{
"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": 38,
"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_yace</td>\n",
" <td>[/home/jovyan/workshop_preparation/potentials/03-ACE/AlLi-6gen-18May.yace]</td>\n",
" <td>ACE</td>\n",
" <td>[Al, Li]</td>\n",
" <td>[pair_style pace\\n, pair_coeff * * AlLi-6gen-18May.yace Al Li\\n]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name \\\n",
"0 LiAl_yace \n",
"\n",
" Filename \\\n",
"0 [/home/jovyan/workshop_preparation/potentials/03-ACE/AlLi-6gen-18May.yace] \n",
"\n",
" Model Species \\\n",
"0 ACE [Al, Li] \n",
"\n",
" Config \n",
"0 [pair_style pace\\n, pair_coeff * * AlLi-6gen-18May.yace Al Li\\n] "
]
},
"execution_count": 38,
"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[1]"
]
},
{
"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": [
"1654235381.0332775"
]
},
"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": [],
"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: 369\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:49:50,541 - 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: 370\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:49:52,046 - 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: 371\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:49:53,264 - 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: 372\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:49:54,515 - 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: 373\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:49:55,839 - 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: 374\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:49:56,853 - 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: 375\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:49:57,855 - 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: 376\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:49:58,873 - 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: 377\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:49:59,856 - pyiron_log - WARNING - The job murn_job_Li4Al4_Li4Al4_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 Al_fcc_relax was saved and received the ID: 378\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:50:00,873 - 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: 379\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:50:02,429 - 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: 380\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:50:03,809 - 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: 381\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:50:05,094 - 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: 382\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:50:06,519 - 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: 383\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:50:07,932 - 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: 384\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:50:09,689 - 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: 385\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:50:11,022 - 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: 386\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:50:12,362 - pyiron_log - WARNING - The job murn_job_Li4Al4_Li4Al4_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\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: 387\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f6145b8d289b4754925a8c982d70b33c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading and filtering jobs: 0%| | 0/18 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6321f124ea254452be5db86c7cca021f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Processing jobs: 0%| | 0/18 [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>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>10</th>\n",
" <td>132</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>11</th>\n",
" <td>145</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>12</th>\n",
" <td>158</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>13</th>\n",
" <td>171</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>14</th>\n",
" <td>184</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>15</th>\n",
" <td>197</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>16</th>\n",
" <td>210</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>17</th>\n",
" <td>223</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 LiAl_yace \n",
"10 132 LiAl_yace \n",
"11 145 LiAl_yace \n",
"12 158 LiAl_yace \n",
"13 171 LiAl_yace \n",
"14 184 LiAl_yace \n",
"15 197 LiAl_yace \n",
"16 210 LiAl_yace \n",
"17 223 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', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e... \n",
"14 (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",
"15 (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",
"16 (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",
"17 (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 4.044553 16.541594 87.130427 -3.478909 \n",
"10 Al bcc 3.953036 16.811334 72.667242 -3.388831 \n",
"11 Li bcc 4.216389 20.403222 15.823747 -1.756104 \n",
"12 Li fcc 4.331457 20.318983 14.231625 -1.755594 \n",
"13 Li2Al2 cubic 6.367064 64.521799 46.107162 -11.185880 \n",
"14 LiAl3 cubic 5.686989 65.028366 66.254925 -12.569153 \n",
"15 Li9Al4 monoclinic 13.519944 213.136118 33.963240 -31.796316 \n",
"16 Li3Al2 trigonal 6.299181 80.375104 39.643133 -13.138303 \n",
"17 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 "
]
},
"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": 11,
"id": "30d27d75",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x432 with 2 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)\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",
" \n",
" ax.tick_params(labelsize=18)\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": 12,
"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>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>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>11</th>\n",
" <td>145</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>13</th>\n",
" <td>171</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>14</th>\n",
" <td>184</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 LiAl_yace \n",
"11 145 LiAl_yace \n",
"13 171 LiAl_yace \n",
"14 184 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', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e... \n",
"14 (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 4.044553 16.541594 87.130427 -3.478909 \n",
"11 Li bcc 4.216389 20.403222 15.823747 -1.756104 \n",
"13 Li2Al2 cubic 6.367064 64.521799 46.107162 -11.185880 \n",
"14 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 "
]
},
"execution_count": 12,
"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": 13,
"id": "0d1c799c-f10b-462d-aaea-253cee4b4b3e",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LiAl_eam\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:50:49,211 - pyiron_log - WARNING - The job elastic_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:49,387 - pyiron_log - WARNING - The job phonopy_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:49,678 - pyiron_log - WARNING - The job elastic_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:49,843 - pyiron_log - WARNING - The job phonopy_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:50,138 - pyiron_log - WARNING - The job elastic_job_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:50,310 - pyiron_log - WARNING - The job phonopy_job_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:50,609 - pyiron_log - WARNING - The job elastic_job_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:50,777 - pyiron_log - WARNING - The job phonopy_job_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": [
"LiAl_yace\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-03 05:50:51,076 - pyiron_log - WARNING - The job elastic_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:51,238 - pyiron_log - WARNING - The job phonopy_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:51,530 - pyiron_log - WARNING - The job elastic_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:51,690 - pyiron_log - WARNING - The job phonopy_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:51,981 - pyiron_log - WARNING - The job elastic_job_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:52,151 - pyiron_log - WARNING - The job phonopy_job_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:52,451 - pyiron_log - WARNING - The job elastic_job_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
"2022-06-03 05:50:52,619 - pyiron_log - WARNING - The job phonopy_job_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\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": 14,
"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": 15,
"id": "ba95973a-a00f-41a9-b23f-2b4bcf629aaf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job table_elastic was saved and received the ID: 388\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "afb9083c93bf4c2e9edeb7ef795c7a82",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading and filtering jobs: 0%| | 0/8 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7c012cfc119d4ef78aa6d7887b091aaf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Processing jobs: 0%| | 0/8 [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>236</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>252</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>268</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>285</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>302</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>5</th>\n",
" <td>318</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>6</th>\n",
" <td>334</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>7</th>\n",
" <td>351</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",
"0 236 LiAl_eam 120.339279 66.483631 45.515458 Al \n",
"1 252 LiAl_eam 16.740018 11.018163 12.688217 Li \n",
"2 268 LiAl_eam 179.464635 54.231219 47.889040 Li2Al2 \n",
"3 285 LiAl_eam 65.443987 47.601166 28.002138 LiAl3 \n",
"4 302 LiAl_yace 133.807535 62.693651 40.423203 Al \n",
"5 318 LiAl_yace 18.307762 13.775557 12.106574 Li \n",
"6 334 LiAl_yace 114.275413 13.925574 42.537995 Li2Al2 \n",
"7 351 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 "
]
},
"execution_count": 15,
"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": 16,
"id": "b317b1d3-549b-4e0e-84bf-3cd02a92596d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABJMAAAFiCAYAAABChZmfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAADCSklEQVR4nOzddZhc1fkH8O+Z2Vm3yO7GnRBIIEKQ4G7FpZRCkZaihVKhAu0PKy0FWtoixSFIcYqTAEUDCcSJu2+Szbrv2Pn9cWd25lybOzu28v08T57dK3PnzO7em7nvvO97hJQSRERERERERERETrgyPQAiIiIiIiIiIuo5GEwiIiIiIiIiIiLHGEwiIiIiIiIiIiLHGEwiIiIiIiIiIiLHGEwiIiIiIiIiIiLHGEwiIiIiIiIiIiLHGEyilBFCuIQQW4UQUghRJYTw2Oy7ObTfqASf8wghxEdCiDohRDB0zLMSOSYRdU9R142jHez7TGjfyxJ4voSPQUTxS8a5LoQYIYS4WgjxXyHEaiFEqxCiSQixSAjxf0KIYodjuTJ0fCmEuDHGvpeF9ntGt75ECPF9IcTTQoglofcsbUKIdUKIfwshxjkZS3cghDg69Bo/i/Nxo0KP25yakRGlRireeyTpulIshPiTEOJ9IcRGIUSjEMIrhNgmhHhZCHF4zBfXTfC60nMwmESpdCKA4aHvywCcnsonE0IMBfAOgOMAfAfgeQAzAWxN5fMSERFRt/cfAP8GcBqARgBvA5gLYCyA2wEsFUKMdnCcH1t8H4+bALwM4DIAeQA+ATAbQC6AqwF8J4T4XhePTUQ9TzKuK+UAbgFwOIBdAD4C8C6AZgDfB/ClEOLXiQySSC8r0wOgXi18MdwBYGho+Y0UPt+JAEoA/EdKeVEKn4eIep7fA7gbwM5MD4SIUsrqXN8B4BcAnpNS1oRXCiHKALwC4GgAzwA4yurAQoh9ABwMoAWAH8B+QojpUsoFcY6xFcDfATwipVwXdXwPgL+Gxvm8EGKslLI2zmP3FDsA7APAl+mBEKVQzPceSbyu7AJwCIAFUsqA7jkuAPACgLuFEG9KKdfHeeyegteVNGNmEqWEEKI/gDMASAA/ABAAcLIQYkgKnzacBbXOdi8i6nOklDullKullA2ZHgsRpY7VuS6lvEBK+Y/oQFJo/R4APwotHimEGA5rPwl9fRXAS6Hv484ikFL+WUr5q+hAUmi9D8CvAawFUAqg12YnSSl9od/ThkyPhShVHL73SNZ1pVlK+Y0+kBTa9jKAzwG4ARwb77F7Cl5X0o/BJEqViwHkAPhMSjkHwIfQLmCXJPuJwrXD0NLUAeDWqLrjz3T7DhBC3CGEWByqJW4J9Sh4RghxqMmxC4QQvxZCzBVC1Id6GmwUQrwqhDg12a+FiFIj2f2OhBBThBBvCiGqQ71XFgohLo/xmJOEEG8IISpDfQx2CSG+EkL8VgiRZ7L/wUKIF4QQW4QQHaHnWiCEuF0IMSAZr4Oot+nKuS6l3A6gOrQ4zOK4WdDe2wDA0wCeCn1/oRAit4vDNRtLEFqpvuVY4iGE2EcI8ZgQYn3oPUydEOI7IcR9QoiRUfuZ9mGJ2h6zh0noPdPdofdJHaFeLQ+YXa9i9Tbh+y/qDWJdj9J1XQnxh762J3ogXlcojGVulCrhm6pnQl+fBnBKaP3dSX6u9dB6I00BMBnAUgBLQttWh3cSQkwF8B6AwQBqAXwG7YI6EsCFod2+jtp/JLQeBntDqzeeA6ABWgbUKdD6QL2f5NdCRN3fwdB6r+yA1pOgHFppzFNCiKlSyhuidxZCCAAPQ+uFAgALoH1C2B9aOvbd0PqnbI56zO8B3AVAAFgBrbdLEYDxAP4PwKfQrmFElCAhxEAA/UKLVuUopwGoALARwJdSSimEWAlgXwDnQOvJlCx7xRiLI0KISwA8DiAb2rjfBeABMA7ArwAsR+R9WqKyAfwPwCRoPaAWQbsu/gzASUKII6SUux2Om++/qK9Iy3VFCHEKgGMAtEE7PxM5Fq8r1InBJEq6UNBmCoAmAK+FVr8FoAbAeCHE4aFspaQIHWuOEOI2aMGkN6WUt+nGVASt2eZgAI8A+KWUsi1qexm0i0t42QXgv6F1bwG4XEpZpzveQcl6DUTUo1wN4F/QriMBQMsighZYul4IMUtKGf2G5MbQY3YDOEtKOS+8IRRoOhpA9PXlbAB/hvZm54dSynein1wIcSDY+4komX4NLXt6kZRys8U+4bKTZ6SUMvT90wDuDW1L1k3fydDey7QB+CCB4xwI4EloAekrADwVNe5wn5ZkmgGtPG9vKeWO0HMUQXsvdRyAB6A1AY41br7/or4kJdcVIcRfoQWp8qF9CDUZ2n3ZpaFMzC7hdYX0WOZGqRCu/X1FStkKAFJKLyIXxK7OUpDomIYBmAfg2uhAEqD1TNAFuM4AMBVapsCF0Rec0P5NUsr/pXbIRNRNVQL4TXRfAinlNwDuDy3+Irw+lMJ+c2jxsuhAUuhxUkr5qa6fwq2hrzfpA0mhx8xP5M0gEUUIIY6HFkwKQvtU3WyfCmifXEtomdBhz0ErHTlWCDEqCWMZAu1GDQD+4vQTdwu3QPvQ+D4p5ZPRN3wAIKVcJaVclcDxzfwqfMMXeo4maIH0AIBzhX0/qjC+/6I+IcXXlXMBXArgfGiBpGpoAZT/dnnAGl5XSMFgEiWVECIHkZKxp3Wbw8vnCyEK0zcqAMDJoa+GC1+M/V/QB56IqM97VUrZYbL+udDXw0NBJACYDmAggO1SylmxDiyEGATtjZ8PwLPJGCwRmRNC7Aet6a0bwP9JKT+z2PVSaDdQ/5NSbg2vDAV73of2Kf1lCY6lGFq5yBBopRh3JXAsN4DjQ4tPJDKuONRLKd/VrwzNGjUP2j3HkQ6Ow/df1Fek7LoipRwnpRTQyncPBfAVgNeEEC+Grg9x43WFzDCYRMl2NrQ+IOuklF9Fb5BSLobWy6gQDlISkyzcDG617V5d35+I+o5NFuu3QstuyAUQbgwZvpascXjs8P5bw5mdRJR8QogJAD6GNmva36SUdsGby0Jf9R+SRa+7LFS22pWxFEIraZsK4EsA54QacXfVQAAFAPxpnAJ8s4NtThqK8/0X9RWXhb6m5LoCAFLKeinlXCnlWQDegTbD9nVdPByvK2TAnkmUbOESthIhhFlfpIqo/Z4y2U5E1Bs4yYAkogwQQoyH1sy1HMDDUspf2+x7KLRG+QDwCyHEtbpdwu+lR0Lr4fFxnGMpgDY5yKEAvgHwvW4aSE7GB9C8LhIh9dcVCzMBnA6tBO5fSTheMvC60sMxmERJE6pZPS60WB76Z+UwIcR4KeXa1I8MALAFwARojdecNP/eEvq6t+1eRNQXjbJYPwLaG6N2aDNGAvFfS8L7DxdC5DEdmyi5hBB7QZsNcTC0GYl+FuMh0X0epzvY1/FNnxAiH1pp25HQZnk8OdQPJFHVAFoB5AshxkopNzh4jDf01aoNwUiL9WGjHGyrdDAOvv+iviBl1xUbe0Jf7e7P7PC6QgYsc6Nkuhza39T/pJTC6h+0/gRAehtxzw4/p8N00fD+FwshclM0JiLqmc4XQmSbrL8o9PUrKaU/9P1CaG/AhgkhTop1YCnlLgDfQZsO95JkDJaINEKIsdACSUOglZFcZddHMZQ1FC7LP8Lmfc3E0D5nCyFKHY4lD1og6WgAiwGcKKWs78LLMghNDhC++bzC4cPCDW4nWGw/NcbjS4UQhn2EEGMAHAIte+ALB+Pg+y/q1VJ5XYnh2NDXdV15MK8rZIbBJEqKUIDm0tDic3b7Rm2/pKtN4LrgCWiR60MBPKC/kAghyoQQh0etegtaf6dRAF4QQpTo9i8SQhwHIuqLhgK4OzTVLIDO6XJ/GVr8Z3i9lNIH4C+hxaeFEMrUs0JztO4ac3vo670Wb6KmCyGc9AggohAhxGhogaSh0Mo9rnAwIcf5AIqg9Un7ymonKeVKAIug9Uv7oYOx5AJ4G8Ax0N5rHK+fXSgJ7oI229GvhRCXmYxhQqhvVNh8aFOHTxRCXKjb91oA5zl4zr8JIQZHPa4QwL+hNTj/b3STYRt8/0W9XaquK5cIIY4xWe8WQlwC4LehVY93ZdAhvK6QgmVulCzHABgDLf3xjRj7zoL2Sf1gaFNiGrr0J5uUskkIcSa0vgTXAfiBEOIraOUoI6E1vXwRoRI4KWVQCHEOgA8BnAPghFAPqAYAwwFMgZaSzmkkiTLvYSFEo832s5P8fI8AuBbA6UKIBQDKABwF7f/Uh6WU7+j2vx9ab4QrAMwLPWY9tMkK9oV2TRkN7foCKeUbQohboQWV3hNCLAOwAtqbz70BjIN2zd2e5NdF1N0lcq6/Du1c64D2YepTFonKd0spw01awxnUzzsIPD0HYFroMQ/H2PfPiMyKtB3A3y3G8qaU8s0YxzIlpfxWCHElgEehBbL/AC1TMgfAWACToGWUrw7t3yqEuAPAvdButq4DsAvAftCuT/cA+I3NU86FdnO3VgjxCbTylqOgXR83wGHTX77/oh6kq9ejVF1XjgVwqRBiO4ClAOqhnX8ToQXRgwBuM3mP4hivK6THYBIly+Whr2/FqveXUvqEEC9B61PwY6QhmBR63gWhaYB/Aa0B3QnQLqyVAP4D7cIYvf8mIcQ0ANdDa1Z3BLQL2q7QmM1mXyCi9NsnxvacJD/fN9A+2bsdwEkA8gAsg/ZG70n9zqE3iz8VQrwF4GoAB0F741ILLd38AWjXlejH3BF643QDgMOhXYMaoH2SeRu0UjiiviaRc71/1D4/stnvGQCrQyVxR4TWPe9gbC9Cu2E6QAixv5TS7hztH/X9aTb7bQbwpoPnNiWlfEoIMR9a1uSxAM4C0AJt5sl7oTUhj97/PiFELbTrznQAbdBu5n4EIB/2N31eAN+Ddl08F1op4R4AD0G7ga2OY9x8/0U9QdzXoxRfVx4H0AhgBrTztz+083IrgPcBPCKlXOTgOW3xukLRROyAKBERERERERERkYY9k4iIiIiIiIiIyDEGk4iIiIiIiIiIyDH2TKJuSQjxTBy7PyGlnJOqsRBR7yOE+B2sp6rVmyOlfCKV4yEisiKEOAtaXxInqqWUv07daIioN+B1hZKBwSTqri6NY9/PEJqFjYjIoZOhzQjiFINJRJQpU+D8fdEWALzpI6JYpoDXFUoQG3ATEREREREREZFjPT4zaeDAgXLUqFGZHgYRdQMLFy6sllKWpfp5eN0hIoDXHCJKP153iCid7K45PT6YNGrUKCxYsCDTwyCibkAIsSUdz8PrDhEBvOYQUfrxukNE6WR3zeFsbkRERERERERE5BiDSURERERERERE5BiDSURERERERERE5BiDSURERERERERE5BiDSURERERERERE5BiDSURERERERERE5FhWpgdAZKexsRFVVVXw+XyZHgplkMfjQXl5OYqLizM9FCIiIiIioj6PwSTqthobG7F7924MHToUeXl5EEJkekiUAVJKtLW1YceOHQDAgBIREREREVGGscyNuq2qqioMHToU+fn5DCT1YUII5OfnY+jQoaiqqsr0cIiIiIiIqDvYvgCYfQuw/I1Mj6RPYmYSdVs+nw95eXmZHgZ1E3l5eSx3JCIiIiIioH4b8NRJQNCvLbuygH3PyOyY+hhmJlG3xowkCuPfAhERERERAQC+vC8SSAKAVy/N3Fj6KAaTiIiIiIiIiKjn2LNWXZbBzIyjD2MwiYiIiIiIiIh6Dpc70yPo8xhMIkqjZ555BkIIrF+/3nT7ZZddhlGjRplumzNnDoQQqKiogN/vN2zfvHkzhBB45plnkjhiIiIiIiKibsbtyfQI+jwGk4i6kT/+8Y/473//a7pt5syZALRZ7j744IN0DouIiIiIiKj7cHEusUxjMImoGxk7diymTp1qWN/W1oZXX30VRx99NPLz8zsDS0RERERERH2Oi5lJmcZgElE3YlXm9uabb6KhoQHXXnstzj77bLz77ruoq6tLynMuXboUZ5xxBvr164e8vDwcdthh+PLLL5V95s+fj/POOw/Dhg1DXl4e9t57b9x8881oa2tT9jv66KNx+OGHY9asWZgyZQry8vIwdepUfPPNN/D7/bj55psxePBg9O/fH5dddhlaWlqS8hqIiIiIiKgPYc+kjGNuGPUoo373XqaHoNh89/fS8jwzZ85EaWkpzjjjDJSUlOCFF17ASy+9hGuuuSah4y5atAhHHHEEpk6discffxz5+fl45JFHcPzxx+Prr7/GAQccAADYunUrpkyZgssuuwxFRUVYsWIF7rjjDmzcuBEvvfSScsz169fjpptuwi233ILCwkL85je/wRlnnIEzzjgDfr8fzzzzDFatWoWbbroJ5eXluOeeexJ6DURERERE1MewZ1LGMZhE1M1VVlbi448/xk9+8hPk5OTg+OOPx9ChQzFz5syEg0k33XQTRowYgU8++QTZ2dkAgJNOOgmTJk3CnXfeiTfffBMAcO6553Y+RkqJww47DMXFxbjkkkvw0EMPYcCAAZ3ba2pq8PXXX2PMmDEAgGAwiDPPPBObNm3Cxx9/3PkcX3zxBV599VUGk4iIiIiIKD4sc8s4lrkRdXPPP/88AoEALrnkEgCAy+XCxRdfjG+++QZr1qzp8nHb2trw+eef4/zzz4fL5YLf74ff74eUEscffzy++OKLzn0bGxvx29/+FmPHjkVOTg48Hg9+9KMfQUqJdevWKccdP358ZyAJACZMmABACyBFmzBhArZv3w4pZZdfAxERERER9UFu5sVkGoNJRN3cs88+ixEjRmDixImor69HfX09zjzzzM5tXVVbW4tAIIA777wTHo9H+ffggw+irq4OwWAQAHD55ZfjkUcewQ033ICPPvoI8+fPx0MPPQQAaG9vV47br18/ZTmc8WS23u/3IxAIdPk1EBERERFRH8TZ3DKOvwHqUdLVo6i7WLBgAVasWAHAGIwBgOeeew533nknXK7448KlpaVwuVy47rrrOrOe9FwuF9rb2/HWW2/htttuw89//vPObcuWLYv7OYmIiIiIiBLGMreMYzCJqBubOXMmhBB47bXX0L9/f2Xb7Nmzcffdd+Ozzz7DscceG/exCwoKcMQRR2Dp0qWYNm2aZUCqo6MDgUAAHo96wX7mmWfifk4iIiIiIqKEMTMp4/gbIMqAWbNmYdCgQcq6kpISZdnn8+Gll17CUUcdhXPOOcdwjClTpuAf//gHZs6c2aVgEgD8/e9/x5FHHomTTjoJP/nJTzB48GBUV1dj0aJFCAQCuPvuu1FSUoJDDjkEf/vb3zB48GAMHDgQTz31FHbs2NGl5yQiIiIiIkoIeyZlHH8DRBlw/fXXG9ZNnDgR06dP71x+9913UV1djR//+MemxygtLcU555yD119/vbN/UbymTZuG+fPn4/bbb8cNN9yAhoYGlJWVYdq0abj66qs793vxxRdxzTXX4LrrrkNeXh6+//3v45///CdOO+20Lj0vERERERFRlzEzKeNET59Jafr06XLBggWZHgalwKpVq7DPPvtkehjUjcT6mxBCLJRSTrfcIUl43SEigNccIko/XneIQj65C/jiHnXdbQ2ZGUsvZnfN4WxuRERERERERNRzmGUm9fBEmZ6GuWFEvZCUEoFAwHafrCye/kRERERE1AMJYVwXDLCXUhoxM4moF/r888/h8Xhs/23evDnTwyQiIiIiIoqfWRZSwJv+cfRhDNsR9UIHHHAA5s+fb7vPkCFD0jQaIiIiIiKiJJJB47qAF0B+2ofSV2U8mCSEOBrApyabGqSUpWkdDFEvUVRUpMwMR0RERERE1HuYZSb50j+MPizjwaQoNwCITqXwZ2ogRERERERERNRNmWUm+dvTP44+rDsFk1ZJKedlehBERERERERE1I2Z9Ux6+lTg+oVAVnb6x9MHsQE3EREREREREfUcZplJDVuBdbPTP5Y+qjsFk14QQgSEEDVCiP8IIUZkekBERERERERE1N2YZCYBwLePp3cYfVh3KHNrAPA3AJ8DaAQwFcDNAOYKIaZKKasyOTgiIiIiIiIi6kbMMpMAwM0St3TJeDBJSrkYwOKoVZ8LIb4A8C20ptx/0D9GCHElgCsBYMQIJjARUerxukNE6cRrDhGlG6871KOY9UwCgKyc9I6jD+tOZW6dpJSLAKwFcKDF9seklNOllNPLysrSOziiBDzzzDMQQmD9+vWm2y+77DKMGjXKdNucOXMghEBFRQX8fuNkh5s3b4YQAs8880wSR0xhvO4QUTrxmkNE6cbrDvUozEzKuG4ZTAoRsCyEJOqd/vjHP+K///2v6baZM2cCAKqqqvDBBx+kc1hERERERETdHzOT0qZbBpOEENMBjAfwTabHQpROY8eOxdSpUw3r29ra8Oqrr+Loo49Gfn5+Z2CJiIiIiIioz7HMTPKkdxx9WMaDSUKIF4QQfxJCnCOEOFYI8SsAswDsAPBAhodHlFZWZW5vvvkmGhoacO211+Lss8/Gu+++i7q6uoSe67XXXoMQAkuXLjVsO/roozFjxozO5QcffBAzZsxA//79UVpaikMOOQTvvfee4XEtLS343e9+h7FjxyInJweDBg3Cueeei927d3fus2nTJlx00UUoKytDTk4OpkyZYpmNRUREREREZGDVMylgbAdCqZHxBtwAlgO4EMD1APIB7ALwBoBbpZTVmRwYdUO3lWR6BKrbGtLyNDNnzkRpaSnOOOMMlJSU4IUXXsBLL72Ea665psvHPOusszBkyBA8+uijePjhhzvXr1mzBp9//jmefvrpznWbN2/GFVdcgVGjRsHv9+Odd97Baaedhvfffx+nnHIKAMDr9eKEE07AkiVL8Pvf/x6HHHIIGhoaMHv2bNTV1aGiogLbtm3DwQcfjPLyctx///0oKyvDyy+/jHPPPRdvvvkmzjjjjK7/kIiIiIiIqG+wykzytaZ3HH1YxoNJUsq/APhLpsdB1F1VVlbi448/xk9+8hPk5OTg+OOPx9ChQzFz5syEgklZWVn46U9/ivvvvx/33nsvCgoKAACPPvooSktLccEFF3Tue99993V+HwwGcdxxx2Ht2rV45JFHOoNJzz//PObOnYu33npLCQqdd955nd/fdtttkFLi888/x4ABAwAAJ510ErZt24b/+7//YzCJiIiIiIgcsMhM8rWldxh9WMbL3IjI3vPPP49AIIBLLrkEAOByuXDxxRfjm2++wZo1axI69pVXXonW1la8+OKLAID29nbMnDkTl1xyCfLy8jr3W7hwIU477TRUVFQgKysLHo8HH330kfL8H374IQYNGmQbEJo1axZOPfVUlJSUwO/3d/476aSTsHTpUjQ2Nib0eoiIiIiIqA+wykzyM5iULgwmEXVzzz77LEaMGIGJEyeivr4e9fX1OPPMMzu3JWLIkCE488wz8cgjjwAAXn31VdTW1uKqq67q3Gfbtm047rjjUFtbiwceeABff/015s+fj5NPPhnt7e2d+9XU1GDo0KG2z1dVVYVnn30WHo9H+XfTTTd1HoOIiIiIiMiWVc8kZialTcbL3IjikqYeRd3FggULsGLFCgBAv379DNufe+453HnnnXC5uh4Xvvbaa3Hcccdh4cKFePTRR3HEEUdg33337dw+a9YsNDQ04JVXXsGwYcM617e2qvXIAwcOxPLly22fa8CAATjiiCPw29/+1nT7kCFDuvw6iIiIiIioj7DsmdRuvp6SjsEkom5s5syZEELgtddeQ//+/ZVts2fPxt13343PPvsMxx57bJef49hjj8U+++yDX/7yl/jqq6/wwgsvKNvDQSOPJzLN5tq1a/HVV18pwaUTTzwRL730Et555x2cfvrpps918sknY+7cuZg4caJSRkdEREREROScVWYSG3CnC4NJRBkwa9YsDBo0SFlXUqLOVOfz+fDSSy/hqKOOwjnnnGM4xpQpU/CPf/wDM2fOTCiYBABXX301fv7zn2PgwIE499xzlW3HH388srKycMkll+BXv/oVdu7ciVtvvRUjRoxAMBj5RODiiy/G448/jgsvvBC///3vcfDBB6OpqQmzZ8/GjTfeiAkTJuCOO+7AQQcdhCOPPBI/+9nPMGrUKNTV1WH58uXYuHEjnnrqqYReBxERERER9QFWZW5+ZialC4NJRBlw/fXXG9ZNnDgR06dP71x+9913UV1djR//+MemxygtLcU555yD119/HQ899FBC4zn//PPx85//HJdddhlycnIM43rhhRc6Z1sbO3Ys7r77bsyaNQufffZZ534ejwcffvghbr/9djz22GO4/fbbMWDAABx22GGdWVUjRozAggULcNttt+Hmm2/Gnj17MGDAAEyaNAmXXnppQq+BiIiIiIj6CMsyN2YmpYuQVhG9HmL69OlywYIFmR4GpcCqVauwzz77ZHoYfcLjjz+Oq666CmvXrsW4ceMyPRxLsf4mhBALpZTTLXdIEl53iAjgNYeI0o/XHbJVswFY8gJQvi+w33mZHk1qvXUdsPh54/qsPOAPu9I/nl7K7prDzCSiPmzlypXYsGEDbr31Vpx11lndOpBEREREREQWOpqAx44GOhq15fZ64MArMjmi1LIsc2vTtgmR3vH0QV2fAoqIui0pJfx+v+0/QJvJ7dxzz8X48ePx4IMPZnjURERERETUJRs/jwSSAOC9XwF+b+bGk2p2FVbsm5QWzEwi6oU+//xzHHPMMbb7bNq0Sel5REREREREPVTTTuO6VW/33nI3q55JAOBrAzycOTrVGEwi6oUOOOAAzJ8/33afIUOGpGk0RERERESUUt4W47pt3/beYBJsMpN8bekbRh/GYBJRL1RUVKTMDEdERERERL1Ye71xXW+e2SxWZhKlHHsmEREREREREfVkbXXGdf6O9I8jXex6JvXmIFo3wmASdWvS7iJBfQr/FoiIiIiILJgGk3pxho5dZhIbcKcFg0nUbXk8HrS19eILIMWlra0NHo8n08MgIiIiIup+2uqN63pzZpJdzyQGk9KCwSTqtsrLy7Fjxw60trYyK6UPk1KitbUVO3bsQHl5eaaHQ0RERETU/ZhmJvXioIpdZpLdNkoaNuCmbqu4uBgAUFlZCZ/Pl+HRUCZ5PB5UVFR0/k0QEREREVEUs8wkX28OJtkkGzCYlBYMJlG3VlxczAACERERERGRHbPZ3JiZRCnEMjciIiIiIkqf6nXAqnfMy3KIKH4BH9DRaFzfq3sm2WCLlLRgZhIREREREaXHxs+AZ88CIIH+Y4FrvgI8eRkeFFEP195gvp6ZSZRCzEwiIiIiIqL0ePFCdM7CVLsB2PJ1RodD1CtYZfn16mASeyZlGoNJRERERESUev4OwNeqrtu1LDNjIepNOprM1/fmYBIYTMo0BpOIiIiIiCj1tn1rXFcyLP3jIOptrHoj9eaeSSxzyzgGk4iIiIiIKPU2fZHpERD1TgGrYFJ7721GzTK3jGMwiYiIiIiIUq9hu3Edb/qIEmeXgdRbs5OYmZRxDCYREREREVHq+VqM63jTR5Q4u95IvbZvEjOTMo3BJCIiIiIiSj0vg0lEKcHMJN22Xlra180wmERERERERKnnbTWuCwbSPw6i3sY2mNSWvnGkE3smZRyDSURERERElHreZuM63vQRJc62zK0vZibxupIODCYREREREVHq+Uwyk3jTR5Q428yk3tozyQavK2nBYBIREREREaUeeyYRpQYzk5xvo6TplsEkIcQsIYQUQvwp02MhIiIiIqIkMOuZxJs+osQFvNbbfOyZRKnR7YJJQogLAUzO9DiIiIiIiChJpLTomcRZl4gSxswk59soabpVMEkIUQrgfgC/zPBQiIiIiIgoWQJeQJrM3MabPqLE9cmeScxMyrRuFUwCcA+AFVLKFzM9ECIiIiIiShKzfkmAeYCJiOJjG0xiZhKlRlamBxAmhDgcwCVgiRsRERERUe9iGUziTR9RwmyDSX2xZxLLZ9OhW2QmCSE8AB4FcJ+Uck2mx0NEREREREnEYBJR6rBnkvNtlDTdIpgE4LcA8gDc5WRnIcSVQogFQogFe/bsSe3IiIjA6w4RpRevOdTr+BhM6u543enB2DNJt4nXlXTIeDBJCDECwC0A/gggRwhRGmrEjahld/RjpJSPSSmnSymnl5WVpXnERNQX8bpDROnEaw6lXTAAfHEf8OxZwLePJ79MhJlJ3R6vOz1YwCaYFPClbxzpxMykjOsOPZPGAMgF8LzJtl+H/k0FsCSNYyIiIiIi6jtWvQ18cqf2/cZPgUH7AyMOTt7xva3m63nTR5Q4u8ykYC9tcm/bM4nXlXTIeGYStCDRMSb/AC3AdAyA9RkZGRERERFRX/D6T9Xl93+d3ON7m83XB3nTR5Qwu1K2YC/NTGKZW8ZlPDNJSlkP4DP9eiEEAGyRUhq2ERERERFREulvOJt2Jff4PmYmEaWMbTDJn75xpBMzkzKuO2QmERERERFRd5LsmzH2TCJKHb/Xeluv7ZnEYFKmddtgkpRSSCn/kOlxEBERERH1OQwmEfUcdplJi58HOizKTHsyNuDOuG4bTCIiIiIiogxhMImo57BrwN1WCzx1UvJnaMw4u8yk3vZauycGk4iIiIiISCfJN2MMJhGlTsAmmAQAu5cDGz5Jz1jShZlJGcdgEhERERERqZI9y5rPKpjUS6ctJ0onu8yksKpVqR9HOrFnUsYxmERERERERKq0lbmxHIUoIVLa90wK622zujEzKeMYTCIiIiIiIp1kl7m1WjwNb/qIEhL0OzuPelswybZnEq8r6cBgEhERERERqYJJLj9jzySi1HBS4gYk/5zONGYmZVxWpgdARERERETdTLJvxix7JvGmjyghAa+z/Xpbf7I4eya1ev14+qvNkFLissNGozCHoZBE8SdIREREREQ6nM2NqEdwmnHU28rc4gwm3fjSEny4cjcAYMGWOjxz+UGpGlmfwTI3IiIiIiJSJb0Bt0XPpN5WekOUbk4zjnrduWYXTFK3BYOyM5AEAJ+t2QN/gIHsRDGYREREREREqqQHk5rT8zxEfU2fzUxy3jOp2Wt87f4gZ5JMFINJRERERESUWj7O5kaUEn01MymOMrfGNp9hl6Dd48kRBpOIiIiIiCh1Aj7rJsG8oSNKjNOAbK9rwO08M6mp3ZiZFGBmUsIYTCIiIiIi6uuEO3XHtmq+DTAziShRTjOOnM761mMkmJnES0/CGEwiIiIiIurrhEjdsRlMIkodp+eQrz2140i3OMrcTDOTmBWZMAaTiIiIiIj6OpHC2wKrfkkAg0lEiXKameRvS+040i2OMrfGdmNmEsvcEsdgEhERERFRX5fKYJLVTG5A7+vjQpRuTgOy/o7UjiPtEstMYgPuxDGYRERERETU16UqmCQlsGu5zXZmJhElRBeQ9SLLfD9fH85MMumZxMykxFn8pRERERERUZ+RimCSlMB/vg+s+9BmHwaTiBKiK3PzwYNsGDNx4O9LPZPUbSxzSw1mJhERERER9XWpCCZtnmMfSAIYTCJKlC4zqQMe8/16XWZSgg24GUxKGINJRERERER9XSpmc1v2Sux92LeEKDG6Oe69VsGk3paZFEfPJNPMJF57EsZgEhERERFRX5eSnkkOAlTMTCJKjO4c8kqrnkm9LJgUR88k0wbczExKGINJRERERER9Xgoyk5wEqJxOa05E5pyWufn7bpmbaQNuZiYljMEkIiIiIqK+LhWZSU5K55iZRJQYXUC2g5lJ7JmUJgwmERERERGRUaJZQ0GTGaX0GEwiSowuM8lrNWE7eyYpgrz0JIzBJCIiIiKivk6aBI4CxhuwuLQ3OHhe3tERJUTfM8mqzC3o611lpQ4zk4JBicY2Y2Dbz2hSwhhMIiIiIiLq68xuMgPexI7JYBJR6ulnc7MqcwMAXy/qm2TbMymybXdTO7wB43UmyJ5JCWMwiYiIiIiorzMLJjkpU7PDYBJR6umyCgN2t/i9qdTNYWbSlppW011M4ksUJwaTiIiIiIj6Opa5EfVMwTiCSYme092Ks55JWy2DScxMSlTMYJLQ5Ashciy2FwshHhBCbBNCtAsh1gsh/iSEyEv+cImIiIiIKOnMspASLXNrq4+9D4NJRInRBYKDdrf4vel8c5iZtLmmxXQXlrklzklm0iUAmgDcrt8ghMgF8DmAawEMBZANYAyA3wN4O3nDJCIiIiKilDEtc0sgi0FKZiYRpYPuHLIPJvWSBtyxAkHRZW61zExKFSfBpMNDX58y2XYDgMnQcsz+BeA0ANcBqAJwrBDigmQMkoiIiIiIUiQYhGnJSCCBnkneFmc3rgwmESXGUOYmrPftLedbPMEki8ykADOTEmbT6r3TdACbpZRrTbb9GNr/PA9KKW8MrxRCLIeWsXQhgJeTME4iIiIiIkoFq6BPIplJTrKSgN5zc0uUKXFlJvWW8y2eYJJFZlKAwaREOclMqgCwTL9SCDEUwPjQ4kPR26SUXwJYCWBqrIMLIU4SQnwihNglhOgQQmwXQrwihNjXwdiIiIiIiCgRZiVuQGI9k9rrzdcXDlKXe83NLVGGxNOAu7dk4zjMTGps96Gp3TzDkplJiXMSTBoAoNlk/fTQ121SynUm29cBKHNw/P4AFgL4GYATofVbmghgnhBipIPHExERERFRV5k13wYSL3PTyykBjr1FXcdgElFidOeQbTDJKnDc08S6boQCRZX1bZa7BNkzKWFOytzaoWUn6YWDSQstHtcGIOb/DlLKFwG8GL1OCPEtgNUAzgPwNwdjJCIiIiKirkhFmZvZzd7P5gPNu3TPwWASUUJ0569kmVvn67QLJjEzKXFOMpPWAjhICFGoW38itN/iVxaPGwJgl8W2WGpCXxP4H4yIiIiIiGKyLHNLYjBpxAygqAIQbvv9iCg++jI32QeCSTEzk7TtO+rbLXfhbG6JcxJMeg9AEYCHhRB5ACCEuATAgdCCSW/qHyCEyAIwDcA2pwMRQriFENlCiL0APAotEPWS08cTEREREVEXpCOYJFzqV6v9iCg+Mp7Z3HpYmdvmr4A3rgRm3QxUR3XWcdgzybbMjZlJCXNS5vYPAFcCuAjABUKIZgCloW0vSyk3mjzmFACFAL6IYyzfADgg9P16AMdKKavMdhRCXBkaE0aMGBHHUxARdQ2vO0SUTrzmUFqlo8yNwaRuj9edHkoXDO41s7k17wFmnh65Pi18GvjxbGDw/o4zk2zL3HrQj6K7ipmZJKWsB3A8gCUAPAD6ARAA3gFwtcXDbgx9/TiOsfwIwCEAfgigEcBHQohRFmN6TEo5XUo5vazMSY9vIqLE8LpDROnEaw6lVVoyk0LZEgwmdVu87vRQugybXhNM2jZPDXT7WoGVb4UWktAzif3aEuYkMwlSypUADhBCjAVQDmCrlHKHzUN+AS3gtNzpQKSUq0LffiOE+ADAZgC/g3XAioiIiIiIEmU5m5u368dkZhJRehjK3HpJMMnfYVzX0ah9dZyZZNczqasDozBHwaQwKeUGABsc7Pddl0ekPb5eCLEewLhEjkNERERERDFYlrlZBJkcHdMqmKTr59LTergQdTfxlLn1pGwcs8zI8Gt10DOpoc2HHZzNLaWcNOBOOyFEBYAJcBC4IiIiIiKiBFiVuZllBjilv1ELB5NcnM2NKKl6a2aSWc+2cIDbQWbSkm319ofnbG4JiyszKTRL2w8BnAGtWXa4mHYPgIUA3gbwHyml448xhBD/BbAIwHfQeiWNh1Ym5wfwt3jGR0REREREcbIMJlmXiMTkuMyNN3RECdGda72mZ5JZZpLTTEYpsXBLnf3hGUxKmONgkhDiIAAvAhgFGOYbHBn6dzaA24QQP5RSznN46HkAvg/gVwCyAWwD8BmAv0gpNzsdHxERERERdYHVDVpagkk96OaWqDsylLnpb9Wj9KSyUrMy23CZnoPMpEW6YFJJngcNbZEAVZCB7IQ5CiYJIY4EMBtADrQspFcAzAewG1pgqRzAQdCCQqMAfCaEOElK+XmsY0sp/wrgr10ZPBERERERJciqN5KPwSSibk93DvWaMjfTnknhMrfYPZNWVDYoqw4c1Q8fr6qKHJ6ZSQmL2TNJCFEI4FVogaR/ABgppbxeSvmslHK2lHJW6PufARgB4J/QMoxeDj2WiIiIiChjVlY24rm5m7GpuiXTQ+meLMvcrJvXxsRgElHKNLT68NK3W/HtplrTBtx3+X5o/sCedL6Z9UwKZ1bFeB0y1IA72sgBBcqyn8GkhDnJTLoWWm+k+6WUv7LbUUrZDuAXQggJ4OcArgFwb8KjJCIiIiLqgmXbG3DOv7+CLyCR53Hjw18cieH98zM9rO7F6sYsqZlJodIbfTDJKpBFRKa8/iC+98CX2F6nBXtnT67H3lHbA9KFZwMnolzU46dZ76sP7knBpIBZmVv4emEfCJLBIKJjRVkugVyPeu1hA+7EOZnN7XQAzQD+EMdx/wCgBVqjbiIiIiKijLjzvZXwBbSbhjZfAPd/tDbDI+qGrMrcmJlE1O28v2xnZyAJAL5cs1vZHoRAB7Jxl/9ifBHYT31wsAedbwGvcZ3D2dykLkidl+2GW6i9pALsmZQwJ8GkvQF8JaV0/L+JlLIVwFcAJnR1YEREREREifp2U62y/OmaKos9+zCr7CD2TCLqduZvVq9pPr8aDI6ezc3QP6knnW+mZW7hBtwxMpN0rzPP44bLpQaTmJmUOCfBpBIANV04dg2Aoi48joiIiIgoJdh01YRlZlIiwSTdz9kymMTfB1E8/AH1nHHBugF3sCcHk0wbcDsvc4uWz8yklHASTKoFMKQLxx4CoC7mXkREREREacJYkgmr6cITCiYxM4koFXy6QInbNpikBlAsz/XuyCzI7bTMTZ+ZlJ1lyEwK8NKTMCfBpO8AzBBCDHR60NC+hwJY1tWBERERERElm78n9QxJF6ufiY89k4i6G58uM0kfTAr25sykztncLDIfw4L6MjcX3PoyN2YmJcxJMOm/AHIAPBDHcR8E4AHwRlcGRURERESUCowlmUhJmZvTYFIPypQg6gbqW7XG1Dnw4jdZL+HyrNnK9uhsJGNmUg+6AJr1TAqXuRmuL25lUZ+ZlJ+dZShz05cLUvycBJOeBLAJwPeFEK8LIQZZ7SiEGCSEeAPA+QA2hx5LRERERNQtsE+GCauADjOTiLqd6mYtmHSF+31cm/W2Ybt9mVsPOt8CZmVuFj2TXFnKoj6YlOtxMzMpBbJi7SCl9AkhzgTwJYCzAHxPCPEhgG8BhOchrABwMIATAGQDaABwppTSJJxIRERERJQZbMBtwmo2t6RmJoVu5Fxu+/2IyFZ1cwcA4EDXGtPt0aVtUp87YnWud0cBr3GdVZlbjOtKfrYxmMT/CxIXM5gEAFLK5UKI6QCeA3AIgNMAfE+3W/i38y2Ai6WU65M2SiIiIiIiSg2rMjcfG3ATdSfBoERtixZkmeDaarpPdGZSwNAzqQcFUGzL3GIEkww9k9zGBtw96WfRTTkKJgGAlHIDgEOFEEcDOAPAAQDKQpurASwE8LaU8tMkj5GIiIiIqEuyXAJ+fgJtzyqgw9nciLqV+jZfZ0bNZjkIg4Rx8vRe0zPJtMzNYjY3YZ+ZlJftNvRMCvL/hYQ5DiaFSSk/A/BZ0kdCRERERJRkLpcA+uBNQ3VzB5Ztb8DEIcUoL8613zmtDbiFyb7SfH2KrahswJpdTTh+3woU53rS/vxE8aoJlbgBgBvmJWtB255JPajMzSwzSVr1TNIFk2BW5qbuwTK3xMUdTBJClAM4BsBEAAOg/aZqASwD8JmUsjqpIyQiIiIi6iL9p9Fd8fX6avx11moU5Xpw51mTMHpgQRJGljo7G9pw+gNzUN3sRWm+B29ddxhGDrAZs1UflYQacNtM3S1carBJBo2ZBSn2xdo9uPyZ+QgEJQaX5OLTXx+NXE96x0AUrz1RwaQsmGcZBWRUMEn24EzAgFmZW2j8+teha8At9JlJHjdcgmVuyeZkNjcAgBCinxDiKQDbAfwHwC0ArgZwLYA/AHgZwA4hxKNCiJJUDJaIiIiIKB76pqvxCgQlbnhpMZZub8Cc9dW4450VSRpZ6jz06frOGZ/qW33458fr7B9gla0Q9HW9Ya9VZpL+eyDtTYGllLhi5oLOzISdDe34Yu2etI6BqCt2N0ayBbNgnlGozubWg4NJZhmTnWVu+mC1vsxN3Z5n0oCbZW6JcxRMEkJUAPgawKXQspnqAHwCLYD0KoBPAdQD8AC4AsBXQoiBKRgvEREREZFjiQaTVu1s7AzMAMCna5IbdJBS4p2llfjTuyuxZFt9Uo75yoLtyvIbi3fYP8CqzA3oenaSbTApszO6LdpaD29Afc6N1S1pHQNRV+xqiJ2ZZFvm1qNmc7MpczNkJqnXFENmkkkwib30Eue0zO0xAHsDWA/gRinl+2Y7CSFOA3A/gH0APALgvGQMkoiIiIioKxINJqXaB8t34foXFwMAnvl6M774zTEYUpqX2EHjvUeyu8H0twM5hV0YQxyZSWkOJr34rXEWrDZvD7rJpj4rOjPJumdSdAPuHpyZFPAa1wUT6Zmky0ximVvCYmYmCSH2A3A6gA0ADrQKJAGAlPJdAAcB2ATgbCHEvskaKBERERFRvPR9MtL9+FhufHlJ5/f+oMTMuZsTPqaMN5pkd4PZ1Sbc3TiYtHircQas8HTrRN3ZroboYJJFzyTbBtw9KJhk1oA7aJWZpO+ZpCtz8xhnc2MD7sQ5KXO7EFro75dSyoZYO0sp6wD8EoAIPZaIiIiIKCPMEpPi6ZWR5U5tMMnrV2+K1u5qSviYcX/gblvmlqxgUtTPMcPBpOYO4+tlMIl6gl1Kz6SuzObWgwIoAZPrUmeZm33PJAF9mVuWNrNn9OF7UFytu3JS5nYggAYp5TtxHPcdaD2UDu7KoIiIiIiIusoXCOLDFbuRl+0y7YvR4Q8iL9vZzF1WwSj9jUmyFCZhivq4bxdty9xS0TMps8Gk1g7j661p6TDZk6h7UcrchJPMJP251oPKORPJTNJdBfOz3fDpAvcsc0uck2DS3gAWx3NQKaUUQiwKPZaIiIiIKG1ueHExPli+y3J7uy/gOJhk9um1NxBErqFHR3IU5iR+XBnvTZLdDWa8mUm7lgGzfg9s/lJdrwSTMld6I6VEq8/4epmZRN1dIChR1RTdgNtJZlJP7plkFkwKZyvpeyapr1NfApjncaPZpWY6scwtcU7K3EoAdGXaij0ASrvwOCIiIiKiLmnu8NsGkgAtM8kpsxsO/UxgXdVuEtTIyepiMGnhM8B944FHj8QYEWP2Nj2zcpKweDOT3rrOGEgCuk1mUoc/aPo7ZTCJurua5g7lb9c6mCRMvwegnmu7VwCr3+/6jI2pZlZ+67DMLbRT53d52W5DNikzkxLnJDOpEEBrF47dDqCgC48jIiIiIuqShjaTT7N1zII4VsxuOPTlEl1V3Wwsrerwd6EMpa0OeP8mbfaj5t24ye3BVcFfOn98wKbEK57MpIAP2LnUfFt0AEmf1ZXGYFKrxaxtda2+lJYvEnXF7sZ2vLZwO4b1y8PIAeqtdbajMjfd33O4TGz1+8DLF2vBmYpJwFVfmMyIlmGmmUmh12wIJgntGhN1LXFBdr7+PI8bWbpz2x9gMClRToJJiVxReTUmIiIiorRp89pk2YS0xxGwMQsmJSszKbpkJcwq2GFry1xlGu2T3AuA2DG1CLsZ2+yac+t12DQP7yaZSa0Wfx+BoERDmw/9CrLTNhbqgxp3al+LB8fc1RcI4qyHvsLO0Axuh44doGz3iKBpgzRHZW5vXx/J8tm9HFg7G5hwqrPXkC4Bk2xBqzI34TIJJgU7X39+ttswM2eAmUkJcxJMAoBBQogj4zz2oHgHQ0RERESUCCfBmHZfYmVuPn9ybkKqGo3BpLauBJPcxgBIDrzogMPAiN+mxCuehr09Iphk/XpqWrwMJlHqfPs48MFvtXPqxLuAQ39mu/uiLXWdgSQA+HpDjbLdbVHmFpB2s7mFzrXWanX9tnndL5hkW+amv2YIw3XFFQo4uQRQlOuBW1/mxp5JCXMaTDop9I+IiIiIqNtyFkxKNDMpOTMi7TEpc2uLY2ydTMrUhok92CCHOnu8bWZS3wkmsW8SpYyvHZh9SyQY8vk9wCHX2JaW7ai372XkctQzSX+uWQVQumFBkWmZm1XPJJfhuhKe0W1AYQ7cLgG37kfBzKTEOQkmbUUXZhglIiIiIko3J5k98TXgNq7zJikzaU+jMYjTpTK3jmbDquGiqjOYpJ88zcCsnCQsnswkr3EcnaIHoQ8mxROwSlBrh3XZXm2LTe8ookRsmaMGfTsagNZaoLDM8iHRgeXhYjd+lfUqAOBv/vOxTVbAbXFu2pe5WZxrMS8SGRC0mc1NH4AWZplJ2j7lRTnasmBmUrLFDCZJKUelYRxERERERAlLdmZSKmdzM8tM6lIwySSIM1xEJmOOeZtom5kUx2t1nJlkM8NUirXY/Hyvfn4RJg0txt+/PwXjK4rSNibqA9Z+aFzXWmMbTKppjgR5/+l5CNNc6wFo5/Z53ls7M2/0lAbc0um51s2CScGgxVhlKCspdmZSuMwtHEzSl7kxMylxrti7EBERERH1DFYNlqMlPJtbshpwm/ZMiqPhdVisYFKsrAPbnknxBJMarbcpwaRMzuZm//NdvqMRd3+wOk2joT5jy1fGdfq+RTrhsstcdHQGkgDgANc6FNlMtm47m5sMpjUTsMvMspI6twXi6plUXpSrLesbcKfvstNrMZhERERERL2Gk55DHQk24PbGUSZnp9qkR0+yytxGit2d3yeUmRRXA267Mje7nknpyxBw8vP9ZHVVGkZCfUprrcm6GuO6KDWh68NA0WDYNliYHC8kCBdyPa7O79WNAfMMQp91cCojzPolhQX9Jj2ThCHjUYTL3IotMpPiybokUwwmEREREVGv4SRY0OGPo8zNtAF3cm5CzIJSXZrNzdtiWDXDtRIeaFk4+k/kDex6JvWhBtxEXbFqZyN+9OQ3+NGT32DtbotzwCxgGyOYFO7hVY56w7bBwvqxQbhQkJ3V+b1CBs3PU7tAcCbE6uPmqGeSdu0uC5W5ZRmCSSxzS1TGg0lCiPOEEK8LIbYIIdqEEGuEEH8RQrBQmYiIiIji4qxnkvPghVmT1mRlJvlNglKtvgBkvJk6XuPNYbFoxSGuldpCrNSkpGUm9YBgkk0DbqKu+MXLS/Dlump8ua4aN732nflOZudYS4zMpOauZSYF4EJBTjiYZFLmZlaOanINyaigzXkaDMA4P5hdmVuoAbcumMRYUuIyHkwC8GsAAQA3AzgZwL8BXAPgIyH0/9MQEREREVlz0nMo0QbcyeqZ5Dc5diAo4QvEG0wyZiYBwPdc87RvYh3ObzOLWVoyk5KXLfTElxsx9Y4PceaDc7C52vhzaY3jd08Ui9cfxOpdkb/7pdvqjcFgKQFfm/HBDsvcykyDSXaZSQL52e7O79Wx9JTMpBhlbu26gFh2gU1mktYzyW3omcRoUqK6Q7DmdCnl96WUL0gpP5dS/gPADQAOBnB0RkdGRERERD2Ko8ykOMrczBpwJyszySooFXepm8WN4OnuuShEK7yBoH22k21JSRyv1S67IQ2ZSZX1bbjr/VWoa/Vh6fYGPPDJesM+TjOTkhUwpN7Nb9J3p0N/fQj4YBrRtWnALaVEXbhnEkyCSbDOTHK5s+BxW/RMsgommTTxzyi7BtwyCLTsUdcVlBmuK509k6xmc2MwKWEZDyZJKfeYrJ4f+jo0nWMhIiIiop7NSSAmrjI3k/uNpGUmWWQgtfriLMWyyEwqEB04za1lJ5llQUUGYlPmlorMJFdqgklvL61U+vK+vmi7YZ8Wh4G6eLLXqO/y+Y3nlaHBv98kKwmwzUxqbPN3nrPmZW7Wj83OykI4buK4zK3bZSbFKHNzEEwKZyaV5nu0ZUOZG4NJicp4MMnCUaGvqzI6CiIiIiLqUZLegDuVPZMsZhOKu0m0TUbQfmKTtovdmP0xmt06ZRtMirqRS1FmkpNAotOsr3gCjtR3+UzOYUPmo88iWGsTTKpuiZSempe5WWcmZWd7IELnm/PMpO7WMylGmZs+mFRYbhlMyvNoJX8sc0u+bhdMEkIMBXAHgI+llAsyPR4iIiIi6jmc9MRp88aTmWQ2m1tybkKseiPFXeZmkZkEAIWiLfRcdsEkuwbccQRV7LIbMtSAW1/e16LrqTVjzADTxzEziZwwyy40/O1YnV82DbhrWyIBXrPMpCE2mUmeqMykgP52PxiwCCZZX0Mywq5nkjTLTBpoEkwKwuMWyAqV/OkTIhlMSly3CiYJIQoBvAXAD+Bym/2uFEIsEEIs2LPHrEqOiCi5eN0honTiNafrnDTgboujjCylmUkWAZ64M5NsgjiF0IJJtmO265mUpDK3lxbsQEu4X5EhmJScmzqzAJD+Z6lfvmTGyM7MhVjH6u143YmfWZDWkNVmFUyyOV/CM7kBQBnqDdvzhPU5W9XsgyuUhSN7ZQPuANBsVuamvlYhJHKzIud2li6aFGCZW8K6TTBJCJEL4G0AYwCcJKU0FjmHSCkfk1JOl1JOLysrS9sYiajv4nWHiNKJ15yucxKIaenoHrO5+Sw+GW+NERCramzHDx6bi+l/+hj/+t86R5lJ3i5nJiUnmLRoWwNeWxh6e5+izKSaFuMNdq1unf5nO6Q0D2/97DDD4/pimRuvO/EzDybpy9wseiZ5my0DqbEyk+w0e9EZTHI8m1ugwz6Ak26xAtyGzKRyQKhB4Wz4kRMVKGaZW/J1i2CSEMID4HUABwE4VUq5LMNDIiIiIqIeyEmJWKxgTbRUzuZmlZkU6zX883/rMG9jLaqbO/D3j9ZA2szEVAAtUGRVUodgQOtBYsWir5OBlLY9YCQEnpu3RVvQB5PiyX6yUdPcYVhnCCbpAokFOW6MryjCASP7KevjmfGP+i6zxvbGMjfj3yUALVBrsS38t5yPdhQIi8dbCEJ0JumY90wyacAN2Pc8S7eAzWuWAaBFNxNeQRmQXaisykMH8rIjr19f5hZkMClhGQ8mCSFcAF4AcByAM6WU8zI8JCIiIiLqoZxkJjXHlZlkXJeMzKRgUJrOFAcAbTFKrF74Zmvn93nogDCbdjykIFaZm9WNbpjTzKSWPYDPOkMqKF2obw1lPqQoM0kfOAKA2lZ9ZpL6evKzswAAuR51THH3raI+yTQzSX+uWc3mBmjZSSZqm9uQBX/cWUkAcNheZfaZSVaZjG11cT9XythNCuBtATqifi7CDeT1A3LUYFIB2pUyN7duNjeWuSUu48EkAA8BOB/AfQBahBCHRP0bluGxEREREVEP4iTrKJ7MJLMbDtuSMYfMMhrC7AJiOxvUG9NC2JSoASgU4cwkizHbZQAAzrOGajfaHwYC+w0t1hZ05Si2mVFxMCtzq9Ot0zfgzs/WxqLvm9QXeyZR/Mwy/joMZW4256g+mCQl8NH/4fdLjsfqnMvw3+z/i3tM3z9gaGcWTlCaBG69reYP/O6VuJ8rZezK3Jp3q8sFA7W0o+wCdbVoR1525Lx26crcnCZdkrXuEEw6JfT1FgBzdf+uyNSgiIiIiKjniZXVA8TXM8msFCIZZW5+mzsZu6yYBZvV7IFioWYZdOSos5OFy9w6LDOTbG7aAOeZSbWbbDcHIZCdFbr1cHt0G5PTqyW6aXHnsKKCSVJKw882nJmUow8mJamUkXo3s1LV6L+dqqZ2NLXYNLfWZwntWQ189U9kyw5kiSAGiPhLz46YONo+M8lnEUxa+EzSmuEnzCbI3VS1WV1RUK591QeTmJmUclmZHoCUclSmx0BEREREPZ8vELTuDRQlrsykrjTgXveRFlzZ7zwgv7/pLnbjtMtMWrC5VlkugXoz2p43CDkdkd5F+aIDLgStx2zXfBtIWmaShCsShMvKUTcmofFvmzdgGkiMDia1+QJKRpjHHQlwRd90AkA7y9zIAbPzOJzV9tCn63Hv7DU437MQ9xonDNSEgklefxDvf7McrtVv44xEBnT0zYAnFyIUTAroc0eCAcvSOjTv0vqeFQxMZATJYXNN+OybhTg9ajlYPER7ldlFyn75oh05UeWrZg24pZSdPyuKX8aDSUREREREyeCkX1J4v2BQwuWKfRMRdwPuhc8A7/xc+37ug8D1iwC38S23VfNtAGj1WQe7Fm61z0xq95TALXM7y9sA7RN6yzHblZMAzvsZ1cXOTOrMjtJnJsXq2+RATYv5MeqieiZVN6mvdUBBJKil75nEBtzkhFmQtsMXQLsvgHtnrwEAZAU7AKtgUkcTpJR49MG/4Mq6vyNHdLHkc+DewA9fAvqPAQC4Q5c2adozySIzCQCadnaPYJLNNUE0bFN+nk3Z5SgBDJlJhWhXylddLgGXgNKrzh+U8LgZTOqq7lDmRkRERESUsHiaJrc67IljFkyyzX4KB5IAoH4LsPod0930PZNK0YS7sp7EvzwPoKJukeljAkGJdbubdY/TlbllFaMFucq6ArR1PTPJaTDJQc+kzoCWO1vdmITMJLPm2/r1e3SzvQ0sioyDPZOoK8zKVdt9QeXvLgc2f9/eFmzY04Kf1t3f9UASAPQf3RlIAhBV5mbSM8mqzA0AGnd2fQzJZBPkHirUmdza8wZp3+gacOejHbm687qz1DYkWTNz9lUMJhERERFRrxBP+Vprh7N9zWIwlv2HzOxaZrpaH9z5i+cJXJT1P5zhnosfr70WePYsoG6zsk9lfZvhuUt0mUltWcVolnnKugJhk5kUq2eS0zK31hrbzUG4ojKT9GVuSchMMumXBKjBpGp9MKkwOjNJvels8/Imk2KzKnPzR63Phf3MZNtqW5ErEgyoutTsR8syNxmwns0NAJoqExtHssQRTGrNrdC+0WcmiXZDxmG2W11OxsycfRmDSURERETUKzgtcwOAFof7mmcmxXEDYlGu4dfdhB7oWqPusPFT4M3rlFXr96hZSW4EUAp1XZu7CM1Qg0lFaLOegS5mZpLDn6ld6Qy0chuvVZlbrFI7B8xmcgPiCSaxzI3ipz+PAS3Y3BH195Mr7IJJzaistwnuOOVSg6EuyzI32eMzk8pFvbLckhNuwG3MTNJnHGbreqMxMykx7JlERERERL1CXMEkx5lJCc7mZnFTpJbHSBTD5IZy61xt/urQPN9bK3fjYc8/MElswiY5GONcOzBUqBlBLa5CCKkrcxNt1qV5sbKCnGYm+drsDwMRucFOQZlbfav5z7muNXJsfc8ku8wklrmRE2aB5XZfAO2+yHr7MrdmVDc1Jj4QlxqgtZzNLeCzDyA3dZNgUqyMyShN2ebBpAJhLHPL0ZW5xZVlSgYMJhERERFRr7C7MUaWTZREgknJyEyKDu7kwIdsYRK8kAGgvb5zRrjRKx7Cke5vAQAjsMf0uM2uYrh0mUmFdg24YzW/dpKZJCXgs8+uUHom6WdzS0ID7qZ2899nfasXgaCE2yUMmUllRQwmUWJMg0n+gJLZFqvMrbquwXLza4EjMebAUzBtRD9g13fAvIfNd9SVuYXiz8ZgktVMbmHdJZgUR7Zig6dM+0ZX5lbgpGcSy9wSwmASEREREfUKOxvss2OiOc1iMp3NzeoGJGAS0LDI2IkujymGTdlJS7UWTAoGcWT1i7ZjBYBmUQBXXA24k5CZFPDGbNSt9kxKfplbY7t59kdQAg1tPvQvyDYpc4tkSBmDSbzJpNj0jfQB7W8nOhiZYxdM6mhGXUO95eb8inGYevo1gBDA1zZ9yZz2TOposj4G0I3K3JwFmOtlAVrD1zsnDbjdbMCdTOyZRERERES9QmV9HJlJDpt1x1Xm5jW5UWurM93VF1XmViTsgkmhDKTt8633idIkitBiKHNLJDPJwc2WXUPf8GFSPJubVWYSEOmbZMhMsuuZxMwkcsBRmZtdc21vM9obrYNEJ00e0RkY0gdLFIaeSdpjZKxgkr4ZfuN26+dIJ4fXhK2yPPKz1pW5FYo2w3ntyVIztRhMSgyDSURERETUK1TWO89MclzmFk9mUodJCYnFLGfRmUlFdplJrdrMRe2rPrDeJ0ojCgwNuAvRbpNNpQ8m6Rv2OrjZitEvCQj3TLIKJiVe5tbYZn3zWdcaDibpeiZFlbnpG/UymEROmM/mpmvAbZOZJHcsxBNtN1pud3uizuVsu2CSrswtdBrHLHPrP1oNKLXVWQbA08ph6asWTAr9rA0NuDuMDbj1mUksc0sIg0lERERE1CvsbIinZ5LDMrd4eiaZ9SOxDCZFZybZBGNCmUktuzdZ7xOlAQVokvnKuhLRYv0JfLuu+W9usbrspMzNUTDJBW8gCCll2jOTakJBpOomu9nc1JvONgaTyAG/ybWgw69mJuXaNOAWVSvtnyC6v1hOsfV+hmCSwzK37EKg3yh1Xa2za01KOSx93SbLI/2putIziZlJCWEwiYiIiIh6hfh6JjktczOus7wBMctMsixzc5iZ1KJlJvkad1nvE6UuWIA9KFHWlYk66wCYPthVUK4uO2nAHaP5NhDJkPAGgibBpNT1TAK0zKR2XwBNUdlobpdAaV6kd5OxzI03mRSbWc+kDl3PJNsG3LEowSTnmUnhyjipz0zSz+SWnQ/0H6Ouq90Y5yBTwOE1wa7MrUC0mQST1GVmJiWGwSQiIiIi6vHafQGljMklbHYG0JJAA26z0hYA5j2TOhpNp7lWM5NiB5NEqNwtlu3NAlWyVFlXgTrrAFhbrbpcqAsmJSkzKXxT2+EPAlm6YFIKZ3MDtJ5J+n5JAwqy4Yr6I+FsbtQVZudVuz+g/P0UCOcZkwbRwSTbMjfznklBGeN231NgEkzqBplJcZS5dYR/1rpgWwE6DEFiNuBOLgaTiIiIiKjH26UrcSsvyrXYU+O0Z5LpbG7xZCYBxoAN1ICUfWaSVuaW22Ezk1PY5B9iV2MHqmQ/ZXW5qI8jM6lMXXaSmeSgAXf4ptbrN8tMSrzMzTYzqcWLuhZ1+4BCtfEwg0nUFf6gSTDJF4j0B4NWbtVlboeZSboZEsNx0oA+M0kvu0DrmxRtzXvAgqeBui1xDDTJHF4TtkX3TMrKVcr6coQPeS71PM5hmVtSMZhERERERD1epa7EbUhprGCSs2CB6Wxulg24Labdbqw0rIq+CS22y0xqrQGCQRQGGmzHue3wvyJ42j+xq6Edu3XBpApRB69VNlVrrMyk5DXgBkKZSUkuc5NSxsxM0s/eV5ijBo+MwSTeZFJsfqsG3FHByEK7nmixZEVdx7rQMykY43Z/7rY2XPGOLqBcuRh490bIf8/A/O+Wo64l8TLUuDlsyl8niyLnqhBoFroZ3aQa4GfPpORiMImIiIiIerzKevXT/8Glebjt9H0t93faM8kqM0marDdtwA0Ae1YbVkXfhBbHyEzyNVfDDeubnhXBkdg26jzUtEt4A0HUogg+GQmOlIhWSK/FcyQjM8lnM/6Qzp5JpsGkxMrcWr0B06BfWG2r1/D7zs9Wb74Ns7n5mZlEsZkFltt9AbQrmUmJBJOizpU4ZnMToWCSoWeSzqqaADb4B5puE94WfPXK33D/3+9E7bcvOQssJ4vDzKR2ZCvnaqMoUrbn+9UJBjxu9efBnkmJyYq9CxERERFR96b/9Ly8KAeXHjoK+TlZ2LinBeVFObjj3cjMSU57JlkFKfxBabgxscxM2r3CsMrndDa31hrsrNyGETZjbEc2mjv8naV+Ei5UoRRDEQkU5XfssTi+LjNJH0xy1DMpdjAp0jMpkPQyN7usJED722jV/b4LDJlJ6mfsbQ7/PqhvM89Miu6ZJFGYSJlbdGZSVo4WNAqa/L0beiZpX4MxgkmtyMFO2d9y+41ZbwABAO8DqPoWOO3vDgeeIAc9k/zSBR/cSklqgyzA8Kh98nQZncxMSi5mJhERERFRj9ek64FUlOuBEALfnz4cvztlAiYNVWc4c9ozyeqDa9ObEKvMJJPpvwNOZ3Nrq0fN7m12Q0Sb1IJJ0aV+e3RNuPM6TBp4S+kgM8nBzZZV1lOUcLmNeWZSYmU0+n5Jbl339ZoWL1p1ZY15HvUz9VzdLE8dVtlnRFHMeyZFZnPLRwdcIvJ3FHR5DPvbim7ALYR1dpJFmVsgxu1+jSxGO3JQI4ts9wMALHgy9j7J4uCa0I5sAEIpSa2T6s8n16cLJrl1s7kxmJQQBpOIiIiIqMdr0gUUinLUm6tC3bJ+fytmZW4A0GbWoNmqAXfVKsMqn9NgUtCH9ir7qbrbkY2WDj921keCSfq+SYU+k8yk9ga1jM1TAHjy1H2SXOZmPptbYsEk/e9y5IB8ZVnLTFKDh/rMJJdLGJrzNjsMOFLf5fWb91TbGcoS1Je4ebOKgKw8w2MsudVG8cixCPoYgknaVxnjdr9SaiVuO+UA52NKBwfBpDZo15Fw4G5XQzuqAuq5XxBUs0UNmUksc0sIg0lERERE1OM1t+szk9Sbq34FakZAXauzYJJVmZtpaZVVZlLTTqCtXlnlDzhswA3AVbPOdns7stHU4cfOxkg5jT6YVNSx2/hAfVZS/gBDuYyzMjfnDbhTkpnUpv4uhpbmKdlJLd4AanW/b33PJAAYUKCOq6Y5A42HqUcxy0wCgPVV2rVAX8La4co3BmztZHUtmCQ6G3Dbl7ntCAWRHAeTAmkKsDq4JnSEgknhmfO+3VyLel2GlaejXllmmVtyMZhERERERD2ePoukUB9MylcDBfWtXkdlTAGLfRraTIJR7TYzrjVXKYvRvVYGCzWo0yzVmejymzbZjrEdOaht9mJnVBPyrVKdla3Mu934wLY6dTm/PyB0wSQnZW6OeiZptx1azyRdqU+Sy9yK8zyG33dlvXpTn5+te50ABhapN+41LYk1Bqfez6xnEoCozCS1X1KbKx/w5Js9xJw+mGRZ5qbvmeSszC2cmbTDcTApTeeEg2zFdqlmJn27qQb1skDdSXeN02cfMjMpMQwmEREREVGPZwgm6cracj1upcmyLyAdlTEFLTKTTINJjTusD9Silpn5QhkNBWjDABEpxfBKN9bKYcq+A9q32I6xXXpQ3dyBnVE9kzbJQco+5X6TsRkyk/p3MTMp3tncdDfICQeT1N9jcW6WITNtT5N6E2waTCpUx7WniZlJZM8XIxhRqMtMakUekB1PMEkNLCPHIpiUo/aEc4cudXaZSW0yG7XQMnnakGO5n8JBY+ykcBC0atNnJm2qRT10P582dYKBbDczk5KJwSQiIiIi6jmCQWD+k8CLF2pfQ5lD+oBCUa6x0a0xOyl2qZtVmVujWTCpwSaY1Ko2wA5nNAwXapBphxyIGlmsrBsSqLQdYzuysbOhHSsrI9Ngb9YFk4YGTMamC3Ahf6BJZpKDYJKjBtxRPZOSXOam75lUnOtBnkd9HdXN+mCSgzI3ZiZRDDGDSbqeSc3Ija/MTX+uWGUmFVUoi+HMJGkTTKqUA4DQ9uXB0c7Gk+C56pjjBtxaZlJTuw/rqppRL/XBJDUziWVuycVgEhERERH1DMEg8ObVwHu/BNa8r31d9xEAoFnfgDvXGCwo1QWT6lpj37BYNeDe09SBJdvqUR8+hq/dEDBStOiDSdpNzAih9jLaJsvRCF2pRgztyMY3m2rREjWd/VZZgYCM3EiWyxrA26I+UFd6h8Jy08ykYFDi4c/W48LH5uGxLzYYsrUCcczmZtqAO8k9k4pyswwNtvXBJP12wFjmVs3MJIrBbxFsDtOXuW1tyUIbci32NmHITCo2369QDR5HeiZZ3+7vCJW4AcAnwSnOZnRLV2aSgzK3Nqmdrzsb2rHfbR9CShgzk1rVzCQPM5OSyvi/LBERERFRd7T+I+C7l9V1Gz8Fxp8Ys8wNAPrlx9+E2+pe8Y53VwIAzs9fhFtHrURh/0HmO4bpSsrCs7kNF2pAZ6sshxfxTR8evqmKdsheg7BtSzlGRQerajcCg/aLLOszkwrKAKG7+ZQBfLhyN+6ZtQYAMHdjDcZXFOHovSM9mTpamxCrcMe2AXeCs7npSw6Lcj2GzCNjmZvx70Nf5qYPQBHpxVvm1hjIwbfbm3CUMZZpzq37O7UqczNkJmlf7XomRTfpP/PAvXDzrkcwrPJ9rJSj8Ies5zHRZVJe240yk8INuKPVGTKT6pVFzuaWXAwmEREREVHPsHuFcV39VgDG2dXMMpP6FRibcMeiL3MTCOJQ1wrkowPnuz/HicGFwEZo/+zoAjeRzCRjMCkXzmaaC2s3CT4dv08FNm8ehFGICibVbLAPJpllJskg/jprtbLqX/9bpwST2lpiB5NkCsvc9IGigYU5hp5I+qCgec8klrlRfHwWDbjD9GVuTchz3p/IjFmZW1aeIWPJ5WA2t+gMyPLiXFx22JE45+EstHoD+JX/Gjzn+QvKhG5SgXRkJgWDQDD2NdDsuhezZxLL3JKKwSQiIiIi6hmaTaa3r92I4Gf34A/Br/GsOAmr5QgAQIFJ5ok+M6m2Jf4yt/s8j+Bc95w4Bh2iK3ML34SO1AWTtslylIv6uA5t9gn9cfuU46P3B+FoLO1cF6xer+Yp6MvczDKTgkFsqlbL45btUG8w25p0s8KZsM1MSjiYpJYSlRfnmGYeRXPSgJtlbhSLP87MpBaZh1aRQDDJLDOpqAIQatAonJkkpXUwqRmR3k1lRTmYMKgYr19zKL5aX40j9joSZz09Hs+2XYexrp2RB6UjM8lBIAkwz8is1fWbQ8sera9e6OdjaMDNzKSEMJhERERERD1D0y7juqqVcFWtxA+zgBPci3B0x98hcorgchlvovQNuJ2UuUVnJpWhznkgqXxfoGplZFnfgDsYBCAx0bVJWb9RDkZ2nJlJ+puqycNLMaxfPraKIcp6QzDJtMwtdgNud9TPtqHVB7e3ATYJENpzd/ZMCiQ9mFSly0wqL8ox7YkUrcCkDNIQTGJmEsUQKzNJ3zOpGXmdU9p3SY5JX6NCY4ltuGeSXZlbk4wKJoWy8vYZXIx9BmsBmeL8bNS36YJX6QgmOcx+ajcJorciF23IQR5Cxwh4gfYGIK8UAJDDzKSkYgNuIiIiIuoZzDKTopSJBpzn/sK0xA0wNuCOt8ztINcaB4MMGTxFXW5Reyb5AxJDUIMyEZmBrVXmYL0cioYuNOAOEwK4/YyJAIBK91BlP1mzXn2ggwbcMmgSTIrKgvh41W4UwflsbpaZSRaNzmMeNygNvY3Ki3JjZibpZ3sDjGVu1U0MJpE9LShsrUSoWX3NyEWrSZnbtpzxwHG3xn7C/AHGdSbZSi4HDbj1mUl6xblZ8EpdKZmDQE8gKA0zLMbFYcCqzSSYBABuXf+o6KA5y9ySi8EkIiIiIuoZzDKTdI52LTFtvg10tQG3FuSYJDbioex/ORhkyPgT1WVdFpAvILGfS220tFyOQgBuNMp4g0mR13XG5CGYMrwUALDbowaTXHUbIgvBgKEpuFlmkt+v9qICgBZvAO0+Lci0YnsNCkW7YR+9cLlNhz8IuFyAS/c7CnTt5rO+zadkhxTlZCEv221axhbNLDOpND8b0Qltje1+LZOKyEKszKShQs1I3CX7m/ZMqskfA5TtHfsJRx9lXNd/rGFVZ5mbTcpgs5KZZJxhriTPA6++kClGoGd7XStO+scX2O+2D3H1cwsNMz864muLvQ/MM5Pys93ILi5XV9oFk1jmlhAGk4iIiIio+5PSUTBpkmszCi0yk/RlbvFkJv2f5znDtiu8v8JPvb80PqhoCDD+ZHVda43WWDbEHwxisi6YtCw4BgCwU5pkH9iIvqk6dkLkRqrRU46OqMwCd1ttZKrs1hoAUTd6ef0At0cL9ETx+82DPDvqtRu+rTvV30lQXyYXXh+67ejMBEhSqVuVrl9SWbF2ox4rmGS23e0S6F+g3ug76atFfVes2dzMGuy3mZS51RWNB/Y6ESgeFll58DXGA+b3B065R1036nDDbuEyX7syt+jMpIFFxjEVmwWTYmQmPT9vK9ZXNQMAZq3YhS/XV9vub8ppMMmkZ1JOlkvLsIwWlYHp0fdMYmZSQhhMIiIi6i46moDFLwAbPs30SIi6n45GwB/7JqNMNGCIp9l0W6khM8lBMElqM7hNFesN2xYG98LHwWlYhAnqhikXAp48ILc0sk4GgOZI4MUfkDjC9Z3ysKWhYNIu9IPf5bxJb3TPpMPGDez8Pjs7G5ulruSjNhTAMjTfDt2A6YJBgYB5Zs72ujZIKVG1WxdMyi8z3T/YOZtb6HhuXflMV4NJjerNbVmo75FZ5lGYSxh7p4TpS930xyeK5rfJTMqBFxWINKeXEKiUA00zk+oGTNXOiUvfBg78KXDMH4ATbjc/8EFXAsffDoyYARxzCzDhe4ZdwpWodrO5hXsmFWS7TctCi3PNMpPsz4dHPt+gLP/twzhKg8N8sctmAfPZ3HI9bi3DMlp0ZhKDSUnVLYJJQohhQogHhBBzhRCtQggphBiV6XERERGlTWst8NgxwFvXAs+dBXz7eKZHRNS9NNn3S4q2N7aarjc04G5xUOYWlOiPJniEGlR5K3Ao6lAMCRduDl4DFIdKykqGAzN+FnrCUerB6jZ3flvUvhP7uSLLQQh8HZwEAJBwoS5HLVGz04j8zu+jm0jnetzYLHXNeRc+o2VINWxX14c/zdf1TAoGjGVuALCjrg17mjsg2xuV9e4i+2BSJDNJd0Pd5cwkXb+kYq1cxy4zqSA7q7NBsd7gErXcp7LeWZYE9U12mUlDRTVcIhJsEsVDccj4ISg26THWUaad+xgwFvjefcBRNwFZFgFlIYDDbwR+PAs46jeGcxaI7pkUeza3gSb9kgBgQGE2vPqAjT++87SxrQvlq/rMpKw8093MytxyPW7bzCSWuSVXtwgmARgH4PsA6gB8meGxEBERpd/sm4GadZHl938NzH8yc+Mh6m6aY5e4hY0KWASTCvSzuTkrcxsk6gzrf+v7aef3q71lCFwzF7jif8D1C7VSFMA2mLR/izor3NrsfVGNks7lmuxhcGqj1GZt+90paoZUnseNTXKwuvPi54A3rgCqdRkDA8ZpX3WZSV6TnkmA1htlza4mFOsaDIvcUmOgCFqADIi6eUtRmVt5UbjMzTozKc8m0DSsX76yvIPBJLKhDyYdNKp/5/f6Ejf0G4krDh+tlJeFFRQYm2gnItIzyabMLZSZVFZoHkwaW1ZgbMAdlZnU2O7DVc8twPQ/fYTb3l5h2h+poUvBJF2wLTQTm55+FksglHFYoO+ZVKVuj8LMpMR0l2DSF1LKCinlqQBezfRgqPdasXI5lj90EXx/GQ3cVgJ8fHukdwARUaZICSx90bj+vV8CVavTPx6i7qh2Y+x9Qob5t5iuL8rJUhost3oDMXueBKVEuS6Y9GVgEtp1pSotIh8YNl3NJug3Uj1YVDDpkKaPlU3fFap9T/ZkW2Qm6WZz2jHkRBw6rhxXHTkGlx82StmW63Fhoz4zCQCWvw6sna2uGzhe++pgNjcAWLmzUQsm6bMscktMMyo6y9x84WCS/ia1aw249+gzk0LBpAK7zCSbErih/dQb/e11DCaRNb8ugHLImEgwaZhQm+6j3yjMGDsAHwYPVFbf5fuh5QyUXRXOTLLrmdQUzkyyCCaNKSs06ZkUCfq+umA7Zq/YjepmL977egk2v/93HCjU9yyN7ebBaFv6zKTocuEoZplJOR43UKjLjmxmA+5U6RbBJCklf4uUcqs3V6Li5VMxac+78HSEAkhz/g68fX1mB0ZE1GbMeui0/LX0jYOoO9u+wPGugzrMA08ul0BRrhrEiPXJeVBKDBLqB09V6GfYr9nspskqM2nTlxjjWxv1HAIr+x2r7Lo7a4j5gA66KvJ9bgmGXnA/nr/iYPz+1H2Qk6UGUPKy3dgU1GUmhW35Sl0uCwWThHp74Ib52/Q566rx9YYaFAt9MKnUmHWEqDK38M2bPuDkYMpxM8Yyt1Bmkk3AKM9jHWgaWqoPJjnr30J9k75n0iFjIsHe0UKXTdlvFDxuFwLFw3Gl9xf4KDAN//KfhZmBk1Cc5GCScFDm1hIKJg0uNc7kBgAjB+TDpytz83ojgZ47310JAJgoNuN/Ob/CmAV34OXsO3GCK3KtDgRl/DO6GYJJJaa7mZa5mWYmsWdSqiT3r5aoG/tw1hu4QTQYN6x5H2irt0yhJCJKObsZqqpWpW8cRN3Z9vnqclYu4Defkn5g6yatL5DL+LlpSZ5HCSA1tvkwsG0LsOApoGAgcPBVQE5R53azMrdd0hhMaulwEkzaojXaf+3Hyuq5wX3hLRwKRPV62pE13HA4vzsfWYffqDX3rt0ATLkYKLEuh8vNMumZZGVgaFpyh8Ekf1Dik9VV+KlbLXNDboltMKnNG8p0MgSTzH+Xsegzk8JTnNv2TMqxK3NjZhI5I6U0ZLZMG9kP/QuyUdvixV5C15ds4F4AtFLKDxsOVDKU9EHuREXK3MyDSa0yBwFo58H5BxivNQCQk+VGTl4eEBVvr2tsRnRL/2z48LDnHygWbaHnlfht1kv4yHsAEHru6uaOzl5mjvh01xSLezTrzKSB6soWm55JDCYlpFtkJhGlWnVzB7Zs3Wa+UQaBLV+nd0BERNGaKq23Va1M3ziIuqu2emBPdPmEAKZcZLl7dqDZGHwKKclTb9qaGuuBF84Fvvk38MmdwKd/VrYHJVAOfTCpP/SanASTatYDS/6j3NwAwH8Cx6FQl0mzKks3QxyArECrFoQ57Abg9H8Cww807BMtN9uNPTD/VF/hKQBKhqHV68et76hlKi5oWQWXzhiJXxw/3vBQY2ZSCZBlvMkL925pDv+csnQ3l13MTGrSZYSFZ+yzCybZ9VPSl7ntYDCJLFQ3q32+CrLdyPW48ddz98ewfnmYkKX7v71MO6f1AUtAmzktmcJlbj5kISiNAaVw36aHL5qGfYcUWx6nMF/tIVbfpAZ6DnMtx0iXej0b56rE4a7lncvb4s3uc1rmJq0yk+Irc5Myzswp6tQjg0lCiCuFEAuEEAv27NkT+wHU57333U4UCps3A5vZ953s8bpDKWWXmVS7Eegwn+acei9ec3Q2fKIul00Ahk6zf8yK/5quLs5TAwl5q18D6qMadn/3srJdy0xSy9xac4wzlplmJpUMV2ciaq0Glr+h7LIkOBbvBQ9GgS7A0eJ34YusGcq6ptEnG5/DRm6WG4DAouA4+x3L9saW2lac8/DXeHXxTmWTG0FceNBw3Hr6RJwxxVh6VwKzzCTrnkmdwR9DZlLXgjZN7WqZYrj3jP7nGc0u0FRWmKM06W3q8HetiXAPxOtOfDbXqH/7IwcUAABO2LcCX/78AJTLmshG4Qb6jwVgDFgCSEHPJO1rEC5skeWG7U2h5tsn7Fth2BatuLBAWa5paFKWo4NG0S5zz+r8Pu7sPn0DbosytyaTRuY5HrcxO9LXAni131WWSyj91AJBiZqWrjX/px4aTJJSPialnC6lnF5WZj79KFGYlBKvLdyOIpNpODtVr7XeRgRedyjFGnfab7frqUS9Eq85OoufU5fHHB2ZfczKyje1UjcdfWbSgA1vqTu01gAtkZvAQFCiQtQru9xz+cnYq1ydfcm0Z5LbA4xUA0LYNk9ZvN9/HgDR2esnbE9TB/4auAiNMnLDJPc9x/gcNvKytbf6j/lPs92vsWwaznjwK6ze1WRo2OtxSdx11n5wuQRGDyzAOdPUxuD62dyQV2qamRQOJkUyk3Q3gl3MTGrWBfHCGV52M7bps8CiCSH6bN8kXnfis6la/dsfPTASeBHV69WdB4ztPC/CTeKj2QU4uyLcMwkA1sgRhu0tyEW/fA88bvtwQEV/NZCzu1ZtGXKYRTDpWNcSjBC7AQDbahPMTLIoc2uS+YZ1uVkuQAiT7CQte0oIgeH91cdtjXd81KlHBpOI4jF/cx2W7Wiwz0zijG5ElEl2ZW4A0NGYnnEQdUe1m4ANn6rrpl4EDNjL/nFNO4Ft3xhWRweTStCMAbWLjI+tWtH5bVBKVOgyk9zFg7HfUPUmSx/U6DT6KNthrg5q/UpGDjDe4Kxo648feX+Px/2n4mfe65E/9VzbY+nlhhpyzwoehCM77rfc7+ltgzqzb4K624MsEYQragq8/zttX5RF3QwPgO76lFtqkZkUKXOTUialZ5KU0lDmVhjK8MjJcsHtMu8XUxGjfwtndCMnNlfrM5OizuFd36k7l+3d+W1xnrGkLTr4kwzRf/trpLEnUr0sxHXHxAjIAxhZpvaHa25tRW0ok6cM9ZjgMm8j4hISF7i16/a22ngzk3T7ZxcarksA0AiTYFK4ub4+mBTVhNtwra1hMKmrGEyiXu/xL7UZXQxT10ZrYzCJiDKoYYf99naTyQOI+op5/wYQ1dNi8BRg0H5AvrFvkcGyVwyronuT7C0s+inujvQqcwW8GCAipR0SAiis6AxahFkGk8ZYB5MaZD52h2aGqyjOVfp5tIYaVS+V43CX/2LMyT0SWVnxlcJEZ+dslRV4PXC46X7PV0ZmfNNnJgndpMul+dl46tIDMW1EKQBgiKhRtqNkmDFQhEgj4EBQot0X1JqIR/PFH0xq9wURiJopKjvL1TmjnRDCMtujosQ+mDSsH282KbYtur+LUVGZSdg8R9158OTObycOUQPRFjHPhEQfMxywjtYy9HBceuiomMcpKFDPBY/0Ye4G7Zyf4Vph9pBO57q/hAtBbK+PNzNJt7/+WhFi2oA7fA0t1JX2NUf6Oo1gZlLSdJtgkhDiPCHEeQAOCK06JbTO/uMcIhubqlvw8SotxbJI3yAyWitLSIgog3br0sSLdFN5M5hEfVXTLmOJ28FXaV9NPsn/LDBZXbHoWWDHQmVVdFbAeJdutqWwqHOyv1Q/cAoWlAHuLBToSqVMeyYBwKDJQOlI001axoD2OrLdLkN5VbT+BcYbp1g6P6UPWRk0jmNjcBD2IJJ9kKfvNSSDgK5B7X7DSvDGtYdh6R9PwBiP7j1UyTDb2dyAUJ+jJGQm6fsl6adXt+qbNChGZtKYgWqfmI3V7FtHRpZlblIa+7GOOrLz23HlhThuQiTYccv39k362KIznVZK3Xnvyccpl/4uZokbAMO5nC38WLZDe0+i75f0jP9EtMjIeT1I1OEAsTbxzCRPPly6WSW1TCXj/wGRzCRdMCkqM4nBpOTpNsEkAK+G/l0dWn44tHx7xkZEPd6TczZ2vv8phM2FrKMBCFi8CSQiSqWWaqAxKjPJnQ0MP0jdp51lbtRHfXyb+il14SBg0nmR5WP/2Plth8zCb3xXokFEfeof9AMf3aocMrrMzTB1d9i2bzu/HRBUg0myUAv26vvumM7mBgAuVyQAprMsOKbz+yy3sVdPtAFJCCat0t9UAngveIiyPHl4Pxhu0qT59NklsgEiEBUEyikO9UyyLnMDQj8rw2xu8QeTGvUlbrrfiVVmUsxgUpkaTNqwp8ViT+qrAkFpaMA9KtSAG9XrgObdkQ2efMOEAY/+6AA8ccl0vH7NDPzk8NFJH58rKpi0RQ7CrEDUzI8n3GHZh8hAH0yCDxv2NANS4jC3Gkz6MDgdnwanKOvGuXagsr5NySCMyUFmkt9lvMYAQK4nnJlkXeY2YoB6fjPzsOu6TTBJSiks/h2d6bFRz7RsewNe/DaSvl5k1zMJYINbIsqMnUvV5fJ9gPyB6jpmJlFftOJNYOmL6rojf602d57xM6yacD3eDByKi7w3owr98ErFjepjtnylTA2tZCYJixLT6jVaryYAA6RaxiULBwEwBi4sM5MAYOrFQP4AZZUfbjwfOL5zOcuV/MykPH0wKWhsxPtfXenbtBH9AJcuCBMMmD9Bw1Z1uWSY9jVGZlJze3KCSfrSwiLd9Or5OVZlbuY3omFjytTm6hsZTCKd1bsaO0tRAaA034OBhaG/+/UfqzuPOERrxh8ly+3C8ftW4ICRDsp1u0BfOneN7+f4kfd3+H3ZQ8BBP3V+IF1gOAd+bKhqBmo3YmhUiWu79GBhcDw2SHXGx5GiCv6gxK7GOM5vk8wkvYBJXzYAnWWuhswklrmlRLcJJhEl2wOfrFOi4P3dsYJJ7JtERBmw7kN1edD+xmlwOxhMoj6mtRZ49xfqurIJwAGXq+s8ufhyyOW40fczLJATAAA7hpwEVEyK7CODwNoPOhdLlDI3i55JALDmfe1ppfphkyw2z0wync0tLLcEOO8pbXrwkBdwCjbJSEmrxy1MpwwP619gHwAxfVqP+la/DsX4ODC1c/nDwAHYqLv5mzqiVBknAEBaBZN0mV0lod4sJsGk6GynZtPMpPhnc9OXuemnV883KXPLcgkMjPGzHN4vDx53ZLzVzR1o1D0X9W3fblLvGw4c1T9SWqb/f33cCWkaVcTAQvVvXMKFL4P7o6X/xPgOpAvaZMOHLbWt8K37RFm/IDgeHcjG1mCFsj48o9v2eAI2hgbcxmCS4foREslM0pe5RYJJQ0vzlGDbrsZ26553ZIvBJOqVqps78MnqKmXdkDz1IlElS9UHcUY3Ikq3jmZg8QvqujFHA7nF6jpmJlFf4vcCb16rfsjjzgbOfhRwG4MDVY1qEKK8OAfY53R1p5Vvd34b7quTj3alsbbB7JuB2bfgaCxQ14fK3PQ9k2LejIw5GrjiI2DKRcAJd+Ce4EXK5qwYPZO6Uuamz0wCgF/5rsHdvh/gTt/F+JXvGsP2KcNLnWcm1euCcZ2ZScbZqqI1tfsBj+5mUH8D6YA+gKcP8JWYzJpVUZyrzE5nJsvtMmQvMDuJoumDSQePDmUYdTRr2ZDR9joxTaOK2Gdwsen6ceWFpustZenL3PwIBCVaVv9PWf9VcD8AwBapBnFGhoJJi7fVO39OQ5mbMZgkLJpy51jN5haVnZqd5VKbpQNYt9vm/wKyxGAS9Upfra+GPyoraa/yQuQE1DcBW3UXO2YmEVHabfkK8Ea9gSko126C9ZlJDCZRX/LhH5RMIgDAUb8Bhkwx3X13kxpMqijKBSacpu604ZPOModwgKFM1BsP5tIFH+Y+iEOE2hdEhDKT9Fkw+inqTQ09ADjrYfgOuR5tPrWHSOzMpPiDSTkmwaQGFOKRwBl4MnAqmnRTa48akI8BhTlxZCbpgkmlocwkl/2sc1oD7mRkJtmXuU0fqU5rDgAVxc4yvIylbmzCTZpAUBqCSQeFg0nrZgMBb2RDv9HAgLFpHJ2mrCgH5UXGv/VpI4znhC1dZpJH+JENH/K3faGsnxPUskG3SH1mUhUAidkrdjl/Tq8aTOoQxtfhzrYIJnXO5qaOQ+lhBWDviiJlec0uBpO6gsEkSo+AX/ukMU12Nah1uYeP6w/RoV4ktktdTxJmJhFRum2dqy7vc7rWn8AQTGIDbuojKhcD3z6mriufCBz6c8uH7GpQM1rKi3OAiolA2T6RlTIALHsNQFQwCfXqgYYeABx6fewxhoJJ+qyX+lbnZVDbalsR3Y+2rCgHOVlu+8ykwuRkJtmZGr7RdOluEawyk6rXqculoZ5MMTKTtDK3JMzmZuiZpAaxjp2g++AQ5tlKZvRNuJmZRGELt9ShpiVyX1OYk4V9w5lAK99Sd97nNNOZJ9NhmElwevLwEpM9bRgyk3w43LUM2VEf0u+RJVghR2nfoxStUTO6FYk29EcTFm+tx26nfZN0mUm1XuN1zKXPbAzpnHRAX+bWrFas7D1IF0xiZlKXMJhEqdOwXWue+dpPIO8eDvypDPX//RU2VKX+pqhK9ynliHwvgMi7tmaZiyqpi8zr6/6JiFJtiy6YNPJQ7WsOM5Ooj/rmMUT/f42SEcDFrxluaKLpp+ce1i9fu3mbfIG646KZgJSdDbjLhO68KhwEHHkTMGQqrPilC2LwZADGniTVzc4za/Szg4Wnoh/WL89ypjH98zmRZzGbmZWpI0q1bwyZSRYzMVWtUpfDAbwYmUlaA27djW5XgkkxeiaZlfTUtzkL+o0dqMtMqmZmEmneX7ZTWT5un3JkuV3aBz9rdf2S9j0rfQPTMctm1GfvxaTLTMqBD99zz1PWzQ5Mj5qtURhK3caISgDAW0ssJjzQ05W8VneYBJMsMpM6g0l5/dRMU28T4I1cd/WZSWsZTOoSBpMoOYJBYNcyYNGzwLu/QOChQ4H7JwKvXgosfw0iFGEuXfoEHvrHXfjta9+ldDh79MEkUa0s75QDDLMNYI/uDRERUSr5vVoWRrQRoSm6DZlJ9WkZElHG7VmtLh/ze6B4iPm+ABpafahujmQIZLtdGB7+NH7/C9SgyJ7VwNa58LhdyM92o1xf5lZYrjV6vfQdYNolps/3dOBkuIu0GyX9jVptq9fx9Nf6kqlwSZUQAmdOMb7e/Gw39h8WZ0YBgFKHWThhnSUw+p5JZmVubfVAU2Vk2ZUFDBgX+j49mUmxeiYJIXD21KHKurOmqMtWmJlEZtp9AbynCyadMkmb4REr3wL8UYGQ4mHAkGlpHJ3qooNHKsuXHToq/oPo3o/s7dqO01zfKOs+CB6kLG+Q6jm2l0sLIr2yYDukVWA6mj6Y1G4MJglPXqSkLUrnOiFss5PG6zKTVu9scjY2UjCYRPELBoHq9Vq6+OxbgJlnQN47DnjkcODt64EFT8G9Z4Xlw3/peQ0vL9iK9VWp+4Snqkl9QzJYqnW622QZ1gaH6R7EYBIRpdGeVUAgKvBdNDjSvLZAV4Zbt6UzM6CmuQOXPvUtjr73U7z0rW5abqKerkX98AfDD7bdfYMuW2TkgHwtQwDQglB7n6I+YP6TALRSJ0PPpHCPjZwi4IwHgBsWQxZFZltbEByP+wPndc7YlJ3lUkqmpARqW5yV9OsDE2OjAhdnmgQ7Lj9sVPwZBdCmK4+elSyaWaZTZ+mHPjPJrMxtzxp1ecC4SAaZSaP0aE1Jm83NvmcSAPzm5L1Rmq+tryjOwdnTnAaT1MykTdUtCDoMFlLv9eK3W5UPrfOz3ThqfChoseQ/6s6Tf2AsGU2jo/cuw5lThkAIYMKgItxw3F7xH6R4iCE4nCMi2X3bgmWYG1RniFsX1AWThBZMWl/VjI9XqeVmBlICXvW6vqvN5BqWlWuYBAGIykwCjE24WyJNuEf2z1dmu6xp8WJHffyTAPR19ld6IgBorAS2zgO2L9A+Rd/1neEkj6cSeJioxmixCws218Y/o4BD+sykAT41mLRVlmOdLmqOmg2Ar904uwgRUSpULlGXo0trSkdqs5eE+wa01WrNI4sG4XdvLMPna7U3RLe8uRwHje5vuOkh6pGkVKZvBmC8GdDZoPtgaqz+XDjwJ8DqdyPLK98Cmu/Wgkkt+jI33afY/cfA97OlOP+2RyAhsEqOhNTdVA0szEZDVNlUdXMHykya3urpS6aix73P4CJMHl6KpaHZj0ryPPjpEWNiHtOMEAJlhTmobDBm/ewzuAit3nws3FIHALjwoOHwhANxQncDbJaZVLVSXS6P6lEVKzMpWbO56XomFeYab20Gl+Thi98cg+XbGzBtZD/1ZtNG/4JslOZ7OnthdfiD2FHfhuH9jTNLUd/Q7gvg4c82KOsuOniEVk5auxHY+rX6gCk/TOPojIQQ+OcPpuKus/czZO055nJrjfVrN5pufi5wfFSJm2a7R82IGici5W1/eHMZDhrVHyX5FtcIbwuUUmdPPqpaglgaHIPJrqgx7H0K+u3wGAL4SraSTRPuLLcL+w8txbebIz1zF2+t18qkyTFmJpFRMKj1OnrzWuCfk4G/7wO8djkw7yHtIul1llEUkAILg3vhKf/J6JC62TVca7B4a33yxx6iDyaVtKv9kLbJMjQjX23CLQMsdSOi9Nm5RF0ePCXyvcsFlE1Qt+9ejiXb6vHRysiboUBQ4oPlccyQQtSdeZvVUqesXC1LyMZGXb+kseVqaRJGHw30jwrEBH3A4ufQLz/bmJlUNMhw/KArC0vlOHwnx8KHLMOU8gN02T01zV3LTIouqRJC4B8XTMFxE8oxY8wAPHXZgSjNj7/5dliZRQ+motwsPH35gfj9KRPwp7Mm4bYzorIL9GVuZplJu5bpnigqmBSjAXeyZnNrjNEzKaw414NDxw10HEgKG6ObPlz/90Z9y/Pztij3GHkeN646KjRT25IX1Z2HH5KRWdzMdDmQFFY60nLTkuA4w7pBY6coy+Ndkfuw3Y0duPM9XSA6Woeut25OEaqbO3CX7yK0h+4n6wrGAPtfgOP3rTA8XDnHDWVu6oxuU0eWKsuLttZZj4tMMTOJjGb/Hvjmkbgf1ijz8U1wApYFx2A5RqN5wGSMHzMKB47qj/ZdTyNn3r2d+x7iWoUHNtVAStmZLp4s7b4AGqPSnt0ugZwmNZi0PdQYbkVwFIa5o1Lqty+wbbxJRJQ0W9UGloZpzysmApWLOhc/n/M5Ll1tvNn6ZHUVrjvG+GaOqMeJKkEAoGUlxXiPoM9MGqNrmgyXC5j+Y+DDP0TWffMoBgw6wrrMLYq+B5JbN56yLjThbmj1KTNBZbtdhk/DRw8swJOXHRjzWE6YTQ8OAEU5HhTneiI3w9GcZCZt/1Zdjr6GxWrA3WHWgDvxzKSiRG+adcaUFWJR1IefG/c046jx9tly1DvVt3oNWUmXHDpSKxf1tgILnlIfkOGspKTqZx1M2on+hnVHHnIQsCkLCGrn5yBRhwFoQA20/kuvLdyOHxw4HNNHGR8L3ezbyCnCnqYOfCv3wQneezBa7ML3z/gBTnN7cMXhY/Do55FsJZcA+kVnPMWY0W3qcHUyplQmOvRWzEwila/deDE0UScLMScwEY/4T8N13htwasefcaLnGcw7+CFM+dGfcf8tv8UrvzwdfzprP5w5ZShK9j5SefwJrgXYVVOH1buS3zl/S406nWRFUQ5EtVrX7y8ZBQBYFNTVDm/TvTEiIkqFpt1qiYhwGXvDlO+rLFau12UBhCzaWqeU2RD1WM0mwaQYNugaWY81K5+fcpE6I1HzLly950+oMGvArRPQNWR16zKTBhaqGUNOgklmfZ70x00mq2CSWUlYJ0NmUlBd7mgGduv6Yw6LCn7FzEwya8Cdmp5JiWATbgq778M1SklVfrYbV4bLTxc9C7RGfTidXQRMPCu9A0ylfqMsN+lnx/7lCeNx8F6DgYpJyvqz+m9Wlv/+0VrzA5oFk0LX1W2yAl8EJ2NAiRaUKivKwR1nRjIqz5o6VM3izNf1n2xTM4+mhWevDFlR2YB2n0ngnCwxM4lUOxYCATVF2yvdWCbHYEFwPBYH98Ky4GjswEAAArkeF07afxB+O20YDhs7INL0Um/EDO1NYehTx2LRhh+6P8HL8/dW06qTQN/Ye8rAILAjaipKlwfFw/cF6vYYg0lb52k9G5KcLUVEpFj8rLo8ZBqQV6qu030SOETUmB5KSmBlZSNmjB2QxAESZYBZZpKN5g4/NlVbl4t1yu8PTDoHWBopQ5nU8JnS8DEg3HAXmpS56TKT9DEffZlbtYMyN7sSt1QoLzIvc7MtfdE34NZnJlUuAmRUgGnAOO3nHBajZ5IWTNKXuSVhNje7AFkX6DPd9L2uqG9YvqMBL3yjTnhx3THjtPO/rR744h71AQf+xDgra09mUeZWI4vQgUjw5pWrZuCg0aHrwKjDlXL+nw6vxJO1kzuXv95QgxWVDZg4RD97ra6XXU4xqqvUQHNZUeQ5L5kxCgeN7o+GVl/kucNyi22PXV6ci6GleZ2Nt30BiRWVjThgpBogI2vMTKJO7b4Alnw9S1k3OzAd+3c8gXO9t+Mv/oswK3gQdqAM00b0w1/P3Q/zbzke//zBVBw1vsw6kARon1Dp0j1/nfUKvvx2PnY3xv/mwc66KjWifWjBDnWH8gmYNkZLZf9OjkGHjHrj0bDVODsJEVEyNe0CvrhPXTf2GON+xeokAYMtgkkAsHJno+U2oh5D33y70D6YtHhrHaJjPWPLClBslZly9O+BHOubu+qcEZGZyKLoJ+8yZibFX+a2UZ9NleIG+uXFFmVu8WQmSV1m0vb56vIwdWrwWJlJO+rbsKpaF3jzxfd+sN0XMPy8o2fXS4Zxuh5cG6qYmdTX+AJB3PLfZYhOUhw1IB9XHDFaW/jyb0Br1P/PngLgkGvTO8hU02UZhe2SkeDNtUePVYM5ow5X9h1UtxCHjFGDPYu2mPQosihzi1ZWqAaiJwwqxsFjBhhbp+gDeu3G90rTRupL3dg3KR4MJhEAYHdjO05/YA7qV32urP8sOBnt0N6EFOZk4ZIZIzHrxiPwxrWH4YIDR8SXTnzwNZBRjTTzRQf+7vonnvrf0qS8hrB1usykiS7d1NmD9sdhoU/wO5CNeUG1lARrP0jqeIhItbm6BX+dtRrPzd1s6EfSJyx+Tv0EPrsIOOAy434lw5TFwUKbcWTMwALccuo+yrYVlbpP8oh6opZqdTlGZtL8zeqb/gPN+m+E9RsJXPgfy83bPKNN1xt6JhkacKsBqBonZW66YFKqZ2PU93UKsw0m6TOT9A24t+mCScN1/Z1MeiZNHqbe2N33yWZ1hzgzk1ZUNsAf9fsZ0T8/8UbDOiP6Fyi/812N7YY+TdS7PfjJeizdrv4fe+sZE5GTFZrBTd9n9vAbgSJj/7UebeBepj3ldkYFk1z6QM6IGVDSP6tW4qRR6n2j6QdhumCSz1OIVm/k+pPtdqE4z+F5nqPLTNI39wYwdXipssy+SfFhMIkgpcSNLy3BhqpGTHOp9avzg3tjr/JC3HnmRMy7+TjcceYkTBhUbHGkGIoHQ5x4l7JqsmsjTlp8HSqr9lg8KH769PHhvs3qDoP2w+iBBRhcokW1Pw5OU7evnZ20sRCRqr7Viwsem4t/f7YBf3xrBf78fupmUAwEJWYt34W3luyALxCM/YB02DwHmPNPdd0xNxsCRwCA/AGQUWUgRaINRWjFBQcOx8Sh6nV4xQ5mJlEvoGuOGiuYtCBqSmcA5s1co406HBh9lOmmDcK8jCMo9WVusTKTumGZm0VmUmGOzQeCLpsG3FIam28P0wWTTDKTfnXi3sryvK1qj8t4eyYt2abe4E/W3RQmQ3aWCyP7q83R9Zll1Hst3lqHBz9dr6w7ZdIgHLN3uXYevPdrtT1I0RBgxs/SPMo0EAIYdYRh9e6oYFKuR3fNyCsFBu+vrDrIpb7nW1EZO5jUJtTzb2BhtvPJmwyZScYP3vSZSQu31EHKPvhBZxcxmERYtbMJczfWYILYimIRmUmj2V2Mv151Hj78xZH40YxRyfm0Z9olCIw9Xl3lWgffs+dqzRwTJKXElhr1TVpJyyZ1p7K9IYTAoWO1pmyfBHSzt237BmhV36ASUXI8/NkG7G7UbhiminU459sfwPuPaSkJ4t7+zgpc/fxC/PylJbjg0bmGKaTTqno98PpPgZmnA96oN0ruHGDyD8wfIwS8+WoPl2HuWpw9bSgmDlbfIK3Z3YRdDcktGSZKu/ot6nKBsSF2mC8QNHyCfOAoB30ujr/N2KsHwMrAcNPdY2UmxTubWyAoDROFjNXPQJdkVj2T7DOTdLcI0ZlJtRvVsp7sQsOEAWY9k44cX4Yjo2ZCa4ea1SX9bUAcN3FLttUry1NSEEwCjJlj+swy6p2qGttx7QuLlGvAwMIc3HX2ftrC8teBDf9TH3T8rUC2GvzoNUzK8bfJyPmsD8oAAEaqpW6jW5Yoy6t3NcGv/7BPlz3UDPXnWWYxoYApQ88kY/Bq38HFyMmKXO92NbZja22rYT8yx2ASYe5G7Q3BES51pqDCcUfggFEm9aeJEALu7z+D6tLJyuqRzUux4y9TcdODz+OXLy/BE19uxLyNNahvjf0JX7TqZq+SClmQ7UJW7Tp1p4HaJ2OHjdNK3XagDKuCIyLbZRBYq/aOIqLEfbamCk98qU3hOl5sw8zsv2Kiawuy6zcAL11kLJtIQHOHHy9+GylxXbS1HjP+/D9c9dwCvL20Em271gLrPtIaZ8ZDSmDpS9qnkRs+1SYt+OhW4JHDgX8fBnz1LyAQKoForgLWfgj892rgoQOBZa8Y+44cc7PatFZne1DdduqwDpQX5aIk32O4cfpo5a74XgtRdyIlsH2Buq5iX/N9AczdUIO2qFl3yopyMKK/g5u4odOA856GjCrjCkiBhd4Rprvrg0n6zKSyohxlzo6dDe22vSC317XCG3XzNKAgGyX5ye3zo6d9km9c37/A2COqk12Zm75f0tBpxh5LbvNA1d/On4zh/fMAAAG44ZeRWxEhg2hobjN9nJmlhmBSahoej2XfpD6nqrEdP3ziG+zUfUhz73n7a+dNSzUw63fqg0YeDux/QRpHmWb7nQ8MPaBzUeaWom3vM1FelIOfHjEaM8aYTAKi65uUv+UTlEWVBnv9QWzQz5Coy0xqDOYpy3EFk/R98jqMmUnZWS5Dw+2vN1j3qCQVZ3MjzN1QA0DiHPeX6obRxnTGpMgpQsGP38LSvx2HyWJD5+qhqMIf9tyEy3b8Fm8sjsyyNnFIMU7dbzBOnjQoZpPKrbXqBWlKvw6IhqiLUnYRUDwEAHDYuMh0kR8Fp2Gf6N5KK/5raBhORF23YU8zrn9xMYISGIJq/Cf7LhSLqE9+gj7gvV8AV32ZlNkU56yrhi+g3gS2eAOYvWI3OlbNxmmeewEh4XflwjVwDFz9xwCefK3PR3Y+AKFNIZtbrKWwV6/T/rVFZS3Of9z4xB8tBz76I1A0GGjaaT/I428DDvu55eY2bwALm/pjbNSP44ziSLr9iRMrlE/m315aiR/NGGX/nETdVe1G9fzKLgLKJlju/sFy9fw6du9y5x9+TTgVHaf/G/63bkChaMejgdOxtq0QUkrDMQxlbvpKjmw39h9WqgQ2Pl+7B9+fbp7pZOyXlNoSNwDIcrsweVipcr2YMKgI+w62aVugn12yfkukL9I2fYmbrvk2YDmbW1lRDp7/ycH48TPzsWFPC9qRjUJEbtiPv3c2Dtp7JH527DjsYzO+6uYOJXvA7RLGWaGSRP/ek5lJvVtlfRt++Pg8bNZlEF5+2CgcMyFU3vbWz9TZJ93ZwGn39+7ZoLNygJ98DGybBzTvhhh+MG4vHoLb7R4z6jDtZxMuBazdiNMG7cTTzZHA03fb67H3oEhPXX1mUn1QDR7pS4ttmWUmBYOGC/mhYwcoAaS5G2pw4UHmHzCQiplJfVyr14+v1ldjhmsl9nZtj2wQbmDSuSl73rzifnh42L1YGhyjrC8RrXgu+y84xLWyc92KykbcO3sNjvvb5zj5H1/gzcU7LJv26lPHD8rfre4wcK/OC31FcS4mDtEuMh8EDlb32/CJdiNJRAnbXN2CHz8zH03tfuTAi/uzH8ZAYVInv2sZsO7DhJ/PFwjihW+2WGyV+G3Wi3AJ7RqSFWyHq2olsPpdLXNo6X+A+U9ogaLlrwELngIWP6+Vv7bFUf5qF0gq2we48GXg8F/YvvH87+Id+J9vP2XdiJpI0P+kiWoJ3PzNdVi23fipG1GPYAhQHGDMdgnxB4KYvUL9//2U/QaZ7mslZ+r3cWTgYUxv/zfu8f8AXn9QyXQKM5S5mZyzR49Xezt9vsa6D+TqXeqn7uPKU1viFnbX2ZNw0Kj+mDysBDcevxdeu+ZQuFw2N76D1GsPKhdHvtf3SxpuEkzKsX5dIwcU4L0bjsDPj9sLXqhBp6C3De8t24mLnvgGtS3W2enzNqqZA/sOLkaux/zvJVH63xGDSb3XttpWXPDYXEMg6bgJ5ZGJL+Y/YZys54hfA2Xj0zTKDHK5gJGHAhPP7vxw3lZuCbDXicqqM/GFsrxI3/BaV4pW41fLdOPKTHJ7tA8KO0nAazx/Z4xVs6q+3lDDvkkOMZjUx/1vVRXafT78KutVdcNeJwKF1r0KkuGCIybhQu8f8B+/WoNbKNox03M3fp/1An7k/hBXuN/Dma45ONy1DAW7F+C2l7/Eyf/4Ah8s24mg7k2ePpg0VddQXJ8yf8ze2mtcJUdgQ3BwZEPQD6x6N8FXSERfra/GaQ/MwZaaVkwW6/FO9i042LXa+gFf3BtXzwy9hlYffvafRfhyXbXp9p+638M+rm1dPn5CBo4HznsKuOZrYO+TbXfdUtOCv7y/CnOCk9AhI0nEonZjZ5PisWWFOFT3BuiWN5ehw2+8ISbq9tbp+qbpGzpHmbO+Wgk0FOdmdfZBdEoIgez8UlQjks1iFrwwZiYZAzDHTFDfL32xbo9l0//VO9Vgkl32TTJNHFKCV66egbd+djhuPH587D6Yg6eoyztDM+92NAO7V6jbzH5Xo44E8qN+J7ryn1yPG784YTyKS9RsonyhZSnVtnhx34drLIenL0PRXwuTafQANXtse10bbzR7oWXbG3D2w19jW61aann8PuV46KJpyHK7tPL4D36rPnDYgcARv0rjSHuY/c5XF6vfQzEilSSLt+o+vNeVue3xquW4cQWTAEczuu0/rBT52ZFgdHVzB9ZXMWjsBINJfZiUEo9/uREXuD/DdH3Q5fBfpPz5j51QgX9dcji2zPgzdky+QdmWI/y4Kus93Ol5Bn/wvIB/Zj+M57P/gtdzbseS3KvwZP1PkPXKRXjy3l9i3rdfd9586humjWvXveEZMUNZjLwBFHg3qG7DijcSfo1EfZXXH8Q9s1bjR09+A39HC36X9SLeyL4V4107lP3qpO7T6+3zsevzx+N6oy6lxJpdTfj3Zxtw3N8/N2Qs7DO4GGvvOAELDvgIt3ispwbviva8CmypOB7Vk35sfMPizgaGTAWm/xj4/rPAtfO0jE99nYxObYsX5z0yF00dfrQgDxul7tO/+khJ7hVHqNOZf7e9AXd/YBOsI+qOWqqNH+CMMTZ7DXt+3lZl+cSJg5CdFf9b2lJdr6K6FmOTfn1MyCwzaf+hJUr/oaZ2v2V20irdVNjpCibFbcgUdXnnUq08ZPu3au+3/mPN+765s4AfvgLsdRIw+YfASX82fZosXRlKASINzF/8dqvlzGnzdMEkfWZBMpXme5SZqlq9ATS2+VP2fJR+n62pwgWPzTU00D9l0iA8fNEBWtbb9gXAK5eoMxtmFwLnPGbZI4wA7H0KUFjRuej2t+JH7o86l9fsbkJzR9T5pAsm7epQg0lxlbkBjmZ087hdOFA3G+jcjeyb5AT/8vuw5+dtQdX2jXg+R3dzNf4UYMTB5g9KsuP3rcDx+1YAuBMoHwh89H+OHjfCtQcjsAdoWwi8/xRqPx6CfpO/h7aqGQC0dMhs+FDe8J36wOGHKItThpeiX74Hda0+vBs4BD/PigogbfwcaKkBClL3BoWoN1q7uwk3vrQEK3c24gjXd7jL8yRGuIw3Vjuyx+CMxptwj+cxHOeOlFAM+uwmzP7iLWwbehoGTzoKx07dG3mhT4zafQHUtHixpaYFG6qa8d32Bny5rhq7LBrejuvnxvOH1yD7+TMxcOvXyjYJgVvkNdjiK8EQUYOAdMEtgihEG/qLJgThQovMgRcebJKDsUEOQa0sQi68qEMhJATQLoA6QGwF/nTaNbhoWDXgawVKRgD9RgFZNg1uTayvasaFj8/DnqbIG9rtciD2QdTNc/1WYNh0AFp25UkTK5QA2tNfbcbBowfg5Enxlf0QZcz8J7S+aWH9x2qlFCa217Xik9VqwLirvS30DahrTSb9iDWbG6BlK508aRD+803kPH1t4fbQ+5uIdl8AG6vV3o5Kr5DupN9orXltuGFtRyNQuQhY9Y66n+5DOsWwA4CLXrF/nmw162dwnh+rQ58LSgl8uHI3rj5K/dBhV0O78nPMcgnDjWAyCSEwpCRPec6djW0pb5xOqRcMSjz11Sb85YPVhnP9rClDcN/5k7WMpB2LgOfO0f5/7ySAsx4G+qstO0gnKwc46Ergkzs7V13reRf/CRyLOhRDSi2Yd9r+oQ/OdJlDle3qeRZ3ZpKDGd0ALbvx87WR96pfr6/BJexDGRODSX3U+qpm/P39xXg6+x9qE1xPPnDqPZkZ1GE/157//V/H/dD+3kpg/uO4Ha/jK9yDJuTjGNcSZPmj3rQVlAMDxiqPc7sEjhpfhjeXVGKdHIY1wWGR3lEyAKx6G5h+eSKviqjPWLy1Dk/O2YQPlu9CabAe93uex9nur8x3nnox5EF/BJ5YhntaL8ARru+QLSKf9p0UnANsmwNsAza+NwjLMQ7LMBYLfGOwUo5EB/RBGolCtKFC1KFC1GGoqMbpectxuH8JXO+YTPGalQdx9r9x+4QzsWRbPVbvasKKHQ34ekMNKuvb4A9YZ0a1wjjNtpTALe+sw+zxZTh76gSMzC6AaGtBUDZjbFkhSvPtg0o1zR34xStL8cVaY9DNVzgUaFsUWRGVmSSEwD3nTsaKyi+xvS6Smv+b15Zi4pBiDHcyuxVRJrXWAnMfUtcdcJllP7F//W8dou/59h1cjGkjSrv01P10waQaXVYCYFLmZjGuc6cNU4JJ/1u9G7UtXiVgtb6qWblhHdYvD8W53TQgIYQ2E9Oa9yLrFjxtnO12n9MTe55sNVB08ZT++DQq7v/5mj24+ij1vdv7y9SedPsPK0FBrLK9BA0qyVWDSfXtmDCom2aVkSM76tvw29e+w5z1xrL4q44cg9+ePEEra925FHjuLONMYKfcA+x7ZnoG29MdeAXw9QNAez0AoACtuMPzDK73aZUpT83ZFAkm6XrWVrar12nbWSjNOChzA4zZjXM31iAYlPa95YjBpL6oqqkdVzz9Ne7DPzHFtUHdeOwfgNIMdq8/6KfAiEOAxS8ArTVaA0eXB2iq1NISW2shq1ZBSPOeIBWoxeXuWfhX4BycnaW7iZ14lumb02MmlOPNJZUAgHcDh2Bv12uRjes+ZDCJyIY/EMSsFbvw5JxNWLy1DpPEJtzq/hxneeagWJhM8Vw8FDj9n8BeJ2AYgDevK8bt75TizrWX4E7P06bPMca1C2OwC2dgDpADeKUbDSiEljMUQBYC8AiTa4JVFULxMODC/wCDJ8MD4MBR/ZVPtYNBiYY2H/Y0d+Dr9dV4e2mlsUGkhS/W7jEEhHKyXPjpEWNw/XHjkJNl3iD2vg/XmgaSBhRk48ApU4C5UdkADWrPp5J8Dx64cCrOf2Qu/KEb1cZ2P3724mK8etWMLpX/EKXN+79W39zn9dOCSSZWVDbg1YXblXWXHjrS+SxuOmW6coma5tg9k8wykwBg2ohSjBlY0Blw8AUkXl2wDVdFBUJWVqo3Md0+GDH1YjWYtOR5dXtOCTDWuhzREV2j7v3L1VuTBVtq0dzhV3o8vb5I/Rs4Yd/UZ2EOLlGnJ9dPGU89RyAoMfPrzbjvwzVo9arvHYQAbj1tX1x2WKiEfMtc4MUfGEujjvwNcPCVaRpxL5BXqvWV+uiPnatOd8/D7MCBeDc4A4u21mPW8l04eWKF9gFDlO3t6rlXkhdnAN5Q5mYeTJo4pATFuVlobNfePDa0+bByZyMmDU3NLJG9BYNJfcyO+jZc/eQX+L/mP+NY9xJ147gTgIOvzsi4FIP2A06523Kz8LUBe1Zj/hcfoHXFBzjKrZayXZ41Cy8GjsGxrkXqA3XNH8OOGl8GlwCCEvgkOA2/QlQwafsCLeWgN0/1SRQnKSVW7mzEW0sq8faSSniatuB01zz8Oftrm+bWAjj4Ki1gnRMp6xjePx9PXHogdjfuh/c+PxxTl96GIX77BtnZIoAydHHWstFHAec+YTvBgMsl0K8gG/0KsjG+ogiXHTYaXn8QKyob8OmaPdjT1I7aFi/qWnxYtbMRTR32vTM6/EE8+Ol6vPDNFkwaWoJstwtD++XhuH0qcMS4gRAC+GjlLsPjTp88BH8+exKKNqhlMag3/nymjuiH350yAX96b1XnuqXb6vH7N5bhr+fup6XpE3U3y14Dlr+urjvsRmNZArQ+bDe9+p3Sn39ceSHOnTasy08/QPcJd3WLMTNJX/pi9Sm1EALfP3C40rPs+W+24IojxnQGoF7TBUH2HdLNg0l7nQgUDdE+0DOz7+laCUsidJlJZTk+Q1Duk9VVOGOylrWwamcjVkQF5YQAzp46NLExODC4RM1I3dlg8mEJdXsrKhtw8xvLsNRk5tPi3Czce/7kyEypS18G3r4eCOiuC4f/Ejjm5jSMtpc5+Gpg2avArsh9212eJ7HKOwIb5FD84c1lOGhoNvpHlTzLrFzsblHfv8QfTNKXudWb7uZ2CRwyZgA+XBkpo/56QzWDSTEwmNSHLN/RgJue/hB/8f4ZU9wblW2yYhLEeU9aTsPbrXjygCFTMeGsSdhvyWSU+eowP/e6zs39RDP+nf1PZEenJfQfAww9wPRwpfnZmDaiHxZsqcMaOQxtMht5IvTpZEsV0LgDKOn6m1Wi3iAQlFhZ2YjP11bh3SXbkLfnO8xwrcAj7oWYkrPB/sEVk7RspFCfH9NdinPxvdPPA049C94Nn6N2wesQ277BwLaNcMN8ViTHioYAE74HTDpH6+/RheBwdpYLU0f0w9QR/QzbVu9qxM1vLIuZvVTX6lNmmXt27hYcNLo/fnnCeFTrMiIOGtUffzt/spZVVDpcPVCDebDtJ4ePxryNNfh4VVXnutcXbceO+lb868KpKC8ylucRZczulcC7v1TXVewHHHKt6e5/nbUaK3XNq28+dUJCgdKBut4b1U0OMpNsLh/fnz4cf/9oLbx+7Zq1rbYN7y/bidMnD8GSbfX4dpP6ifvJE7t5XzN3FnDSn4DXfmy+fcb1iT+HLpiEjmYcOb5MKSm7672VOGR0f5QV5eDWt9SJVQ4fNxCDSlJ/bRtcqg8mMTOpJ2nu8OOBT9bhiS83GQLEAHDw6P64/4IpGFKap2UhvX8T8N3LxgMdegNw3P/xQ+auyMoGzn4UeOwoIKBda0tEK2Zm/xVnd9yBPc2luP/tebgz6iEytx9kVA/+/Gw3PPFe83NL1WWLYBIAHDZuoBJM+mp9Da48cqzl/sRgUp/g9Qfx8Gfrseqzl/Cc+3EMdKlvxmS/URAXv25MA+zminI9GN4/D9tqgcf9p+KnWe93bjPMTrf/BbYX/mMmlGPBljr4kYXlchQOFFGPX/A0cNwfLR9L1Fu99O1WPPjpejR3+OH1B3Ccfw7Ocn+FS12rUZTj4FPZ/IHAodcDM64D3A4/SXJnIXv8cRg0/jgAgOxohr9yCeT2hUDlImTtXAQR1TNIkZULFA0CigZrXweMA8afDAyZFnMGtURMGFSM164+FJ+srsL8LbXYUNWM3Y0dqGnuQGWMG45vN9XiB4/NM6x/8cpDIuU0xbpP3ZuMWUyAlhlx3/mT8b1/zcGO+sjvZ97GWpz54Ff477WHpeWmiyim+q3A8+eoPUjcOdqsSCZN61/4ZguenLNJWXf65CE4dkKFYd946DOTakwzk9RlqzI3QOvlccbkIXgtqhTv9ndWYmi/PPz61aXKvoeOHdD9M5MAbQbKqlXAF/fq1p8HlE9I/Pi6Mjd4m/D96cPx7NzNnb2xdjd24Oj7PkNhThaqmtTf0Q+72Hw9XsxM6pnafQG8PH8b/vW/dahpMQaLi3Ky8NtTJuCHB43Qsg63zgPe+KnSm7DTMX8Ajvw1A0mJqNgXOO5W4MNbOlcNE9V4Kvse/MD7x/9v787j46rr/Y+/PpN9T9qk+97SlZa2tLS27JusBVkUf2BBUETc0HvlJ6joFe9Pr6Jc5cKVRURAqLIIAoLIUtaWUorQlrWl+5626ZKkWb+/P86kmT2TdLYk7+eDwzRnzpn55pvJO2e+811Y9sFWCGjjb84P/gCvvLO9ksAbOh2ovibqoXPHBM+b9Na63Zo3qQNqTOrhFq3eyf88/jIX7L6Ta0LnEAJa+0/Gd8nD3huvbmjCgFI27KrnTy0ncXnW02RZhElzLQumXhzzceYETLq2tHUcMwMbo165yZtH6shLE1VskYz3yY79fP+xFQc/wftJ9h+Yn/vPDs7C+3077BRvro3DPt3p1czCHi6vmOyRR8PIo9t31u2C5gavl2JWrtdQZVlJbTDqiM9nAatTtttV28gtL3zMw29tZN+B+JaS/s4pY4PfsBZVgfnal+Ou3wXNjRHrtrwwl/u/NIuL71wc1JC1Zc8Bvv7AMh68cnbnP9UTSaTqVV5D0r7gSZQ5+cfeG40QC5as5wePrQjaN6A0nxvPmXTIRQnrmRRhAu7QXgwdzc901XGjePxfm2jyT+Jfvb+B8257Pey4Lx0zsrPFTZ8TfwBjToZ3Fng/t34T4djvJuaxI/RMmjiolCuPHc3vXmrv9VrX2BI2v82c0albtVJzJnUvdY3NPPDGeu54+ZOwBsg2px8+gB/Pm0T/0nyvN9ILP4UldwIh7yWy8rze1VM/n/yC9waf+hpUfwjL7j24a7JvLbfn/Jq7W04POrQxpzzo69IkNyaNriqmrCCHPfXeULv9Dc2s21XHyMqiqOf0dmpM6qGWrd/N7c8sZeL6B7gz6ykKs8KD1I06Ad9n7404N0F3MWNEBc++t421biCPtR7N+VmvhB90+Pnhw0RCTBpURl62j4bmVv7YfCqfz3qBcguYp+TJa7w5VsadHvUxRHqSv7696eCbqFn2PvOzO2hIGjYHJp8PE86B4qrkFq4weUtAJ1qfolx+dPYkrv30eJau20V9YwvNrY7bX/6EdzbUhB3vMzhj8sCQnVleL6/a9uFr7N8WNddGVhbx+NeP5lsL3ub11TsP7l+6bjc3Pvke/zFvUpcnLBY5JOtehz9f4i2wEWjml2H2V4N2NTS38JMn3uNPbwT3EMjP8XHn/BkdrpAYj8qiLkzA3cHvzph+JXz1+DH89vmPox5z5pSBnDAu+rxtGWnYbG9LtNDGpEbv2uuakw9j2frdYUMD25TmZ3PjuYenLMvCeibVHMA5pyzNMHsPNHHfIq8n464IPZHA3xh97uGcMrE/tDR5i/48/xPYH6HXb79J3jyLERq6pYvM4MybvV7WHz97cPfRWSs5Oit4GOuBnOBRM52eLwm8yb8DhawWF1w0Y9Kg0qBrpxWb9qgxKQY1JvUgDc0tPPXuFv7x6mKmbf8rN2U9T0l2eDfcVsvGd8L3sLnf9sbDd2NHjWzvUfTfzecxz/d6+KpOc77e4ePkZvs4Ymg5S9bsYit9uaLx3/lLwc/JavU3wrlWeOiLcOkTMHRmIr8FkYyzp66JB5d4b+Cm20f8Kvd/w45pLazEN/JYGHms1xNJ84rFVJCbxTGHtTeyHTWyD6fe/HLYxe4Pz5rImH7FoadDSf+QxqTtMRvJq0ryuO+KWXzxnjeDVom7d9E69tY38YsLjtAqb5I6zQ3w0n/Bqze397BrM+k8b4ntgDfl72yo4fuPLWfFpuBh+dk+47cXTWPykMQMy68sCRnmtr8xrIGgOaRnUqxhbm2+dsJo3lyzi0Wf7Ay7b/Yobz40NUL4hQ1z8yZIyc/J4v4rZnHzcx9x96traGhuf92cMK6K7585gdFVEbIyScoKcijIyaK+ybvGrG9qoaauiYrOLlMuSbFq+37uX7yOh9/ayP4oi2IU5GTx5WNGcuVxoyn2NXm9kF77LeyJMnR+9tXekKwcDQ9PuKxsuOAP8MezYPPbUQ/b5YJ/x7vWmBTaMyl6YxLA4YPLghqTVm7ey9n+BQAkXPduSRAam1t5bXU1/3x7NY0fPMO8lue5PWt51J9sc9Uksj9zKwyaltqCJsmkQaVUFOawu66JDa4/v285nauyn2w/YOI5MPCIuB5r4sDSg5+AveXG8Y8J/8kZ713bfuHbXA8PXAiXPwtVYxP9rYhkhJq6Rr5871Ia9+/mJ9l/4ZKs5/CFDh+d9z/4pl2ieQMOQWVxHg98eRbXLPgXH2zdR0leNjecPZELZ0RpICoeACxv/zrSJ6ghsnzGzZ/15lDaurd9SMZj/9pMls/HTRdO0RtaSb61r3oTbVd/GH7f1Evg7P8+OER1/c46bnnhYx56a2PYoXnZPn5z0VROTeCk1YW52RTmZh0cPtXY0sreA81Bb1ge/9emoHOK8jpeqCQvO4t7Lp/JjU+296waU1XM/DkjuGjmUA01DRQ2zG1f+13ZPv7vaeP5xoljWLFpL80trYwdUEJl8SGuINcFZsawPoV8uK29fJ9U13KkGpPSpqG5hWdXbmPBm+t5bVV4w22bgpwsLp41jCuPG0U/2wdLfgOLfxf8AU2gihFw1s0w+sTkFFw8ecVw8SPwh9Og+qOIhzy7JvgDt1Q0Jk0Kmctu5eYurh7cS6gxqZtxzrFuZx2vrqrmvQ/eJ2vtQo5pWcINvnfJtyaIco3TlNeHnFN+SPb0S7vHim1xysny8dNzJ3P9X703Wa3H/ojW4pPxbVsOVeNhWuy5kgKNqgruwvhc60zOOOMmeCpgtZn63fDgRXDli91uwnKRWBqbW3nwjbUseu4RLm1+jlPylpJnET7dm3oxTP9C6gvYA40fUMrT3zqGdTvrGFCWT35OjGwuDploOMok3KH6Fudx16UzuPTuJUGTjz6ybCPHjas6uNy2SMKtXwwLfw6fvBj5/mOvhROup8XBG6uq+dMb63l6xRYiLLTEsD6F/O8l05k0KPF/d/sW51K3q70Xd/X+hoNvWJ5evoVHlwU3Jp0U56TfedlZ/PTcyXzv9AlkmVGQ23OuvRIqbJjb/rBDCnOzOWpk+oc3j6oqCmpMWlNdy5HDw1f4lORatX0/C5as59G3N0Udygbe5Nrz5wzn8qMG0Hf7InjmF/D+kxCw9HwQXw7M/ZY3yXZOQeRjJLGK+sIXHoO7T4vYQ2x3Inomha7m1mFjUvDfmZWb90Y5UiBDGpPMbChwM3AKYMBzwDXOuSj9DnuPA00tfLh1H8s31rBx9Uoa173ByPqVzPa9zyU+/wVOjOuTxvy+5B79TXJmXgF5JakpdIqdOWWgN+4Z/MM2utZrKLS79OrqWvjcFd6btpd/0X7HrtXwzxu8yfhEujHX3MjWla+wetFj5GxdxrnuEy61uuiZcuQX4YybUlrGns7MGBHPWPySkDewb98PR14W14cDhw8u49Gr5zD/7iWs21l3cP83H3ybvkW5zB1T2clSi0TR0gSrX4BF/wNrXo58TPEAGs6+ldeZwstPvsfTy7cG9ZwLdcbkAfzsM1MoK+zCm4g4VBbnsSGgMWnHvgZ27GvgoaUbeWRZcA+pcf1LOG/64NCHiKk4LyMutTNX2DC32sjHZYDQeVM+2RHe8CXJUd/Ywt+Xb2HBm+t5c23sxoDK4jyunFHOJZUfUbj6frjteWiK8brKyvM+fJ7zTejTjSbG7ynKBsP8x+Cuk70FRgLUuOD3ruVd+TsQ2jPpQE3Mw0dWFpGf4+NAkzcyZVdtIzv2NVBVkvoekd1B2v/CmVkh8ALQAFyKN4X+T4EXzWyKcy5z/6ok0IGmFtbtrGPNjr1s3bye2i2rcNvfo3zfx4y19cyzDZSa/01AHD+1A/2mkT/7CnIPPx9yC5Nb+AyQiLk/QnsmfbJ9vzd3wgnXw56N8M4D7Xe+9UdvwtABhx/y84okXUsT7F6Lq/6I2o0rqFn7Dq07PqKyYQMDOcDB6Z6jjXjqMwrO+m8YdVxqyivhSkIm5d68DP4yHz53f1zDDYf3LeLO+TM44zevBM0Bc/Fdb/DKtScwtE/P/zshSVJf4/U++vg5+PgfULsj6qHv9pvHbVnzeeG+Jhqb34z5sGP6FfOjsycGzTWWDH1DJuG+6I7FEY8zgx/Nm0i2hqglVoTV3DLVqJAPHT/Z0SveoqRFS6tj5eY9vLqqmtdWVfPm2t00NrdGOdox1LZzbr/tfKbPOkbUvYtv8UrCVmULlVsMMy73Vhfrpqta9xh9R8MFv4f7PhO0ezcJ6JmUW+T1OmvrkdZ8AJrqo/Y+y/IZh/UrYfmm9uFtH23bp8akKNLemAR8GRgFjHPOrQIws3eBj4GvAL9OY9kSorG5lZraBrbv3ElN9Rb279zMgZqtNO/dDrXbsdrt9GncwlDbwQm2gzwL6H7ZiV7RB4oGkzv5XHxHfI78OOcJknYDSvMpyctmn3/ivn0NzazbWef1GjjrZti4BHau8h/tYNkf4Yxfpq/AIgCtLbBvC27PJvavW8b+hhZ21bdQsHctBfvWULh3DcV1G8iiBQOK/Vs8XMlAbPp8OPrb6vKdbuPPgmeuC+6e/8GTsPFNGHpUXA8xtn8JF84YenBy9TZPLd/CVceNTmRppadyzpv8fddqb2W2Vc/BhiXgWmKetrh1Ajc1XcjS9eOB6MNSAMYPKOFLx4zinKmDUjK3UFVJx3PemMEvLziCOaPViy/hckN6ZkYY5pYpQj90XFOtxqRE27CrjtsWruLpFVupqQsejpZLE1XUMNB2Mtq3hfG2nsnZ6znct5781lrYg7d1pO8Yr2fv1Iu71eqwPd7oE2H6fFh2LwD1LpelreOCDintSmOSmdc7KXCOrPrdMa9rx/YPbkz6cOs+9eKOIhMak+YBi9sakgCcc2vM7DXgHFLYmPTGvT/A1e7wLpZcK+Aw14rDYW37nMNo9R/jvGP8XzvXCs2NZDfXkdtSS15rHQWunkLqKeMA/SLNP9KmC0PpW8mivt9UCsafjG/8GV4DkiZT7TIz4/DBZUGrr/y/v79/8BP7CWWXccHOH7SfsOQO3lm9gfpsb2ytAbMCVpeLKK8ETrgu0UWXHmDhwmfJf/+RTp2T11LLuJqXKWzegwEl/m1gB+fFcsBXBGNPI3/25djwucqUTFE6EObdAo9dFbz/96d4K85E7VYW7IacZlxWCwta2icW/fObG9ixr6HTRRpRWcQXZg/v9HmSXL97abX382xt5uSNtwGt3jWM/5rG8P6Na/Vfv3DwOsYCjwm4xslubaKiaSt9GzeS3xq+Smw0r7ZM4pbm83jDTYh5XEFOFqcdPoDzpw9h7pi+KZ0Yvqok9kpNo6qKuOGsiRw/rl+KStTLhE7BULsDnrm+a49VOhDmfOPQyxTFqMrwxqQbn3wvYY//f08b3+1W2Vy6dhfPrNiKA1qd82Ij4N9Hb7uPoqa2YWnePnfwK+9/zr9nb30T2/c1MBr4OkA2DLZqRtkW+lkNFRaloTFah6VAJYNg/Jkw6VzQtU3mOvPXUDqYPRvf4/KVU9lL8O9cl3omARSUBzcmPX9j+PC3AJfuq2FCdvuQu4pXclm8vID3K05gY/GUTj/9kIoCvji3Zw6hzITGpEnA4xH2rwQuTGVBBq99lCGtmzo+sDMSmFWNWUXUVU6hePSnyB4+C9/wT1GkSaATasrQ4MakZ9/bdvDfuQzl5Lwiyq39k6gjdj4d/ADbiK1koBqTJKKNH73DJdsWpPx5d7kSVhTOJG/cKUybczL5lWMOrqwkGWbq571P0/4RkiGLb4v7IQqAH445kQUftjcmramu5fevrul0ceaO6avGpAz00NINrN5RSzbN/DD/wZQ//15XyJMts3io5XjedodFPW5weQHHjaviuLFVHD2mkqI0zS102qQB3PbiqqDhn4W5WZwysT/zjhjEcWOrNLQtmUKHuTUfgMW3du2xBkxJamNSeWEufYpyD0763NjS2qXsjObfTx3X8UEZZuXmvdwVow6+kPsEY32deG+VsBgw6D8JDjvV69k7aJqubbqDrBw4/nuUAc23vgYbaoLuLi/s4uqJoQ1HgVOXRDAFmBL4WmwAtsHfNhbxQEvn5yA+cniFGpOSqA8QaSa1XUDEJkMzuxK4EmDYsGEJK0gHI2tTpj6rhLrCwbT2GU3x0CkUDJkC/SeSWzaMXAVhUk0bWh71vkZy+GPLp/lW9qOpK5BkjGTlTqpsc+V80jqI9b5B7CwaQ+6gScw8chYTxozh2FiriElmmXUVLH/ImzOpiwpzsxlcXsCmmvh7mEjqdTVz2nr1tJK664UPW4ewsPUIFrZOZWnrOJpCLi/zc3xMHFjKlCHlTB5cxtRh5YyqLEppD6RoJg4q5dGr5/DKx9VUFucycWAZYwcUk5etXEyJ7DzIKYo9QXIGGVNVzJLaXR0f2E11Nnd86f8V9uSVwYDJMHia1/No6CwNYevmbrpgChfevujgcMcBpflMGlTatQcrVs/SZMmExiSI3I4TNZ6cc3cAdwDMmDEjYW1ALskXXo3ksDergvrcPjTm98UVVpFVUkVRxSDKB40kt+9IqBhOQUEFmp0kPU4c358x/YpZtT1yV9pbms/lKPuAT2UlrluzdA/Jyp1E+qebSUN2KeV5Rk3eIHbkDqW2ZCS+ytGMGjKQqUMrmF2alxFv4KSLfD44/y5veFvdzo6Pj8CAb5w4hu89ujyxZZOEOtTMaU1k1+gA+10+a90A1rgBvN46iZdajmCbr4oBpfkMKMvn1LJ8BpbmM6SigNH9ihlVVczA0nx8GfOuM9yUIeVMGVKe7mL0TmZwxOdg6d3pLklcPjtzKEvW9tzGpE7nTiqvJ8wHRf28hoG+o6FynNeANGAylA/T0LUe5rD+JTz3neN48I31bNl7gC/OGdH1efSmXATvP5HYAgqQGY1Ju/F6J4WqIHKPpaTZPvkrbK7d6YWVGZjPe9Pl/9rMh8PbZ23H+HyAYT4fPvORl5dHbmEp+UVlFBSXU1hSRkFxGZZbTG5OAZUKuoyWm+3jr1fP4bn3t7Fzf+RJQj9ouZv6HS9Q2FgdtN8wZo3s4FOQ0IkmRfwOmzqXxUXf6fR5jiy2VBxJ4bCpHD+uH/nqZdTz9R0NX3nFWzmrsa7z51cM53PjhzJ+YClLD+FN0aByfeyRib5y7Chq6powgzfWfRtnBrRdy3jXLK7tmsYM8K5nnP8+7/rHnyMB10P1+f2oKx5OXtkASgtzGZ2fw7SCbK4ryKE4NzujG4skw51xE4w+CXavPbTHKUruyn8AFxw5hOF9C3l34x6cS+znStlZ3e93aPqwcq4/Yzw+//sbM8PweiyZGds3X8XephoMw/+f18xthO3L8vkYXJFPeUHAUKacfBg4DcqGQFEl+HSN05tUFufxjZOiD5eO24Sz4EsveIsptcZeMKJNU2srH27dx/6G9jmPZ1TMYlTJ2E4/fb/S2HPzdWeW6CDsdAHMXgBynXNHh+xfiFe+mGtRz5gxwy1dujSJJRSR7sLM3nLOzUj28yh3RASUOSKSesodEUmlWJmTCRPw/A2YbWaj2naY2Qhgrv8+ERERERERERHJEJnQmHQnsBZ43MzOMbN5eKu7bQBuT2fBREREREREREQkWNobk5xztcCJwEfAfcCfgDXAic65yLMgi4iIiIiIiIhIWmTCBNw459YD56e7HCIiIiIiIiIiElvaeyaJiIiIiIiIiEj3ocYkERERERERERGJmxqTREREREREREQkbmpMEhERERERERGRuKkxSURERERERERE4mbOuXSX4ZCY2Q5gXQqfshKoTuHzZTrVRzDVR7t01MVw51xVsp9EuZN2qo92qotgqa4PZU7voPoIpvpop2udxNHrqp3qIpjqI1jGXOt0+8akVDOzpc65GekuR6ZQfQRTfbRTXSSO6jKY6qOd6iKY6iMxVI/BVB/BVB/tVBeJo7psp7oIpvoIlkn1oWFuIiIiIiIiIiISNzUmiYiIiIiIiIhI3NSY1Hl3pLsAGUb1EUz10U51kTiqy2Cqj3aqi2Cqj8RQPQZTfQRTfbRTXSSO6rKd6iKY6iNYxtSH5kwSEREREREREZG4qWeSiIiIiIiIiIjETY1JcTCzoWb2sJntMbO9ZvaomQ1Ld7nSwcyONzMXYatJd9mSzcyGmNktZrbIzOr83/eICMdVmNldZlZtZrVm9pyZTU5DkZMqnvowsxFRXi/OzMrTU/LuQbnTTrmj3AFlTrIpc9opc5Q5bZQ7yaXcaafcUe5A98uc7FQ+WXdkZoXAC0ADcCnggJ8CL5rZFOdcbTrLl0bfBN4M+Lo5XQVJoTHAZ4G3gFeAU0MPMDMD/gaMBL4B7Aauw3u9THXObUxdcZOuw/oI8DO8egm0L0nl6vaUO1Epd3p37ihzkkSZE5Uyp3dnDih3kka5E5Vyp3fnTrfKHDUmdezLwChgnHNuFYCZvQt8DHwF+HUay5ZO7zvnFqe7ECn2snOuP4CZfYnIv9zzgKOBE51zL/qPXQSsAa7F+wPRU8RTH20+6YWvl0Oh3IlMudO7c0eZkzzKnMiUOb07c0C5k0zKnciUO707d7pV5miYW8fmAYvbQg7AObcGeA04J22lkpRzzrXGcdg8YHNbyPnP2wM8QQ97vcRZH9I1yh0BlDuBlDlJpcwRQJkTSrmTVModAZQ7gbpb5qgxqWOTgBUR9q8EJqa4LJnkT2bWYmY7zeyB3jq+OYJYr5dhZlac4vJkip+ZWbN/TPzfetr45iRQ7kSm3IlMuRNOmdM5ypzIlDmRKXMiU+50jnInMuVOZMqdcGnPHA1z61gfvDGZoXYBFSkuSybYA/wKeAnYC0wDrgcWmdk059z2dBYuA/QB1kbYv8t/WwHsT1lp0q8BuB14FtgBjMd7vbxuZkc5595PZ+EymHInmHInNuVOO2VO1yhzgilzYlPmBFPudI1yJ5hyJzblTruMyRw1JsXHRdhnKS9FBnDOvQ28HbDrJTN7GViCN1b1B2kpWOYw9Ho5yDm3BbgqYNcrZvYM3qcI3wcuSUvBuge9jvyUOx1S7vgpcw6JXkN+ypwOKXMCKHcOiV5HfsqdDil3/DIpczTMrWO78VpCQ1UQuTW913HOLQM+AmamuywZYBfRXy+g1wzOuQ3Aq+j1EotypwPKnSDKnRiUOXFR5nRAmRNEmdMB5U5clDsdUO4EUe7EkK7MUWNSx1bijdEMNRF4L8VlyWTRWot7m1ivl/XOud7S/bIjer3EptyJj15HHuVOx/RaiU2ZEx+9jjzKnPjo9RKbcic+eh15lDsdS/lrRY1JHfsbMNvMRrXtMLMRwFz/fb2emc0AxgJvpLssGeBvwGAzO65th5mVAmej1wsA/okE56LXSyzKnQ4od4Iod2JQ5sRFmdMBZU4QZU4HlDtxUe50QLkTRLkTQ7oyx5xTQ2csZlYEvAPU441VdcCNQAkwpbe1gprZn4A1wDKgBm9yuOuAOmC6c646faVLPjO7wP/Pk/DGql6NN/HZDufcS2bmw+tiOBT4Ll6Xy+uAKcAR/i6IPUYc9fErvEbrRf794/DqowyY5Zz7MPWlznzKnWDKHeVOG2VOcihzgilzlDmBlDvJodwJptxR7rTpVpnjnNPWwQYMAx7Bm1l/H/AYMCLd5UpTXVwHvIu34kATsAG4AxiY7rKl6Pt3UbaFAcf0Ae7GG9tbBzyPF3JpL3+q6wO4HHgTL/Cbga3AA8C4dJc90zflTlBdKHeUO3HVhTLnkOpWmdNeF8ocZU7c9aHcOaS6Ve6014VyR7kTV11kUuaoZ5KIiIiIiIiIiMRNcyaJiIiIiIiIiEjc1JgkIiIiIiIiIiJxU2OSiIiIiIiIiIjETY1JIiIiIiIiIiISNzUmiYiIiIiIiIhI3NSYJCIiIiIiIiIicVNjUhqZ2Vozcx1s56a7nJI4ZnZ8HD/zSNtl/vN/7P/6ng6e5zL/cQtT8G1JN6Lc6X2UO5JOypzeR5kj6abc6X2UO+mRne4CCAD/ALZGuW99KgsiSbcV+GOE/VOBI4DVwKsR7l+VxDJJ76Tc6T2UO5IJlDm9hzJHMoVyp/dQ7qSBGpMyw8+dcwvTXQhJPufcB8BlofvN7Md4Qfeqcy7sfpEkUO70EsodyRDKnF5CmSMZRLnTSyh30kPD3EREREREREREJG5qTOom2sZ1+v99hZm9YWZ7/fvLA46bYGa/N7M1ZnbAzHab2XNmNi/GYw83s3vNbJuZ1ZvZe2Z2rZllB4w5HhGtPFEeM+J5/vtyzOwqM3vFX74DZvaxmf3azKoiHN82NvUeMysxs1/6v78GM9tkZv9rZn1ilGWCmd1hZqv8399uM3vXzG4ys+H+Y+b7n+OZGI8z2X/MJjPrlr364hxD/uN0l1Myg3JHuZMIyh2JlzJHmZMIyhzpDOWOcicRemvudMsfVm9mZrcAVwOvAU8CY4G2ALwIb6xoLrDSf38VcAxwkpnd6Jy7IeTxJgIvAZXABuBxoAK4EZiVhPKXAk8BRwN7gLeAGmA68G3gfDM7zjm3NsLpZXjf92DgZWCF/3GuAo4ys9nOuaaQ55sP3IlXJ5/g1UkOMAb4N/9j3AMsAH4JnGpmY5xzkcbPfs1/e4dzrrkL334meBjvZx3JBUAR0JK64kh3oNxR7hwi5Y50ijJHmXOIlDnSacod5c4h6p2545zTlqYNWIsXUsfHcazzbzXAURHunwI0APuA00Pum4Q3yZwDTgi57y3//nuB3JBztgc874hI5Ynjews9b4F//0NARcD+LOC//PctDDnnsoByPAUUB9w3KOB7uzjkvJlAE9AMXAFYyP0TgAkBX9/of5ybInw/pf66bQIGJeG18GP/c9+ToOPa6mxhnM//ff/xHwF90/27oS15m3JHuRPw+ModbUnflDnKnIDHV+ZoS8mm3FHuBDy+cieJW9oL0Ju3gDCItt0TcGzbvuujPNaf/fd/Ncr9F/jvfyRg3zEB4VkW4ZxvJDLogIn+fWuBggjn+IB3/MdMDtjf9ku7DxgQ4bxr/fffHbL/Mf/+n8f58xjkD7KdQH6UuvhLkl4LnQ26eLeFcTz354FWYAcwOt2/F9qSuyl3ws5R7ih3tCVxU+aEnaPMUeZoS/Km3Ak7R7mj3EnKpmFumSHaspWRli98NHSHmfmA0/Be2A9HeY6X/LefCth3nP/2Sefcngjn3Af8NsrjdcXpAc9XH3qnc67VzF7F+wTgU8DykEPecs5FqqcP/LeD2naYWRZwsv/Lu+IpnHNus5k9CnwWuAiva2abr/pvb43nsVIg2vKWbcYAczt6EDM7FvgD3icu85xzqxNTPOkGlDsodzpJuSOHQpmDMqeTlDlyqJQ7KHc6SbnTCWpMygydWbZyXYR9ffG6CAJsN7NY5wdOvjbEf7sm0oHOuRoz24M3jjYRRvlvv2ZmX4t5ZHA526yPcuxe/21+wL5KvLGpzS7y2NxofosXdFfjDzozOwGvu+ZK59xL0U9NqZjLW5rZZXQQdGY2Dvgr3ljnC51zixJZQMl4yp1wyp3YlDtyKJQ54ZQ5sSlz5FApd8Ipd2JT7nSCGpO6mUitzXhjYcGb1Ov+FBYnlkgrBbaV8y28SdliWRlhX+shlSgOzrnXzOxtYKaZzXDOLaV9Urjbkv38qeJf0eHvQB/g35xzj6S5SJLBlDvJpdwRCabMSS5ljkg45U5yKXd6JjUm9QzVQD1QAHzdObc/zvM2+W9HRLrTzMqI3mLeBOSYWXHo85lZDjAwwjkb/LcvOue+G2cZu6oaqAMKzWx0J7sW3gLcDVxtZj8AzsEbS3xf4ouZemZWADyB9ynGrc65X6e5SNI9KXfCKXeiUO5IAihzwilzolDmSIIod8Ipd6LojbkTqWVTuhnnLaH4nP/LCzpxalt3wrP9y0mGuiTGuW0hOT7CfacSuaHyaf/tuWaW1IZM51wL7XXypU6e/iBeUF4EfA/ve7nXObcvcSVMD//Y7/vxliR9AvhWeksk3ZVyJ5xyJzLljiSCMiecMicyZY4kinInnHInst6aO2pM6jl+gteS/Rszu8hCBvWamc/MTjKz0wJ2vwL8C69l/Df+1u624ycAP4zxfM/7b28ws9yA8ybhtTqHcc4tw5v9fwzwFzMbEnqMmQ00s2sSFIT/idct9d/941tDn2u8mYUFtXPuAN6EcgV4KwxAjO6XZnaPmTkzuycBZU62XwHnAUuBz/v/IIh0lXInnHInnHJHEkWZE06ZE06ZI4mk3Amn3AnXK3NHjUk9hH/c6XwgB6/Vd42Z/d3M/mxmrwHb8FqRjw84xwFfAHbhLQ25yswWmNkzeAH4OpEnowP4Gd6kbGcDH5rZw+atErAMeC3GeZfitdZ/BvjYzBb7y/i0ma0ANgI3k4AhmM65JcCV/i//YGar/M/1mJktB94HZkc5/Ta8kARv6cf3YjxV2+9R06GWOZnMbChwjf/LauBWf0iHbuemrZDSrSh3wil3gil3JJGUOeGUOcGUOZJoyp1wyp1gvTl3NGdSD+KcW2BmbwLfBE6hfVnKrXgB9BQhy1o651aY2Qy8VvdPA+cCa4H/AH4BRJyl3zm32szm4rVMHwuc6T/2u3it5tFWL9hrZicB/wevi+d04EhgN7AZ+B3wuL/l+pA55+7218l3gBP9318t3qoFvwReiHLeBjP7AJhEx0tVTvff3p2IMidRVsC/T4t6lPfzfyypJZEeQ7kT8fmUO+2UO5JQypyIz6fMaafMkYRT7kR8PuVOu16bO+Y1nIpEZmZrgeHASOfc2vSWJnXM7Ai8Tw42A8P9Y6YjHTfQf8xfnXPnpa6EIj2Xcke5I5JKyhxljkiqKXeUOz2BhrmJRPYT/+1vo4Wc3yl4XTWvT36RRKSHU+6ISCopc0Qk1ZQ7PYh6JklMvanV3Mzm4S1RORmYidcV8XDnXG06yyXS2yh3lDsiqaTMUeaIpJpyR7nTE6hnkki76cDleEtxPgOcppATkSRT7ohIKilzRCTVlDs9lHomiYiIiIiIiIhI3NQzSURERERERERE4qbGJBERERERERERiZsak0REREREREREJG5qTBIRERERERERkbipMUlEREREREREROKmxiQREREREREREYnb/wej4qJ6T0N0WgAAAABJRU5ErkJggg==\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",
"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": 17,
"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": 18,
"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>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": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from collections import Counter\n",
"\n",
"pot = \"LiAl_eam\"\n",
"\n",
"data_convexhull = data_murn[data_murn[\"potential\"]==pot]\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": {
"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",
" </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>1.000000</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>1.000000</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>1.000000</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>1.000000</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>0.500000</td>\n",
" <td>0.500000</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>0.750000</td>\n",
" <td>0.250000</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>0.307692</td>\n",
" <td>0.692308</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>0.400000</td>\n",
" <td>0.600000</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>0.500000</td>\n",
" <td>0.500000</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",
"\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",
"\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",
"\n",
" n_atoms phase comp_dict n_Al n_Li cAl \\\n",
"0 1 Al_fcc {'Al': 1} 1 0 1.000000 \n",
"1 1 Al_bcc {'Al': 1} 1 0 1.000000 \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 0.500000 \n",
"5 4 LiAl3_cubic {'Li': 1, 'Al': 3} 3 1 0.750000 \n",
"6 13 Li9Al4_monoclinic {'Li': 9, 'Al': 4} 4 9 0.307692 \n",
"7 5 Li3Al2_trigonal {'Al': 2, 'Li': 3} 2 3 0.400000 \n",
"8 8 Li4Al4_cubic {'Li': 4, 'Al': 4} 4 4 0.500000 \n",
"\n",
" cLi \n",
"0 0.000000 \n",
"1 0.000000 \n",
"2 1.000000 \n",
"3 1.000000 \n",
"4 0.500000 \n",
"5 0.250000 \n",
"6 0.692308 \n",
"7 0.600000 \n",
"8 0.500000 "
]
},
"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": [
"[-3.4830968311997506, -1.7581073374325675]"
]
},
"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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>1.000000</td>\n",
" <td>0.001096</td>\n",
" <td>0.001096</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>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 \\\n",
"0 2 LiAl_eam \n",
"1 15 LiAl_eam \n",
"5 67 LiAl_eam \n",
"4 54 LiAl_eam \n",
"8 106 LiAl_eam \n",
"7 93 LiAl_eam \n",
"6 80 LiAl_eam \n",
"2 28 LiAl_eam \n",
"3 41 LiAl_eam \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",
"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",
"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",
"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",
"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",
"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",
"2 (Atom('Li', [0.0, 0.0, 0.0], index=0)) \n",
"3 (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",
"5 LiAl3 cubic 5.607502 62.227580 51.472656 -12.774590 \n",
"4 Li2Al2 cubic 6.165940 58.604895 100.347240 -11.074362 \n",
"8 Li4Al4 cubic 6.061226 131.389799 71.221355 -20.506570 \n",
"7 Li3Al2 trigonal 6.094693 72.810229 69.231669 -12.413856 \n",
"6 Li9Al4 monoclinic 13.023701 190.504374 53.125276 -28.970054 \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",
"\n",
" n_atoms phase comp_dict n_Al n_Li cAl \\\n",
"0 1 Al_fcc {'Al': 1} 1 0 1.000000 \n",
"1 1 Al_bcc {'Al': 1} 1 0 1.000000 \n",
"5 4 LiAl3_cubic {'Li': 1, 'Al': 3} 3 1 0.750000 \n",
"4 4 Li2Al2_cubic {'Li': 2, 'Al': 2} 2 2 0.500000 \n",
"8 8 Li4Al4_cubic {'Li': 4, 'Al': 4} 4 4 0.500000 \n",
"7 5 Li3Al2_trigonal {'Al': 2, 'Li': 3} 2 3 0.400000 \n",
"6 13 Li9Al4_monoclinic {'Li': 9, 'Al': 4} 4 9 0.307692 \n",
"2 1 Li_bcc {'Li': 1} 0 1 0.000000 \n",
"3 1 Li_fcc {'Li': 1} 0 1 0.000000 \n",
"\n",
" cLi E_form E_form_per_atom \n",
"0 0.000000 0.000000 0.000000 \n",
"1 0.000000 0.067785 0.067785 \n",
"5 0.250000 -0.567192 -0.141798 \n",
"4 0.500000 -0.591954 -0.147988 \n",
"8 0.500000 0.458247 0.057281 \n",
"7 0.600000 -0.173341 -0.034668 \n",
"6 0.692308 0.785300 0.060408 \n",
"2 1.000000 0.001096 0.001096 \n",
"3 1.000000 0.000000 0.000000 "
]
},
"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": 45,
"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": 46,
"id": "e12e56f8",
"metadata": {},
"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",
"ax.set_ylim(-200,10)\n",
"\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": 26,
"id": "ca9dbcc8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total run time for the notebook 397.49062490463257 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": 27,
"id": "59d98667-c265-4a29-9267-25faeb033471",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6.624843748410543"
]
},
"execution_count": 27,
"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
}