{
"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",
" 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>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/minaam.qamar/workshop_POTENTIALS/new_no...</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 ... Config\n",
"0 LiAl_eam ... [pair_style eam/fs\\n, pair_coeff * * AlLi.eam....\n",
"\n",
"[1 rows x 5 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from helper import potentials_list\n",
"\n",
"potentials_list = [potentials_list[0]]\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": [
"6.366608268"
]
},
"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.process_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": [],
"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": 34,
"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": 34,
"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": 35,
"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": 36,
"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: 481\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-30 19:18:40,708 - pyiron_log - WARNING - The job murn_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job Al_bcc_relax was saved and received the ID: 482\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-30 19:18:44,855 - pyiron_log - WARNING - The job murn_job_Al_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job Li_bcc_relax was saved and received the ID: 483\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-30 19:18:49,187 - pyiron_log - WARNING - The job murn_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job Li_fcc_relax was saved and received the ID: 484\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-30 19:18:55,198 - pyiron_log - WARNING - The job murn_job_Li_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job Li2Al2_Li2Al2_cubic_relax was saved and received the ID: 485\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-30 19:19:00,132 - pyiron_log - WARNING - The job murn_job_Li2Al2_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job LiAl3_LiAl3_cubic_relax was saved and received the ID: 486\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-30 19:19:06,418 - pyiron_log - WARNING - The job murn_job_LiAl3_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job Li9Al4_Li9Al4_monoclinic_relax was saved and received the ID: 487\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-30 19:19:11,676 - pyiron_log - WARNING - The job murn_job_Li9Al4_Li9Al4_monoclinic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job Li3Al2_Li3Al2_trigonal_relax was saved and received the ID: 488\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-30 19:19:14,923 - pyiron_log - WARNING - The job murn_job_Li3Al2_Li3Al2_trigonal is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job Li4Al4_Li4Al4_cubic_relax was saved and received the ID: 489\n",
"The job murn_job_Li4Al4_Li4Al4_cubic was saved and received the ID: 490\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_9 was saved and received the ID: 491\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_92 was saved and received the ID: 492\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_94 was saved and received the ID: 493\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_96 was saved and received the ID: 494\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_0_98 was saved and received the ID: 495\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_0 was saved and received the ID: 496\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_02 was saved and received the ID: 497\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_04 was saved and received the ID: 498\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_06 was saved and received the ID: 499\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_08 was saved and received the ID: 500\n",
"The job murn_job_Li4Al4_Li4Al4_cubic_1_1 was saved and received the ID: 501\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": 37,
"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": 38,
"id": "255c28af-e4af-48c6-ae01-e90377c94e32",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job table_murn was saved and received the ID: 502\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a5d41b6f348b4e31b85295d74e6f93b0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading and filtering jobs: 0%| | 0/9 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d4443355c4a458584cfc4343b77d69b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Processing jobs: 0%| | 0/9 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/minaam.qamar/.conda/envs/workshop2/lib/python3.10/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>298</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>311</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>324</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>337</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>350</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.359978178265942, 2.5172345748814804, 1.7799536377360752], index=0), Atom('Li', [6.53996726740165, 3.7758518623203585, 2.669930456604318], index=1), Atom('Al', [-3.964456982410852e-1...</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>363</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>376</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.9874675377354745, 1.0099032069001204, 0.8188717268019969], index=0), Atom('Li', [3.1237856900347722, 1.4557299809760758, 2.6737242758835587], index=1), Atom('Li', [-3.44219970899484...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.023702</td>\n",
" <td>190.504371</td>\n",
" <td>53.125273</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>389</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [2.1548001975659243, 1.2440753587819189, 1.8617841750008692], index=0), Atom('Al', [-2.154798282819334, 3.7322233132135576, 2.664676023808053], index=1), Atom('Li', [8.560563403365655e...</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>490</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [2.1429671662366734, 1.237242669260901, 7.662122465542487], index=0), Atom('Li', [-8.827603092953495e-10, 2.4744853400282523, 0.5913662557792814], index=1), Atom('Li', [-8.827603092953...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.061226</td>\n",
" <td>131.389799</td>\n",
" <td>71.221356</td>\n",
" <td>-20.506570</td>\n",
" <td>8</td>\n",
" <td>Li4Al4_cubic</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential ... n_atoms phase\n",
"0 298 LiAl_eam ... 1 Al_fcc\n",
"1 311 LiAl_eam ... 1 Al_bcc\n",
"2 324 LiAl_eam ... 1 Li_bcc\n",
"3 337 LiAl_eam ... 1 Li_fcc\n",
"4 350 LiAl_eam ... 4 Li2Al2_cubic\n",
"5 363 LiAl_eam ... 4 LiAl3_cubic\n",
"6 376 LiAl_eam ... 13 Li9Al4_monoclinic\n",
"7 389 LiAl_eam ... 5 Li3Al2_trigonal\n",
"8 490 LiAl_eam ... 8 Li4Al4_cubic\n",
"\n",
"[9 rows x 11 columns]"
]
},
"execution_count": 38,
"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": 40,
"id": "30d27d75",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 480x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax_list = plt.subplots(ncols=len(potentials_list), nrows=1, sharex=\"row\", sharey=\"row\")\n",
"\n",
"fig.set_figwidth(20/3)\n",
"fig.set_figheight(6)\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[i]\n",
" \n",
" data = data_murn[data_murn.potential == get_clean_project_name(pot)]\n",
" \n",
" for j,(_, row) in enumerate(data.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.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.set_title(f\"{get_clean_project_name(pot)}\",fontsize=22)\n",
" ax.set_xlabel(\"Volume per atom, $\\mathrm{\\AA^3}$\",fontsize=20)\n",
" ax.set_ylabel(\"Energy per atom, eV/atom\",fontsize=20)\n",
" ax.tick_params(labelsize=18)\n",
" ax.legend(prop={\"size\":16})\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": {
"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>298</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>324</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>350</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.359978178265942, 2.5172345748814804, 1.7799536377360752], index=0), Atom('Li', [6.53996726740165, 3.7758518623203585, 2.669930456604318], index=1), Atom('Al', [-3.964456982410852e-1...</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>363</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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential ... n_atoms phase\n",
"0 298 LiAl_eam ... 1 Al_fcc\n",
"2 324 LiAl_eam ... 1 Li_bcc\n",
"4 350 LiAl_eam ... 4 Li2Al2_cubic\n",
"5 363 LiAl_eam ... 4 LiAl3_cubic\n",
"\n",
"[4 rows x 11 columns]"
]
},
"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: 414\n",
"The job s_e_0 was saved and received the ID: 415\n",
"The job s_01_e_m0_05000 was saved and received the ID: 416\n",
"The job s_01_e_m0_02500 was saved and received the ID: 417\n",
"The job s_01_e_0_02500 was saved and received the ID: 418\n",
"The job s_01_e_0_05000 was saved and received the ID: 419\n",
"The job s_08_e_m0_05000 was saved and received the ID: 420\n",
"The job s_08_e_m0_02500 was saved and received the ID: 421\n",
"The job s_08_e_0_02500 was saved and received the ID: 422\n",
"The job s_08_e_0_05000 was saved and received the ID: 423\n",
"The job s_23_e_m0_05000 was saved and received the ID: 424\n",
"The job s_23_e_m0_02500 was saved and received the ID: 425\n",
"The job s_23_e_0_02500 was saved and received the ID: 426\n",
"The job s_23_e_0_05000 was saved and received the ID: 427\n",
"The job phonopy_job_Al_fcc was saved and received the ID: 428\n",
"The job ref_job_Al_fcc_0 was saved and received the ID: 429\n",
"The job elastic_job_Li_bcc was saved and received the ID: 430\n",
"The job s_e_0 was saved and received the ID: 431\n",
"The job s_01_e_m0_05000 was saved and received the ID: 432\n",
"The job s_01_e_m0_02500 was saved and received the ID: 433\n",
"The job s_01_e_0_02500 was saved and received the ID: 434\n",
"The job s_01_e_0_05000 was saved and received the ID: 435\n",
"The job s_08_e_m0_05000 was saved and received the ID: 436\n",
"The job s_08_e_m0_02500 was saved and received the ID: 437\n",
"The job s_08_e_0_02500 was saved and received the ID: 438\n",
"The job s_08_e_0_05000 was saved and received the ID: 439\n",
"The job s_23_e_m0_05000 was saved and received the ID: 440\n",
"The job s_23_e_m0_02500 was saved and received the ID: 441\n",
"The job s_23_e_0_02500 was saved and received the ID: 442\n",
"The job s_23_e_0_05000 was saved and received the ID: 443\n",
"The job phonopy_job_Li_bcc was saved and received the ID: 444\n",
"The job ref_job_Li_bcc_0 was saved and received the ID: 445\n",
"The job elastic_job_Li2Al2_cubic was saved and received the ID: 446\n",
"The job s_e_0 was saved and received the ID: 447\n",
"The job s_01_e_m0_05000 was saved and received the ID: 448\n",
"The job s_01_e_m0_02500 was saved and received the ID: 449\n",
"The job s_01_e_0_02500 was saved and received the ID: 450\n",
"The job s_01_e_0_05000 was saved and received the ID: 451\n",
"The job s_08_e_m0_05000 was saved and received the ID: 452\n",
"The job s_08_e_m0_02500 was saved and received the ID: 453\n",
"The job s_08_e_0_02500 was saved and received the ID: 454\n",
"The job s_08_e_0_05000 was saved and received the ID: 455\n",
"The job s_23_e_m0_05000 was saved and received the ID: 456\n",
"The job s_23_e_m0_02500 was saved and received the ID: 457\n",
"The job s_23_e_0_02500 was saved and received the ID: 458\n",
"The job s_23_e_0_05000 was saved and received the ID: 459\n",
"The job phonopy_job_Li2Al2_cubic was saved and received the ID: 460\n",
"The job ref_job_Li2Al2_cubic_0 was saved and received the ID: 461\n",
"The job ref_job_Li2Al2_cubic_1 was saved and received the ID: 462\n",
"The job elastic_job_LiAl3_cubic was saved and received the ID: 463\n",
"The job s_e_0 was saved and received the ID: 464\n",
"The job s_01_e_m0_05000 was saved and received the ID: 465\n",
"The job s_01_e_m0_02500 was saved and received the ID: 466\n",
"The job s_01_e_0_02500 was saved and received the ID: 467\n",
"The job s_01_e_0_05000 was saved and received the ID: 468\n",
"The job s_08_e_m0_05000 was saved and received the ID: 469\n",
"The job s_08_e_m0_02500 was saved and received the ID: 470\n",
"The job s_08_e_0_02500 was saved and received the ID: 471\n",
"The job s_08_e_0_05000 was saved and received the ID: 472\n",
"The job s_23_e_m0_05000 was saved and received the ID: 473\n",
"The job s_23_e_m0_02500 was saved and received the ID: 474\n",
"The job s_23_e_0_02500 was saved and received the ID: 475\n",
"The job s_23_e_0_05000 was saved and received the ID: 476\n",
"The job phonopy_job_LiAl3_cubic was saved and received the ID: 477\n",
"The job ref_job_LiAl3_cubic_0 was saved and received the ID: 478\n",
"The job ref_job_LiAl3_cubic_1 was saved and received the ID: 479\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: 480\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bb9d01006694f59be188c5ec42858f9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading and filtering jobs: 0%| | 0/4 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6994f98ab95140fe94dad01da536cc32",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Processing jobs: 0%| | 0/4 [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>414</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>430</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>446</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>463</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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential C11 ... compound crystal_structure phase\n",
"0 414 LiAl_eam 120.339279 ... Al fcc Al_fcc\n",
"1 430 LiAl_eam 16.740018 ... Li bcc Li_bcc\n",
"2 446 LiAl_eam 179.464635 ... Li2Al2 cubic Li2Al2_cubic\n",
"3 463 LiAl_eam 65.443987 ... LiAl3 cubic LiAl3_cubic\n",
"\n",
"[4 rows x 8 columns]"
]
},
"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": 58,
"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\", 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",
" \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": 33,
"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\"].item()\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": 41,
"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>298</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>311</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 ... n_atoms phase\n",
"0 298 LiAl_eam ... 1 Al_fcc\n",
"1 311 LiAl_eam ... 1 Al_bcc\n",
"\n",
"[2 rows x 11 columns]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from collections import Counter\n",
"\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": 42,
"id": "b0bba971",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"</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>298</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>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>311</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>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>324</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>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>337</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>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>350</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.359978178265942, 2.5172345748814804, 1.7799536377360752], index=0), Atom('Li', [6.53996726740165, 3.7758518623203585, 2.669930456604318], index=1), Atom('Al', [-3.964456982410852e-1...</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>0.500000</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>363</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>0.750000</td>\n",
" <td>0.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>376</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.9874675377354745, 1.0099032069001204, 0.8188717268019969], index=0), Atom('Li', [3.1237856900347722, 1.4557299809760758, 2.6737242758835587], index=1), Atom('Li', [-3.44219970899484...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.023702</td>\n",
" <td>190.504371</td>\n",
" <td>53.125273</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>0.307692</td>\n",
" <td>0.692308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>389</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [2.1548001975659243, 1.2440753587819189, 1.8617841750008692], index=0), Atom('Al', [-2.154798282819334, 3.7322233132135576, 2.664676023808053], index=1), Atom('Li', [8.560563403365655e...</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>0.400000</td>\n",
" <td>0.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>490</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [2.1429671662366734, 1.237242669260901, 7.662122465542487], index=0), Atom('Li', [-8.827603092953495e-10, 2.4744853400282523, 0.5913662557792814], index=1), Atom('Li', [-8.827603092953...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.061226</td>\n",
" <td>131.389799</td>\n",
" <td>71.221356</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>0.500000</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential ... cAl cLi\n",
"0 298 LiAl_eam ... 1.000000 0.000000\n",
"1 311 LiAl_eam ... 1.000000 0.000000\n",
"2 324 LiAl_eam ... 0.000000 1.000000\n",
"3 337 LiAl_eam ... 0.000000 1.000000\n",
"4 350 LiAl_eam ... 0.500000 0.500000\n",
"5 363 LiAl_eam ... 0.750000 0.250000\n",
"6 376 LiAl_eam ... 0.307692 0.692308\n",
"7 389 LiAl_eam ... 0.400000 0.600000\n",
"8 490 LiAl_eam ... 0.500000 0.500000\n",
"\n",
"[9 rows x 16 columns]"
]
},
"execution_count": 42,
"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\"]\n",
"data_convexhull[\"cLi\"]=data_convexhull[\"n_Li\"]/data_convexhull[\"n_atoms\"]\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": 43,
"id": "3fccd2e0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-3.4830968311997523, -1.758107337432567]"
]
},
"execution_count": 43,
"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": 44,
"id": "43b89ed8",
"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",
" .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",
" <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>298</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>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>311</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>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.067785</td>\n",
" <td>0.067785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>363</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>0.750000</td>\n",
" <td>0.250000</td>\n",
" <td>-0.567192</td>\n",
" <td>-0.141798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>350</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.359978178265942, 2.5172345748814804, 1.7799536377360752], index=0), Atom('Li', [6.53996726740165, 3.7758518623203585, 2.669930456604318], index=1), Atom('Al', [-3.964456982410852e-1...</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>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>-0.591954</td>\n",
" <td>-0.147988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>490</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [2.1429671662366734, 1.237242669260901, 7.662122465542487], index=0), Atom('Li', [-8.827603092953495e-10, 2.4744853400282523, 0.5913662557792814], index=1), Atom('Li', [-8.827603092953...</td>\n",
" <td>Li4Al4</td>\n",
" <td>cubic</td>\n",
" <td>6.061226</td>\n",
" <td>131.389799</td>\n",
" <td>71.221356</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>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.458247</td>\n",
" <td>0.057281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>389</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Al', [2.1548001975659243, 1.2440753587819189, 1.8617841750008692], index=0), Atom('Al', [-2.154798282819334, 3.7322233132135576, 2.664676023808053], index=1), Atom('Li', [8.560563403365655e...</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>0.400000</td>\n",
" <td>0.600000</td>\n",
" <td>-0.173341</td>\n",
" <td>-0.034668</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>376</td>\n",
" <td>LiAl_eam</td>\n",
" <td>(Atom('Li', [4.9874675377354745, 1.0099032069001204, 0.8188717268019969], index=0), Atom('Li', [3.1237856900347722, 1.4557299809760758, 2.6737242758835587], index=1), Atom('Li', [-3.44219970899484...</td>\n",
" <td>Li9Al4</td>\n",
" <td>monoclinic</td>\n",
" <td>13.023702</td>\n",
" <td>190.504371</td>\n",
" <td>53.125273</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>0.307692</td>\n",
" <td>0.692308</td>\n",
" <td>0.785300</td>\n",
" <td>0.060408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>324</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>1.000000</td>\n",
" <td>0.001096</td>\n",
" <td>0.001096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>337</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>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential ... E_form E_form_per_atom\n",
"0 298 LiAl_eam ... 0.000000 0.000000\n",
"1 311 LiAl_eam ... 0.067785 0.067785\n",
"5 363 LiAl_eam ... -0.567192 -0.141798\n",
"4 350 LiAl_eam ... -0.591954 -0.147988\n",
"8 490 LiAl_eam ... 0.458247 0.057281\n",
"7 389 LiAl_eam ... -0.173341 -0.034668\n",
"6 376 LiAl_eam ... 0.785300 0.060408\n",
"2 324 LiAl_eam ... 0.001096 0.001096\n",
"3 337 LiAl_eam ... 0.000000 0.000000\n",
"\n",
"[9 rows x 18 columns]"
]
},
"execution_count": 44,
"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\"]\n",
"\n",
"data_convexhull = data_convexhull.sort_values(\"cLi\")\n",
"\n",
"data_convexhull"
]
},
{
"cell_type": "code",
"execution_count": 46,
"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": 56,
"id": "e12e56f8",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig,ax = plt.subplots(figsize=(8,6),constrained_layout=True)\n",
"\n",
"ax.plot(data_convexhull[\"cLi\"]*100, data_convexhull[\"E_form_per_atom\"]*1e3,\"o\",markersize=10)\n",
"\n",
"# ax.axhline(0,ls=\"--\",color=\"k\")\n",
"ax.plot(subset_covexhull[\"cLi\"]*100, subset_covexhull[\"E_form_per_atom\"]*1e3,\"--\",color=\"k\",markersize=20)\n",
"ax.legend()\n",
"ax.set_xlabel(\"Li,%\",fontsize=\"20\")\n",
"ax.set_ylabel(\"E$_f$, meV/atom\",fontsize=\"20\")\n",
"ax.tick_params(labelsize=20,axis=\"both\")\n",
"# plt.savefig(\"Convexhull\")\n",
"\n",
"for _,row in data_convexhull.iterrows():\n",
" plt.text((row[\"cLi\"]+0.01)*100,row[\"E_form_per_atom\"]*1e3,row[\"phase\"],size=13)\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 194.79580644700002 seconds\n"
]
}
],
"source": [
"time_stop = time.process_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": [
"3.246596774116667"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"194.79580644700002/60"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "workshop2",
"language": "python",
"name": "workshop2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}