C.3 Size dependence of H in Fe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9c412e3e-05d9-48ec-86b2-cb9055daa0e4",
   "metadata": {},
   "source": [
    "### SIZE DEPENDENCE OF HYDROGEN IN α-IRON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c71497d5-6aa5-4be2-a83b-c54a9df0094f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1e97a84a6ad40fb8a543d6a8741a64b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyiron\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f725a2cd-c448-4c99-b8d4-6b9821e01c1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "potential = 'FeH-NNIP'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d1b5304-fc11-49d8-b5ed-3497ce80bdcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr = pyiron.Project('size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e289407-275b-4dff-b526-27151d31336b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job bulk was saved and received the ID: 10608\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Cell([[2.8299282001208694, 1.7328312560474265e-16, 1.7328312560474265e-16], [0.0, 2.8299282001208694, 1.7328312560474263e-16], [0.0, 0.0, 2.8299282001208694]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job = pr.create.job.Lammps('bulk', delete_existing_job=True)\n",
    "bulk = pr.create.structure.bulk('Fe', cubic=True)\n",
    "job.structure = bulk\n",
    "job.potential = potential\n",
    "job.calc_minimize(pressure=0.0)\n",
    "job.run(delete_existing_job=True)\n",
    "bulk = job.get_structure(-1)\n",
    "bulk.get_cell()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f361f45-04bf-4282-892f-9794c54fc9eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job sc_16 was saved and received the ID: 10609\n"
     ]
    }
   ],
   "source": [
    "# 16 atoms\n",
    "job = pr.create.job.Lammps('sc_16', delete_existing_job=True)\n",
    "sc_16 = bulk.repeat([2,2,2])\n",
    "\n",
    "cell = sc_16.get_cell()\n",
    "H = pr.create.structure.atoms(['H'], [(2.83/2, 2.83, 2.83/4*6-2.83/4)], cell=cell)\n",
    "sc_16 += H\n",
    "\n",
    "job.structure = sc_16\n",
    "job.potential = potential\n",
    "job.calc_minimize()\n",
    "job.run(delete_existing_job=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79dc9a55-9210-4e34-aafb-5fcc57713eef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job sc_54 was saved and received the ID: 10610\n"
     ]
    }
   ],
   "source": [
    "# 54\n",
    "job = pr.create.job.Lammps('sc_54', delete_existing_job=True)\n",
    "sc_54 = bulk.repeat([3,3,3])\n",
    "\n",
    "cell = sc_54.get_cell()\n",
    "H = pr.create.structure.atoms(['H'], [(2.83/2, 2.83, 2.83/4*6-2.83/4)], cell=cell)\n",
    "sc_54 += H\n",
    "\n",
    "job.structure = sc_54\n",
    "job.potential = potential\n",
    "job.calc_minimize()\n",
    "job.run(delete_existing_job=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "780d857c-9d6b-4eeb-bcce-67a664881999",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job sc_128 was saved and received the ID: 10611\n"
     ]
    }
   ],
   "source": [
    "# 128\n",
    "job = pr.create.job.Lammps('sc_128', delete_existing_job=True)\n",
    "sc_128 = bulk.repeat([4,4,4])\n",
    "\n",
    "cell = sc_128.get_cell()\n",
    "H = pr.create.structure.atoms(['H'], [(2.83/2, 2.83, 2.83/4*6-2.83/4)], cell=cell)\n",
    "sc_128 += H\n",
    "\n",
    "job.structure = sc_128\n",
    "job.potential = potential\n",
    "job.calc_minimize()\n",
    "job.run(delete_existing_job=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eaf4e5b5-9f82-4360-9152-6e5d352c4a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "job = job.load('sc_16')\n",
    "p = job.output.pressures[0] - job.output.pressures[-1]\n",
    "p_main = np.diagonal(p)\n",
    "P1 = p_main.mean()\n",
    "P1\n",
    "E1 = job.output.energy_tot[0] - job.output.energy_tot[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c92645d-9a43-4bfe-8e6e-984bf3a47787",
   "metadata": {},
   "outputs": [],
   "source": [
    "job = job.load('sc_54')\n",
    "p = job.output.pressures[0] - job.output.pressures[-1]\n",
    "p_main = np.diagonal(p)\n",
    "P2 = p_main.mean()\n",
    "P2\n",
    "E2 = job.output.energy_tot[0] - job.output.energy_tot[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "25c8085e-698f-47dd-bbec-f5cf06ffc6af",
   "metadata": {},
   "outputs": [],
   "source": [
    "job = job.load('sc_128')\n",
    "p = job.output.pressures[0] - job.output.pressures[-1]\n",
    "p_main = np.diagonal(p)\n",
    "P3 = p_main.mean()\n",
    "P3\n",
    "E3 = job.output.energy_tot[0] - job.output.energy_tot[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09df71e0-76f9-4214-bb58-4fd979cbd334",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the points\n",
    "points = [(1/128, P3), (1/54, P2), (1/16, P1)]\n",
    "\n",
    "# Separate x and y coordinates\n",
    "x_values = [point[0] for point in points]\n",
    "y_values = [point[1] for point in points]\n",
    "\n",
    "# Plot the points\n",
    "plt.plot(x_values, y_values, 'o-')\n",
    "\n",
    "# Customize x-axis ticks\n",
    "plt.xticks([1/128, 1/54, 1/16], ['$1/128$', '$1/54$', '$1/16$'])\n",
    "\n",
    "# Customize y-axis limits and ticks\n",
    "plt.ylim(-0.1, 0.5)\n",
    "plt.yticks([ 0.0, 0.2, 0.4])\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('X-axis')\n",
    "plt.ylabel(r'$\\Delta P_{rlx} \\, (\\mathrm{GPa})$')  # Using a raw string for LaTeX\n",
    "\n",
    "plt.axvline(x=1/128, c=\"tab:orange\")\n",
    "plt.axvline(x=1/54, c=\"tab:orange\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38576eb4-61c3-4221-8999-58cbd503b9c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the points\n",
    "points = [(1/128, -E3*1000), (1/54, -E2*1000), (1/16, -E1*1000)]\n",
    "\n",
    "# Separate x and y coordinates\n",
    "x_values = [point[0] for point in points]\n",
    "y_values = [point[1] for point in points]\n",
    "\n",
    "# Plot the points\n",
    "plt.plot(x_values, y_values, 'o-')\n",
    "\n",
    "# Customize x-axis ticks\n",
    "plt.xticks([1/128, 1/54, 1/16], ['$1/128$', '$1/54$', '$1/16$'])\n",
    "\n",
    "# Customize y-axis limits and ticks\n",
    "plt.ylim(-257, -215)\n",
    "plt.yticks([ -250, -240, -230, -220])\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('X-axis')\n",
    "plt.ylabel(r'$\\Delta E_{rlx} \\, (\\mathrm{meV})$')  # Using a raw string for LaTeX\n",
    "\n",
    "plt.axvline(x=1/128, c=\"tab:orange\")\n",
    "plt.axvline(x=1/54, c=\"tab:orange\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90cf7ef6-2ff3-4853-a177-18e11c30f8a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d0436dad-32cb-4859-b8dd-c5098e7d9655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21842607979201034"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0d71bc44-ec6a-4b32-8dda-338004cf3392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2399448922880083"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7c584ba5-3909-4cac-b6e7-21688ec5657d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24651355322998825"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c74879bc-1913-4058-915a-419fe7083869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FeH-NNIP',\n",
       " '2007--Lee-B-J--Fe-H--LAMMPS--ipr1',\n",
       " '2021--Wen-M--Fe-H--LAMMPS--ipr1',\n",
       " '2022--Starikov-S--Fe-Cr-H--LAMMPS--ipr1',\n",
       " '2023--Kumar-P--Fe-H--LAMMPS--ipr1',\n",
       " 'MEAM_LAMMPS_LeeJang_2007_FeH__MO_095610951957_000',\n",
       " 'Sim_LAMMPS_ReaxFF_AryanpourVanDuinKubicki_2010_FeHO__SM_222964216001_001',\n",
       " 'FeH-NNIP']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.list_potentials()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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