{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entropy and enthalpy parameters\n",
    "\n",
    "In this example, energy and entropy parameters will be used for structural distinction. We will consider bcc, fcc, and hcp structures to calculate the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyscal.core as pc\n",
    "import pyscal.crystal_structures as pcs\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will create some structures with thermal vibrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bcc_atoms, bcc_box = pcs.make_crystal('bcc', \n",
    "                    lattice_constant=3.147, \n",
    "                    repetitions=[10,10,10], noise=0.1)\n",
    "bcc = pc.System()\n",
    "bcc.atoms = bcc_atoms\n",
    "bcc.box = bcc_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fcc_atoms, fcc_box = pcs.make_crystal('fcc', \n",
    "                     lattice_constant=3.147, \n",
    "                     repetitions=[10,10,10], noise=0.1)\n",
    "fcc = pc.System()\n",
    "fcc.atoms = fcc_atoms\n",
    "fcc.box = fcc_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "hcp_atoms, hcp_box = pcs.make_crystal('hcp', \n",
    "                     lattice_constant=3.147, \n",
    "                     repetitions=[10,10,10], noise=0.1)\n",
    "hcp = pc.System()\n",
    "hcp.atoms = hcp_atoms\n",
    "hcp.box = hcp_box"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to calculate the neighbors using adaptive cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "bcc.find_neighbors(method='cutoff', cutoff=0)\n",
    "fcc.find_neighbors(method='cutoff', cutoff=0)\n",
    "hcp.find_neighbors(method='cutoff', cutoff=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to calculate energy, a molybdenum potential is used here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bcc.calculate_energy(species=['Mo'], pair_style='eam/alloy', \n",
    "                     pair_coeff='* * Mo.set Mo', mass=95,\n",
    "                     averaged=True)\n",
    "fcc.calculate_energy(species=['Mo'], pair_style='eam/alloy', \n",
    "                     pair_coeff='* * Mo.set Mo', mass=95,\n",
    "                     averaged=True)\n",
    "hcp.calculate_energy(species=['Mo'], pair_style='eam/alloy', \n",
    "                     pair_coeff='* * Mo.set Mo', mass=95,\n",
    "                     averaged=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will calculate the entropy parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "latbcc = (bcc.box[0][1]-bcc.box[0][0])/10\n",
    "latfcc = (fcc.box[0][1]-fcc.box[0][0])/10\n",
    "lathcp = (hcp.box[0][1]-hcp.box[0][0])/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "bcc.calculate_entropy(1.4*latbcc, averaged=True, local=True)\n",
    "fcc.calculate_entropy(1.4*latfcc, averaged=True, local=True)\n",
    "hcp.calculate_entropy(1.4*lathcp, averaged=True, local=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gather the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "bccenergy = [atom.energy for atom in bcc.atoms]\n",
    "avgbccenergy = [atom.avg_energy for atom in bcc.atoms]\n",
    "fccenergy = [atom.energy for atom in fcc.atoms]\n",
    "avgfccenergy = [atom.avg_energy for atom in fcc.atoms]\n",
    "hcpenergy = [atom.energy for atom in hcp.atoms]\n",
    "avghcpenergy = [atom.avg_energy for atom in hcp.atoms]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "bccentropy = [atom.entropy for atom in bcc.atoms]\n",
    "avgbccentropy = [atom.avg_entropy for atom in bcc.atoms]\n",
    "fccentropy = [atom.entropy for atom in fcc.atoms]\n",
    "avgfccentropy = [atom.avg_entropy for atom in fcc.atoms]\n",
    "hcpentropy = [atom.entropy for atom in hcp.atoms]\n",
    "avghcpentropy = [atom.avg_entropy for atom in hcp.atoms]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f82fd6bc690>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(fccenergy, fccentropy, s=10, label='fcc', color='#FFB300')\n",
    "plt.scatter(hcpenergy, hcpentropy, s=10, label='hcp', color='#388E3C')\n",
    "plt.scatter(bccenergy, bccentropy, s=10, label='bcc', color='#C62828')\n",
    "plt.xlabel(\"Energy\")\n",
    "plt.ylabel(\"Entropy\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f82fd615d90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(avgfccenergy, avgfccentropy, s=10, label='fcc', color='#FFB300')\n",
    "plt.scatter(avghcpenergy, avghcpentropy, s=10, label='hcp', color='#388E3C')\n",
    "plt.scatter(avgbccenergy, avgbccentropy, s=10, label='bcc', color='#C62828')\n",
    "plt.xlabel(\"Avg Energy\")\n",
    "plt.ylabel(\"Avg Entropy\")\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lammps",
   "language": "python",
   "name": "lammps"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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