Preprocessing notebook

IPython/Pynta preprocessing.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"/Users/shikim/pynta_local/pynta/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preprocessing notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing notebook will calculate: \n",
+    "1. notes for choice of pseudopotentials\n",
+    "2. Lattice parameter optimization and analysis\n",
+    "3. Energy convergence test\n",
+    "4. k-points optimization\n",
+    "\n",
+    "*Preprocessing can only run with surfaces that ASE can build*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "from ase.visualize import view\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pynta.calculator import get_lattice_parameters\n",
+    "from pynta.preprocessing import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Notes on Pseudopotentials:\n",
+    "\n",
+    "We can choose from various pseudopotential libraries. Choice of pseudopotential depends on the problem we are investigating, *e.g.,* if there is a heavy element present in our system and we are interested in the spin-orbit coupling effects, we should choose a full relativistic pseudopotential. We need to be careful whether our chosen pseudopotential correctly reproduces physical properties. Various pseudopotential libraries:\n",
+    "\n",
+    "- https://www.quantum-espresso.org/pseudopotentials\n",
+    "- https://www.materialscloud.org/discover/sssp/table/efficiency\n",
+    "- http://www.pseudo-dojo.org\n",
+    "- https://www.physics.rutgers.edu/gbrv/\n",
+    "- https://nninc.cnf.cornell.edu\n",
+    "- http://www.quantum-simulation.org/potentials/\n",
+    "- BLYP pseudopotentials (https://pseudopotentials.quantum-espresso.org/legacy_tables/hartwigesen-goedecker-hutter-pp) \n",
+    "- SCAN pseudopotentials (https://yaoyi92.github.io/scan-tm-pseudopotentials.html)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Lattice parameter optimization:\n",
+    "\n",
+    "\n",
+    "If your slab is not optimized, it is NOT recommended to use for Pynta production run. Please optimize your slab before running Pynta\n",
+    "\n",
+    "*Preprocessing can only run with surfaces that ASE can build*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#calculator\n",
+    "pseudo = {'Cu': 'Cu.pbe-spn-kjpaw_psl.1.0.0.UPF'}\n",
+    "command = '/Users/shikim/miniconda3/envs/pynta_env/bin/pw.x -in PREFIX.pwi > PREFIX.pwo'\n",
+    "software_kwargs={'command':command,'kpts': (10,10,10), 'ecutwfc': 70, 'degauss':0.02, 'mixing_mode': 'plain', \n",
+    "                 'pseudopotentials':pseudo,'occupations':'smearing', 'smearing':'gauss',} "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#calculate lattice constant using Pynta\n",
+    "get_lattice_parameters('Cu','fcc111','Espresso',software_kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test lattice constant\n",
+    "analyze_lattice_parameter('Cu','fcc111','Espresso',software_kwargs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Cutoff Energy convergence test: \n",
+    "\n",
+    "We can calculate the total energy of the system for various values of energy cutoff values. \n",
+    "Converged energy cutoff value with peudopotentials of choice can construct more accurate DFT input for Pynta. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prep = Prep(metal='Pt', surface_type='fcc111', a0=3.96, software='Espresso', software_kwargs={})\n",
+    "# Define your metal, software, software kwargs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutoff_energy = prep.opt_cutoff_energy(slab=None, ecut_range=(200, 501, 50),output_file='cutoff_energy.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. k-point convergence test:\n",
+    "\n",
+    "With the optimized slab, appropriate k-points should be calculated to execute Pynta efficiently. \n",
+    "\n",
+    "Lowest total energy is estimated from lattice parameter optimization. We use the lowest potential energy to estimate k-points.\n",
+    "\n",
+    "Tip: If you have already obtained satisfactory convergence with a (relatively) sparse k-point grid, there is no motivation to go for a denser grid. It makes calculations more expensive. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpts_list, energies = prep.opt_kpoints(slab=None, kpts_range=[(1, 1, 1), (2, 2, 1)], output_file='kpts.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_results(cutoff_energy, kpts_list, energies)\n",
+    "# Plot energy vs energy cutoff \n",
+    "# Plot energy vs k-points"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.12.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}