>\n",
" <th>phase</th>\n",
" <th>comp_dict</th>\n",
" <th>n_Al</th>\n",
" <th>n_Li</th>\n",
" <th>cAl</th>\n",
" <th>cLi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.039967</td>\n",
" <td>16.495612</td>\n",
" <td>85.876912</td>\n",
" <td>-3.483097</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.898853</td>\n",
" <td>16.147864</td>\n",
" <td>48.620841</td>\n",
" <td>-3.415312</td>\n",
" <td>1</td>\n",
" <td>Al_bcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.195477</td>\n",
" <td>20.114514</td>\n",
" <td>13.690609</td>\n",
" <td>-1.757011</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>41</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>4.253841</td>\n",
" <td>19.241330</td>\n",
" <td>13.985972</td>\n",
" <td>-1.758107</td>\n",
" <td>1</td>\n",
" <td>Li_fcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>54</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.165940</td>\n",
" <td>58.604895</td>\n",
" <td>100.347240</td>\n",
" <td>-11.074362</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" <td>{'Li': 2, 'Al': 2}</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>67</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.607502</td>\n",
" <td>62.227580</td>\n",
" <td>51.472656</td>\n",
" <td>-12.774590</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" <td>{'Li': 1, 'Al': 3}</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>75.000000</td>\n",
" <td>25.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>80</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.9874611628416465, 1.0099045365192156, 0.8188840806477526], index=0), Atom('Li', [3.1237816780987666, 1.455730745331952, 2.673723152073369], index=1), Atom('Li', [-3.4421956688209843...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.023701</td>\n",
" <td>190.504374</td>\n",
" <td>53.125276</td>\n",
" <td>-28.970054</td>\n",
" <td>13</td>\n",
" <td>Li9Al4_monoclinic</td>\n",
" <td>{'Li': 9, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>30.769231</td>\n",
" <td>69.230769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>93</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [2.1548001975659234, 1.244075358781918, 1.861784175000869], index=0), Atom('Al', [-2.154798282819334, 3.732223313213554, 2.6646760238080542], index=1), Atom('Li', [8.560563403365654e-0...</td>\n",
" <td>Li3Al2</td>\n",
" <td>trigonal</td>\n",
" <td>6.094693</td>\n",
" <td>72.810229</td>\n",
" <td>69.231669</td>\n",
" <td>-12.413856</td>\n",
" <td>5</td>\n",
" <td>Li3Al2_trigonal</td>\n",
" <td>{'Al': 2, 'Li': 3}</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>40.000000</td>\n",
" <td>60.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>106</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [2.142967147985671, 1.2372426587287435, 7.662120717536293], index=0), Atom('Li', [-8.783761113500244e-10, 2.4744853189563414, 0.5913679335098909], index=1), Atom('Li', [-8.783761113500...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.061226</td>\n",
" <td>131.389799</td>\n",
" <td>71.221355</td>\n",
" <td>-20.506570</td>\n",
" <td>8</td>\n",
" <td>Li4Al4_cubic</td>\n",
" <td>{'Li': 4, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>119</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.025259</td>\n",
" <td>16.355737</td>\n",
" <td>76.669339</td>\n",
" <td>-3.484016</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>132</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.958447</td>\n",
" <td>16.870137</td>\n",
" <td>51.052272</td>\n",
" <td>-3.432183</td>\n",
" <td>1</td>\n",
" <td>Al_bcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>145</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.211118</td>\n",
" <td>20.286595</td>\n",
" <td>8.517306</td>\n",
" <td>-1.755918</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>158</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>3.967043</td>\n",
" <td>15.678901</td>\n",
" <td>147.215464</td>\n",
" <td>-1.769260</td>\n",
" <td>1</td>\n",
" <td>Li_fcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>171</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.376805</td>\n",
" <td>64.816143</td>\n",
" <td>57.934650</td>\n",
" <td>-11.212634</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" <td>{'Li': 2, 'Al': 2}</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>184</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.700455</td>\n",
" <td>65.403086</td>\n",
" <td>59.308440</td>\n",
" <td>-12.574696</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" <td>{'Li': 1, 'Al': 3}</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>75.000000</td>\n",
" <td>25.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>197</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [5.206051477294367, 1.0619663179427192, 0.8311820920214751], index=0), Atom('Li', [3.28638171437237, 1.5211864250363467, 2.7226207058417775], index=1), Atom('Li', [-3.6198784902055765,...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.640614</td>\n",
" <td>218.932018</td>\n",
" <td>33.874957</td>\n",
" <td>-31.820765</td>\n",
" <td>13</td>\n",
" <td>Li9Al4_monoclinic</td>\n",
" <td>{'Li': 9, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>30.769231</td>\n",
" <td>69.230769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>210</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Al', [2.2338755345732753, 1.289729472183878, 1.9126243306628208], index=0), Atom('Al', [-2.233873547699001, 3.869185551846968, 2.7799443936883206], index=1), Atom('Li', [9.007133262260959e-...</td>\n",
" <td>Li3Al2</td>\n",
" <td>trigonal</td>\n",
" <td>6.318351</td>\n",
" <td>81.143544</td>\n",
" <td>44.574696</td>\n",
" <td>-13.185198</td>\n",
" <td>5</td>\n",
" <td>Li3Al2_trigonal</td>\n",
" <td>{'Al': 2, 'Li': 3}</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>40.000000</td>\n",
" <td>60.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>223</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [2.220260976080854, 1.2818682724036983, 7.872085429446316], index=0), Atom('Li', [1.722758777253687e-10, 2.5637365444716322, 0.6790950189344616], index=1), Atom('Li', [1.72275877725368...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.279846</td>\n",
" <td>146.014891</td>\n",
" <td>37.664442</td>\n",
" <td>-21.680919</td>\n",
" <td>8</td>\n",
" <td>Li4Al4_cubic</td>\n",
" <td>{'Li': 4, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>236</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.044553</td>\n",
" <td>16.541594</td>\n",
" <td>87.130427</td>\n",
" <td>-3.478909</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>249</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.953036</td>\n",
" <td>16.811334</td>\n",
" <td>72.667242</td>\n",
" <td>-3.388831</td>\n",
" <td>1</td>\n",
" <td>Al_bcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>262</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.216389</td>\n",
" <td>20.403222</td>\n",
" <td>15.823747</td>\n",
" <td>-1.756104</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>275</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>4.331457</td>\n",
" <td>20.318983</td>\n",
" <td>14.231625</td>\n",
" <td>-1.755594</td>\n",
" <td>1</td>\n",
" <td>Li_fcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>288</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.367064</td>\n",
" <td>64.521799</td>\n",
" <td>46.107162</td>\n",
" <td>-11.185880</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" <td>{'Li': 2, 'Al': 2}</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>301</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.686989</td>\n",
" <td>65.028366</td>\n",
" <td>66.254925</td>\n",
" <td>-12.569153</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" <td>{'Li': 1, 'Al': 3}</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>75.000000</td>\n",
" <td>25.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>314</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [5.141009159558869, 1.0571139195527752, 0.820249453790277], index=0), Atom('Li', [3.2705789348169056, 1.5045550288016276, 2.715159327393234], index=1), Atom('Li', [-3.601125467999465, ...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.519944</td>\n",
" <td>213.136118</td>\n",
" <td>33.963240</td>\n",
" <td>-31.796316</td>\n",
" <td>13</td>\n",
" <td>Li9Al4_monoclinic</td>\n",
" <td>{'Li': 9, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>30.769231</td>\n",
" <td>69.230769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>327</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Al', [2.2270976540671734, 1.2858164055924044, 1.9025646270076813], index=0), Atom('Al', [-2.227095628822777, 3.8574462424884515, 2.7757665665986657], index=1), Atom('Li', [8.407589514518869...</td>\n",
" <td>Li3Al2</td>\n",
" <td>trigonal</td>\n",
" <td>6.299181</td>\n",
" <td>80.375104</td>\n",
" <td>39.643133</td>\n",
" <td>-13.138303</td>\n",
" <td>5</td>\n",
" <td>Li3Al2_trigonal</td>\n",
" <td>{'Al': 2, 'Li': 3}</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>40.000000</td>\n",
" <td>60.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>340</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [2.2269869888586107, 1.285751535686306, 7.864026721150146], index=0), Atom('Li', [-1.5554058443124377e-09, 2.571503074062492, 0.7130584901440213], index=1), Atom('Li', [-1.555405844312...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.298870</td>\n",
" <td>147.356944</td>\n",
" <td>46.701117</td>\n",
" <td>-21.607231</td>\n",
" <td>8</td>\n",
" <td>Li4Al4_cubic</td>\n",
" <td>{'Li': 4, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential \\\n",
"0 2 LiAl_eam \n",
"1 15 LiAl_eam \n",
"2 28 LiAl_eam \n",
"3 41 LiAl_eam \n",
"4 54 LiAl_eam \n",
"5 67 LiAl_eam \n",
"6 80 LiAl_eam \n",
"7 93 LiAl_eam \n",
"8 106 LiAl_eam \n",
"9 119 RuNNer-AlLi \n",
"10 132 RuNNer-AlLi \n",
"11 145 RuNNer-AlLi \n",
"12 158 RuNNer-AlLi \n",
"13 171 RuNNer-AlLi \n",
"14 184 RuNNer-AlLi \n",
"15 197 RuNNer-AlLi \n",
"16 210 RuNNer-AlLi \n",
"17 223 RuNNer-AlLi \n",
"18 236 LiAl_yace \n",
"19 249 LiAl_yace \n",
"20 262 LiAl_yace \n",
"21 275 LiAl_yace \n",
"22 288 LiAl_yace \n",
"23 301 LiAl_yace \n",
"24 314 LiAl_yace \n",
"25 327 LiAl_yace \n",
"26 340 LiAl_yace \n",
"\n",
" ase_atoms \\\n",
"0 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"1 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"2 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"3 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"4 (Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12... \n",
"5 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760... \n",
"6 (Atom('Li', [4.9874611628416465, 1.0099045365192156, 0.8188840806477526], index=0), Atom('Li', [3.1237816780987666, 1.455730745331952, 2.673723152073369], index=1), Atom('Li', [-3.4421956688209843... \n",
"7 (Atom('Al', [2.1548001975659234, 1.244075358781918, 1.861784175000869], index=0), Atom('Al', [-2.154798282819334, 3.732223313213554, 2.6646760238080542], index=1), Atom('Li', [8.560563403365654e-0... \n",
"8 (Atom('Li', [2.142967147985671, 1.2372426587287435, 7.662120717536293], index=0), Atom('Li', [-8.783761113500244e-10, 2.4744853189563414, 0.5913679335098909], index=1), Atom('Li', [-8.783761113500... \n",
"9 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"10 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"11 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"12 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"13 (Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12... \n",
"14 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998... \n",
"15 (Atom('Li', [5.206051477294367, 1.0619663179427192, 0.8311820920214751], index=0), Atom('Li', [3.28638171437237, 1.5211864250363467, 2.7226207058417775], index=1), Atom('Li', [-3.6198784902055765,... \n",
"16 (Atom('Al', [2.2338755345732753, 1.289729472183878, 1.9126243306628208], index=0), Atom('Al', [-2.233873547699001, 3.869185551846968, 2.7799443936883206], index=1), Atom('Li', [9.007133262260959e-... \n",
"17 (Atom('Li', [2.220260976080854, 1.2818682724036983, 7.872085429446316], index=0), Atom('Li', [1.722758777253687e-10, 2.5637365444716322, 0.6790950189344616], index=1), Atom('Li', [1.72275877725368... \n",
"18 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"19 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"20 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"21 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"22 (Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e... \n",
"23 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993... \n",
"24 (Atom('Li', [5.141009159558869, 1.0571139195527752, 0.820249453790277], index=0), Atom('Li', [3.2705789348169056, 1.5045550288016276, 2.715159327393234], index=1), Atom('Li', [-3.601125467999465, ... \n",
"25 (Atom('Al', [2.2270976540671734, 1.2858164055924044, 1.9025646270076813], index=0), Atom('Al', [-2.227095628822777, 3.8574462424884515, 2.7757665665986657], index=1), Atom('Li', [8.407589514518869... \n",
"26 (Atom('Li', [2.2269869888586107, 1.285751535686306, 7.864026721150146], index=0), Atom('Li', [-1.5554058443124377e-09, 2.571503074062492, 0.7130584901440213], index=1), Atom('Li', [-1.555405844312... \n",
"\n",
" compound crystal_structure a eq_vol eq_bm eq_energy \\\n",
"0 Al fcc 4.039967 16.495612 85.876912 -3.483097 \n",
"1 Al bcc 3.898853 16.147864 48.620841 -3.415312 \n",
"2 Li bcc 4.195477 20.114514 13.690609 -1.757011 \n",
"3 Li fcc 4.253841 19.241330 13.985972 -1.758107 \n",
"4 Li2Al2 cubic 6.165940 58.604895 100.347240 -11.074362 \n",
"5 LiAl3 cubic 5.607502 62.227580 51.472656 -12.774590 \n",
"6 Li9Al4 monoclinic 13.023701 190.504374 53.125276 -28.970054 \n",
"7 Li3Al2 trigonal 6.094693 72.810229 69.231669 -12.413856 \n",
"8 Li4Al4 cubic 6.061226 131.389799 71.221355 -20.506570 \n",
"9 Al fcc 4.025259 16.355737 76.669339 -3.484016 \n",
"10 Al bcc 3.958447 16.870137 51.052272 -3.432183 \n",
"11 Li bcc 4.211118 20.286595 8.517306 -1.755918 \n",
"12 Li fcc 3.967043 15.678901 147.215464 -1.769260 \n",
"13 Li2Al2 cubic 6.376805 64.816143 57.934650 -11.212634 \n",
"14 LiAl3 cubic 5.700455 65.403086 59.308440 -12.574696 \n",
"15 Li9Al4 monoclinic 13.640614 218.932018 33.874957 -31.820765 \n",
"16 Li3Al2 trigonal 6.318351 81.143544 44.574696 -13.185198 \n",
"17 Li4Al4 cubic 6.279846 146.014891 37.664442 -21.680919 \n",
"18 Al fcc 4.044553 16.541594 87.130427 -3.478909 \n",
"19 Al bcc 3.953036 16.811334 72.667242 -3.388831 \n",
"20 Li bcc 4.216389 20.403222 15.823747 -1.756104 \n",
"21 Li fcc 4.331457 20.318983 14.231625 -1.755594 \n",
"22 Li2Al2 cubic 6.367064 64.521799 46.107162 -11.185880 \n",
"23 LiAl3 cubic 5.686989 65.028366 66.254925 -12.569153 \n",
"24 Li9Al4 monoclinic 13.519944 213.136118 33.963240 -31.796316 \n",
"25 Li3Al2 trigonal 6.299181 80.375104 39.643133 -13.138303 \n",
"26 Li4Al4 cubic 6.298870 147.356944 46.701117 -21.607231 \n",
"\n",
" n_atoms phase comp_dict n_Al n_Li cAl \\\n",
"0 1 Al_fcc {'Al': 1} 1 0 100.000000 \n",
"1 1 Al_bcc {'Al': 1} 1 0 100.000000 \n",
"2 1 Li_bcc {'Li': 1} 0 1 0.000000 \n",
"3 1 Li_fcc {'Li': 1} 0 1 0.000000 \n",
"4 4 Li2Al2_cubic {'Li': 2, 'Al': 2} 2 2 50.000000 \n",
"5 4 LiAl3_cubic {'Li': 1, 'Al': 3} 3 1 75.000000 \n",
"6 13 Li9Al4_monoclinic {'Li': 9, 'Al': 4} 4 9 30.769231 \n",
"7 5 Li3Al2_trigonal {'Al': 2, 'Li': 3} 2 3 40.000000 \n",
"8 8 Li4Al4_cubic {'Li': 4, 'Al': 4} 4 4 50.000000 \n",
"9 1 Al_fcc {'Al': 1} 1 0 100.000000 \n",
"10 1 Al_bcc {'Al': 1} 1 0 100.000000 \n",
"11 1 Li_bcc {'Li': 1} 0 1 0.000000 \n",
"12 1 Li_fcc {'Li': 1} 0 1 0.000000 \n",
"13 4 Li2Al2_cubic {'Li': 2, 'Al': 2} 2 2 50.000000 \n",
"14 4 LiAl3_cubic {'Li': 1, 'Al': 3} 3 1 75.000000 \n",
"15 13 Li9Al4_monoclinic {'Li': 9, 'Al': 4} 4 9 30.769231 \n",
"16 5 Li3Al2_trigonal {'Al': 2, 'Li': 3} 2 3 40.000000 \n",
"17 8 Li4Al4_cubic {'Li': 4, 'Al': 4} 4 4 50.000000 \n",
"18 1 Al_fcc {'Al': 1} 1 0 100.000000 \n",
"19 1 Al_bcc {'Al': 1} 1 0 100.000000 \n",
"20 1 Li_bcc {'Li': 1} 0 1 0.000000 \n",
"21 1 Li_fcc {'Li': 1} 0 1 0.000000 \n",
"22 4 Li2Al2_cubic {'Li': 2, 'Al': 2} 2 2 50.000000 \n",
"23 4 LiAl3_cubic {'Li': 1, 'Al': 3} 3 1 75.000000 \n",
"24 13 Li9Al4_monoclinic {'Li': 9, 'Al': 4} 4 9 30.769231 \n",
"25 5 Li3Al2_trigonal {'Al': 2, 'Li': 3} 2 3 40.000000 \n",
"26 8 Li4Al4_cubic {'Li': 4, 'Al': 4} 4 4 50.000000 \n",
"\n",
" cLi \n",
"0 0.000000 \n",
"1 0.000000 \n",
"2 100.000000 \n",
"3 100.000000 \n",
"4 50.000000 \n",
"5 25.000000 \n",
"6 69.230769 \n",
"7 60.000000 \n",
"8 50.000000 \n",
"9 0.000000 \n",
"10 0.000000 \n",
"11 100.000000 \n",
"12 100.000000 \n",
"13 50.000000 \n",
"14 25.000000 \n",
"15 69.230769 \n",
"16 60.000000 \n",
"17 50.000000 \n",
"18 0.000000 \n",
"19 0.000000 \n",
"20 100.000000 \n",
"21 100.000000 \n",
"22 50.000000 \n",
"23 25.000000 \n",
"24 69.230769 \n",
"25 60.000000 \n",
"26 50.000000 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_convexhull[\"comp_dict\"] = data_convexhull[\"ase_atoms\"].map(lambda at: Counter(at.get_chemical_symbols()))\n",
"data_convexhull[\"n_Al\"] = data_convexhull[\"comp_dict\"].map(lambda d: d.get(\"Al\",0))\n",
"data_convexhull[\"n_Li\"] = data_convexhull[\"comp_dict\"].map(lambda d: d.get(\"Li\",0))\n",
"\n",
"data_convexhull[\"cAl\"]= data_convexhull[\"n_Al\"]/data_convexhull[\"n_atoms\"] * 100\n",
"data_convexhull[\"cLi\"]= data_convexhull[\"n_Li\"]/data_convexhull[\"n_atoms\"] * 100\n",
"\n",
"data_convexhull"
]
},
{
"cell_type": "markdown",
"id": "13e71b7d",
"metadata": {},
"source": [
"Obtain the equilibrium energies for unary Al and Li phases from the Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "3fccd2e0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-3.484015731440474, -1.7692598828082993]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"E_f_Al = data_convexhull.loc[data_convexhull[\"n_Li\"]==0,\"eq_energy\"].min()\n",
"E_f_Li = data_convexhull.loc[data_convexhull[\"n_Al\"]==0,\"eq_energy\"].min()\n",
"\n",
"[E_f_Al, E_f_Li]"
]
},
{
"cell_type": "markdown",
"id": "88533361",
"metadata": {},
"source": [
"Calculate the relative formation energies by subtracting the total energies of the mixed phases with the energies of eq Al and Li\n",
"\n",
"$$E^{A_xB_y}_{f} = E_{A_xB_y} - (x E_A + yE_B)$$"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "43b89ed8",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
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" }\n",
"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
" <th>potential</th>\n",
" <th>ase_atoms</th>\n",
" <th>compound</th>\n",
" <th>crystal_structure</th>\n",
" <th>a</th>\n",
" <th>eq_vol</th>\n",
" <th>eq_bm</th>\n",
" <th>eq_energy</th>\n",
" <th>n_atoms</th>\n",
" <th>phase</th>\n",
" <th>comp_dict</th>\n",
" <th>n_Al</th>\n",
" <th>n_Li</th>\n",
" <th>cAl</th>\n",
" <th>cLi</th>\n",
" <th>E_form</th>\n",
" <th>E_form_per_atom</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.039967</td>\n",
" <td>16.495612</td>\n",
" <td>85.876912</td>\n",
" <td>-3.483097</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000919</td>\n",
" <td>0.918900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.898853</td>\n",
" <td>16.147864</td>\n",
" <td>48.620841</td>\n",
" <td>-3.415312</td>\n",
" <td>1</td>\n",
" <td>Al_bcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.068704</td>\n",
" <td>68.704086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>249</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.953036</td>\n",
" <td>16.811334</td>\n",
" <td>72.667242</td>\n",
" <td>-3.388831</td>\n",
" <td>1</td>\n",
" <td>Al_bcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.095184</td>\n",
" <td>95.184393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>236</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.044553</td>\n",
" <td>16.541594</td>\n",
" <td>87.130427</td>\n",
" <td>-3.478909</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.005107</td>\n",
" <td>5.106504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>119</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.025259</td>\n",
" <td>16.355737</td>\n",
" <td>76.669339</td>\n",
" <td>-3.484016</td>\n",
" <td>1</td>\n",
" <td>Al_fcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>132</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.958447</td>\n",
" <td>16.870137</td>\n",
" <td>51.052272</td>\n",
" <td>-3.432183</td>\n",
" <td>1</td>\n",
" <td>Al_bcc</td>\n",
" <td>{'Al': 1}</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.051832</td>\n",
" <td>51.832389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>301</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.686989</td>\n",
" <td>65.028366</td>\n",
" <td>66.254925</td>\n",
" <td>-12.569153</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" <td>{'Li': 1, 'Al': 3}</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>75.000000</td>\n",
" <td>25.000000</td>\n",
" <td>-0.347845</td>\n",
" <td>-86.961359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>67</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.607502</td>\n",
" <td>62.227580</td>\n",
" <td>51.472656</td>\n",
" <td>-12.774590</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" <td>{'Li': 1, 'Al': 3}</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>75.000000</td>\n",
" <td>25.000000</td>\n",
" <td>-0.553283</td>\n",
" <td>-138.320664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>184</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998...</td>\n",
" <td>LiAl3</td>\n",
" <td>cubic</td>\n",
" <td>5.700455</td>\n",
" <td>65.403086</td>\n",
" <td>59.308440</td>\n",
" <td>-12.574696</td>\n",
" <td>4</td>\n",
" <td>LiAl3_cubic</td>\n",
" <td>{'Li': 1, 'Al': 3}</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>75.000000</td>\n",
" <td>25.000000</td>\n",
" <td>-0.353389</td>\n",
" <td>-88.347230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>288</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.367064</td>\n",
" <td>64.521799</td>\n",
" <td>46.107162</td>\n",
" <td>-11.185880</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" <td>{'Li': 2, 'Al': 2}</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" <td>-0.679329</td>\n",
" <td>-169.832278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>223</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [2.220260976080854, 1.2818682724036983, 7.872085429446316], index=0), Atom('Li', [1.722758777253687e-10, 2.5637365444716322, 0.6790950189344616], index=1), Atom('Li', [1.72275877725368...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.279846</td>\n",
" <td>146.014891</td>\n",
" <td>37.664442</td>\n",
" <td>-21.680919</td>\n",
" <td>8</td>\n",
" <td>Li4Al4_cubic</td>\n",
" <td>{'Li': 4, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" <td>-0.667816</td>\n",
" <td>-83.477017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>171</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.376805</td>\n",
" <td>64.816143</td>\n",
" <td>57.934650</td>\n",
" <td>-11.212634</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" <td>{'Li': 2, 'Al': 2}</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" <td>-0.706083</td>\n",
" <td>-176.520795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>106</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [2.142967147985671, 1.2372426587287435, 7.662120717536293], index=0), Atom('Li', [-8.783761113500244e-10, 2.4744853189563414, 0.5913679335098909], index=1), Atom('Li', [-8.783761113500...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.061226</td>\n",
" <td>131.389799</td>\n",
" <td>71.221355</td>\n",
" <td>-20.506570</td>\n",
" <td>8</td>\n",
" <td>Li4Al4_cubic</td>\n",
" <td>{'Li': 4, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" <td>0.506533</td>\n",
" <td>63.316583</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>54</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12...</td>\n",
" <td>Li2Al2</td>\n",
" <td>cubic</td>\n",
" <td>6.165940</td>\n",
" <td>58.604895</td>\n",
" <td>100.347240</td>\n",
" <td>-11.074362</td>\n",
" <td>4</td>\n",
" <td>Li2Al2_cubic</td>\n",
" <td>{'Li': 2, 'Al': 2}</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" <td>-0.567811</td>\n",
" <td>-141.952730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>340</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [2.2269869888586107, 1.285751535686306, 7.864026721150146], index=0), Atom('Li', [-1.5554058443124377e-09, 2.571503074062492, 0.7130584901440213], index=1), Atom('Li', [-1.555405844312...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.298870</td>\n",
" <td>147.356944</td>\n",
" <td>46.701117</td>\n",
" <td>-21.607231</td>\n",
" <td>8</td>\n",
" <td>Li4Al4_cubic</td>\n",
" <td>{'Li': 4, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" <td>-0.594129</td>\n",
" <td>-74.266095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>327</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Al', [2.2270976540671734, 1.2858164055924044, 1.9025646270076813], index=0), Atom('Al', [-2.227095628822777, 3.8574462424884515, 2.7757665665986657], index=1), Atom('Li', [8.407589514518869...</td>\n",
" <td>Li3Al2</td>\n",
" <td>trigonal</td>\n",
" <td>6.299181</td>\n",
" <td>80.375104</td>\n",
" <td>39.643133</td>\n",
" <td>-13.138303</td>\n",
" <td>5</td>\n",
" <td>Li3Al2_trigonal</td>\n",
" <td>{'Al': 2, 'Li': 3}</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>40.000000</td>\n",
" <td>60.000000</td>\n",
" <td>-0.862492</td>\n",
" <td>-172.498406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>210</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Al', [2.2338755345732753, 1.289729472183878, 1.9126243306628208], index=0), Atom('Al', [-2.233873547699001, 3.869185551846968, 2.7799443936883206], index=1), Atom('Li', [9.007133262260959e-...</td>\n",
" <td>Li3Al2</td>\n",
" <td>trigonal</td>\n",
" <td>6.318351</td>\n",
" <td>81.143544</td>\n",
" <td>44.574696</td>\n",
" <td>-13.185198</td>\n",
" <td>5</td>\n",
" <td>Li3Al2_trigonal</td>\n",
" <td>{'Al': 2, 'Li': 3}</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>40.000000</td>\n",
" <td>60.000000</td>\n",
" <td>-0.909387</td>\n",
" <td>-181.877324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>93</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [2.1548001975659234, 1.244075358781918, 1.861784175000869], index=0), Atom('Al', [-2.154798282819334, 3.732223313213554, 2.6646760238080542], index=1), Atom('Li', [8.560563403365654e-0...</td>\n",
" <td>Li3Al2</td>\n",
" <td>trigonal</td>\n",
" <td>6.094693</td>\n",
" <td>72.810229</td>\n",
" <td>69.231669</td>\n",
" <td>-12.413856</td>\n",
" <td>5</td>\n",
" <td>Li3Al2_trigonal</td>\n",
" <td>{'Al': 2, 'Li': 3}</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>40.000000</td>\n",
" <td>60.000000</td>\n",
" <td>-0.138045</td>\n",
" <td>-27.609020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>197</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [5.206051477294367, 1.0619663179427192, 0.8311820920214751], index=0), Atom('Li', [3.28638171437237, 1.5211864250363467, 2.7226207058417775], index=1), Atom('Li', [-3.6198784902055765,...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.640614</td>\n",
" <td>218.932018</td>\n",
" <td>33.874957</td>\n",
" <td>-31.820765</td>\n",
" <td>13</td>\n",
" <td>Li9Al4_monoclinic</td>\n",
" <td>{'Li': 9, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>30.769231</td>\n",
" <td>69.230769</td>\n",
" <td>-1.961363</td>\n",
" <td>-150.874092</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>80</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.9874611628416465, 1.0099045365192156, 0.8188840806477526], index=0), Atom('Li', [3.1237816780987666, 1.455730745331952, 2.673723152073369], index=1), Atom('Li', [-3.4421956688209843...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.023701</td>\n",
" <td>190.504374</td>\n",
" <td>53.125276</td>\n",
" <td>-28.970054</td>\n",
" <td>13</td>\n",
" <td>Li9Al4_monoclinic</td>\n",
" <td>{'Li': 9, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>30.769231</td>\n",
" <td>69.230769</td>\n",
" <td>0.889348</td>\n",
" <td>68.411395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>314</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [5.141009159558869, 1.0571139195527752, 0.820249453790277], index=0), Atom('Li', [3.2705789348169056, 1.5045550288016276, 2.715159327393234], index=1), Atom('Li', [-3.601125467999465, ...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.519944</td>\n",
" <td>213.136118</td>\n",
" <td>33.963240</td>\n",
" <td>-31.796316</td>\n",
" <td>13</td>\n",
" <td>Li9Al4_monoclinic</td>\n",
" <td>{'Li': 9, 'Al': 4}</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>30.769231</td>\n",
" <td>69.230769</td>\n",
" <td>-1.936914</td>\n",
" <td>-148.993374</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>158</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>3.967043</td>\n",
" <td>15.678901</td>\n",
" <td>147.215464</td>\n",
" <td>-1.769260</td>\n",
" <td>1</td>\n",
" <td>Li_fcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>262</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.216389</td>\n",
" <td>20.403222</td>\n",
" <td>15.823747</td>\n",
" <td>-1.756104</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.013156</td>\n",
" <td>13.156331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>275</td>\n",
" <td>LiAl_yace</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>4.331457</td>\n",
" <td>20.318983</td>\n",
" <td>14.231625</td>\n",
" <td>-1.755594</td>\n",
" <td>1</td>\n",
" <td>Li_fcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.013666</td>\n",
" <td>13.665671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>41</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>4.253841</td>\n",
" <td>19.241330</td>\n",
" <td>13.985972</td>\n",
" <td>-1.758107</td>\n",
" <td>1</td>\n",
" <td>Li_fcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.011153</td>\n",
" <td>11.152545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.195477</td>\n",
" <td>20.114514</td>\n",
" <td>13.690609</td>\n",
" <td>-1.757011</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.012249</td>\n",
" <td>12.248592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>145</td>\n",
" <td>RuNNer-AlLi</td>\n",
" <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.211118</td>\n",
" <td>20.286595</td>\n",
" <td>8.517306</td>\n",
" <td>-1.755918</td>\n",
" <td>1</td>\n",
" <td>Li_bcc</td>\n",
" <td>{'Li': 1}</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.013342</td>\n",
" <td>13.341610</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential \\\n",
"0 2 LiAl_eam \n",
"1 15 LiAl_eam \n",
"19 249 LiAl_yace \n",
"18 236 LiAl_yace \n",
"9 119 RuNNer-AlLi \n",
"10 132 RuNNer-AlLi \n",
"23 301 LiAl_yace \n",
"5 67 LiAl_eam \n",
"14 184 RuNNer-AlLi \n",
"22 288 LiAl_yace \n",
"17 223 RuNNer-AlLi \n",
"13 171 RuNNer-AlLi \n",
"8 106 LiAl_eam \n",
"4 54 LiAl_eam \n",
"26 340 LiAl_yace \n",
"25 327 LiAl_yace \n",
"16 210 RuNNer-AlLi \n",
"7 93 LiAl_eam \n",
"15 197 RuNNer-AlLi \n",
"6 80 LiAl_eam \n",
"24 314 LiAl_yace \n",
"12 158 RuNNer-AlLi \n",
"20 262 LiAl_yace \n",
"21 275 LiAl_yace \n",
"3 41 LiAl_eam \n",
"2 28 LiAl_eam \n",
"11 145 RuNNer-AlLi \n",
"\n",
" ase_atoms \\\n",
"0 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"1 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"19 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"18 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"9 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"10 (Atom('Al', [0.0, 0.0, 0.0], index=0)) \n",
"23 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993... \n",
"5 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760... \n",
"14 (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998... \n",
"22 (Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e... \n",
"17 (Atom('Li', [2.220260976080854, 1.2818682724036983, 7.872085429446316], index=0), Atom('Li', [1.722758777253687e-10, 2.5637365444716322, 0.6790950189344616], index=1), Atom('Li', [1.72275877725368... \n",
"13 (Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12... \n",
"8 (Atom('Li', [2.142967147985671, 1.2372426587287435, 7.662120717536293], index=0), Atom('Li', [-8.783761113500244e-10, 2.4744853189563414, 0.5913679335098909], index=1), Atom('Li', [-8.783761113500... \n",
"4 (Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12... \n",
"26 (Atom('Li', [2.2269869888586107, 1.285751535686306, 7.864026721150146], index=0), Atom('Li', [-1.5554058443124377e-09, 2.571503074062492, 0.7130584901440213], index=1), Atom('Li', [-1.555405844312... \n",
"25 (Atom('Al', [2.2270976540671734, 1.2858164055924044, 1.9025646270076813], index=0), Atom('Al', [-2.227095628822777, 3.8574462424884515, 2.7757665665986657], index=1), Atom('Li', [8.407589514518869... \n",
"16 (Atom('Al', [2.2338755345732753, 1.289729472183878, 1.9126243306628208], index=0), Atom('Al', [-2.233873547699001, 3.869185551846968, 2.7799443936883206], index=1), Atom('Li', [9.007133262260959e-... \n",
"7 (Atom('Al', [2.1548001975659234, 1.244075358781918, 1.861784175000869], index=0), Atom('Al', [-2.154798282819334, 3.732223313213554, 2.6646760238080542], index=1), Atom('Li', [8.560563403365654e-0... \n",
"15 (Atom('Li', [5.206051477294367, 1.0619663179427192, 0.8311820920214751], index=0), Atom('Li', [3.28638171437237, 1.5211864250363467, 2.7226207058417775], index=1), Atom('Li', [-3.6198784902055765,... \n",
"6 (Atom('Li', [4.9874611628416465, 1.0099045365192156, 0.8188840806477526], index=0), Atom('Li', [3.1237816780987666, 1.455730745331952, 2.673723152073369], index=1), Atom('Li', [-3.4421956688209843... \n",
"24 (Atom('Li', [5.141009159558869, 1.0571139195527752, 0.820249453790277], index=0), Atom('Li', [3.2705789348169056, 1.5045550288016276, 2.715159327393234], index=1), Atom('Li', [-3.601125467999465, ... \n",
"12 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"20 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"21 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"3 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"2 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"11 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"\n",
" compound crystal_structure a eq_vol eq_bm eq_energy \\\n",
"0 Al fcc 4.039967 16.495612 85.876912 -3.483097 \n",
"1 Al bcc 3.898853 16.147864 48.620841 -3.415312 \n",
"19 Al bcc 3.953036 16.811334 72.667242 -3.388831 \n",
"18 Al fcc 4.044553 16.541594 87.130427 -3.478909 \n",
"9 Al fcc 4.025259 16.355737 76.669339 -3.484016 \n",
"10 Al bcc 3.958447 16.870137 51.052272 -3.432183 \n",
"23 LiAl3 cubic 5.686989 65.028366 66.254925 -12.569153 \n",
"5 LiAl3 cubic 5.607502 62.227580 51.472656 -12.774590 \n",
"14 LiAl3 cubic 5.700455 65.403086 59.308440 -12.574696 \n",
"22 Li2Al2 cubic 6.367064 64.521799 46.107162 -11.185880 \n",
"17 Li4Al4 cubic 6.279846 146.014891 37.664442 -21.680919 \n",
"13 Li2Al2 cubic 6.376805 64.816143 57.934650 -11.212634 \n",
"8 Li4Al4 cubic 6.061226 131.389799 71.221355 -20.506570 \n",
"4 Li2Al2 cubic 6.165940 58.604895 100.347240 -11.074362 \n",
"26 Li4Al4 cubic 6.298870 147.356944 46.701117 -21.607231 \n",
"25 Li3Al2 trigonal 6.299181 80.375104 39.643133 -13.138303 \n",
"16 Li3Al2 trigonal 6.318351 81.143544 44.574696 -13.185198 \n",
"7 Li3Al2 trigonal 6.094693 72.810229 69.231669 -12.413856 \n",
"15 Li9Al4 monoclinic 13.640614 218.932018 33.874957 -31.820765 \n",
"6 Li9Al4 monoclinic 13.023701 190.504374 53.125276 -28.970054 \n",
"24 Li9Al4 monoclinic 13.519944 213.136118 33.963240 -31.796316 \n",
"12 Li fcc 3.967043 15.678901 147.215464 -1.769260 \n",
"20 Li bcc 4.216389 20.403222 15.823747 -1.756104 \n",
"21 Li fcc 4.331457 20.318983 14.231625 -1.755594 \n",
"3 Li fcc 4.253841 19.241330 13.985972 -1.758107 \n",
"2 Li bcc 4.195477 20.114514 13.690609 -1.757011 \n",
"11 Li bcc 4.211118 20.286595 8.517306 -1.755918 \n",
"\n",
" n_atoms phase comp_dict n_Al n_Li cAl \\\n",
"0 1 Al_fcc {'Al': 1} 1 0 100.000000 \n",
"1 1 Al_bcc {'Al': 1} 1 0 100.000000 \n",
"19 1 Al_bcc {'Al': 1} 1 0 100.000000 \n",
"18 1 Al_fcc {'Al': 1} 1 0 100.000000 \n",
"9 1 Al_fcc {'Al': 1} 1 0 100.000000 \n",
"10 1 Al_bcc {'Al': 1} 1 0 100.000000 \n",
"23 4 LiAl3_cubic {'Li': 1, 'Al': 3} 3 1 75.000000 \n",
"5 4 LiAl3_cubic {'Li': 1, 'Al': 3} 3 1 75.000000 \n",
"14 4 LiAl3_cubic {'Li': 1, 'Al': 3} 3 1 75.000000 \n",
"22 4 Li2Al2_cubic {'Li': 2, 'Al': 2} 2 2 50.000000 \n",
"17 8 Li4Al4_cubic {'Li': 4, 'Al': 4} 4 4 50.000000 \n",
"13 4 Li2Al2_cubic {'Li': 2, 'Al': 2} 2 2 50.000000 \n",
"8 8 Li4Al4_cubic {'Li': 4, 'Al': 4} 4 4 50.000000 \n",
"4 4 Li2Al2_cubic {'Li': 2, 'Al': 2} 2 2 50.000000 \n",
"26 8 Li4Al4_cubic {'Li': 4, 'Al': 4} 4 4 50.000000 \n",
"25 5 Li3Al2_trigonal {'Al': 2, 'Li': 3} 2 3 40.000000 \n",
"16 5 Li3Al2_trigonal {'Al': 2, 'Li': 3} 2 3 40.000000 \n",
"7 5 Li3Al2_trigonal {'Al': 2, 'Li': 3} 2 3 40.000000 \n",
"15 13 Li9Al4_monoclinic {'Li': 9, 'Al': 4} 4 9 30.769231 \n",
"6 13 Li9Al4_monoclinic {'Li': 9, 'Al': 4} 4 9 30.769231 \n",
"24 13 Li9Al4_monoclinic {'Li': 9, 'Al': 4} 4 9 30.769231 \n",
"12 1 Li_fcc {'Li': 1} 0 1 0.000000 \n",
"20 1 Li_bcc {'Li': 1} 0 1 0.000000 \n",
"21 1 Li_fcc {'Li': 1} 0 1 0.000000 \n",
"3 1 Li_fcc {'Li': 1} 0 1 0.000000 \n",
"2 1 Li_bcc {'Li': 1} 0 1 0.000000 \n",
"11 1 Li_bcc {'Li': 1} 0 1 0.000000 \n",
"\n",
" cLi E_form E_form_per_atom \n",
"0 0.000000 0.000919 0.918900 \n",
"1 0.000000 0.068704 68.704086 \n",
"19 0.000000 0.095184 95.184393 \n",
"18 0.000000 0.005107 5.106504 \n",
"9 0.000000 0.000000 0.000000 \n",
"10 0.000000 0.051832 51.832389 \n",
"23 25.000000 -0.347845 -86.961359 \n",
"5 25.000000 -0.553283 -138.320664 \n",
"14 25.000000 -0.353389 -88.347230 \n",
"22 50.000000 -0.679329 -169.832278 \n",
"17 50.000000 -0.667816 -83.477017 \n",
"13 50.000000 -0.706083 -176.520795 \n",
"8 50.000000 0.506533 63.316583 \n",
"4 50.000000 -0.567811 -141.952730 \n",
"26 50.000000 -0.594129 -74.266095 \n",
"25 60.000000 -0.862492 -172.498406 \n",
"16 60.000000 -0.909387 -181.877324 \n",
"7 60.000000 -0.138045 -27.609020 \n",
"15 69.230769 -1.961363 -150.874092 \n",
"6 69.230769 0.889348 68.411395 \n",
"24 69.230769 -1.936914 -148.993374 \n",
"12 100.000000 0.000000 0.000000 \n",
"20 100.000000 0.013156 13.156331 \n",
"21 100.000000 0.013666 13.665671 \n",
"3 100.000000 0.011153 11.152545 \n",
"2 100.000000 0.012249 12.248592 \n",
"11 100.000000 0.013342 13.341610 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_convexhull[\"E_form\"]=(data_convexhull[\"eq_energy\"])-(data_convexhull[[\"n_Al\",\"n_Li\"]].values * [E_f_Al, E_f_Li]).sum(axis=1)\n",
"data_convexhull[\"E_form_per_atom\"] = data_convexhull[\"E_form\"]/data_convexhull[\"n_atoms\"] * 1e3\n",
"\n",
"data_convexhull = data_convexhull.sort_values(\"cLi\")\n",
"\n",
"data_convexhull"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "35df2f07",
"metadata": {},
"outputs": [],
"source": [
"subset_covexhull = data_convexhull[data_convexhull[\"phase\"].isin([\"Al_fcc\",\"LiAl3_cubic\",\"Li2Al2_cubic\",\"Li_bcc\"])]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "ea09f703-8f80-41be-972d-1d894885c2ba",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1584x576 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig,ax = plt.subplots(figsize=(22,8),ncols=len(potentials_list),constrained_layout=True)\n",
"\n",
"dfs = ([pd.DataFrame(y) for x, y in data_convexhull.groupby(by='potential', as_index=False)])\n",
"\n",
"for i,pot in enumerate(potentials_list):\n",
" sns.lineplot(data=dfs[i],\n",
" marker='o',\n",
" x='cLi', y='E_form_per_atom',\n",
" estimator=np.min,\n",
" ax=ax[i],lw=3)\n",
"\n",
" ax[i].axhline(0,ls=\"--\",color=\"k\")\n",
" ax[i].plot(dfs[i][\"cLi\"], dfs[i][\"E_form_per_atom\"],\"o\",markersize=10)\n",
" # ax.legend()\n",
" ax[i].set_xlabel(\"Li,%\",fontsize=\"20\")\n",
" ax[i].set_ylabel(\"E$_f$, meV/atom\",fontsize=\"20\")\n",
" ax[i].tick_params(labelsize=20,axis=\"both\")\n",
"# ax.set_ylim(-200,10)\n",
" for _,row in dfs[i].iterrows():\n",
" ax[i].text((row[\"cLi\"]+0.01),row[\"E_form_per_atom\"],row[\"phase\"],size=12)\n",
" \n",
" ax[i].set_title(f\"{get_clean_project_name(pot)}\",fontsize=22)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ca9dbcc8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total run time for the notebook 562.4219348430634 seconds\n"
]
}
],
"source": [
"time_stop = time.time()\n",
"print(f\"Total run time for the notebook {time_stop - time_start} seconds\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "59d98667-c265-4a29-9267-25faeb033471",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6.624843748410543"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"397.49062490463257/60"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "76ca2c3f-b8ee-4b4f-a3f9-ea996533c5cb",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}