{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8656c0d1-d101-4d1e-bb5f-87eb07c8059f",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6fc8050f-5451-4c49-9213-08143f9b111d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scm.plams import Settings, fromASE, view, Units\n",
    "from scm.plams.recipes.bandfragment import BANDFragmentJob\n",
    "\n",
    "# build the surface\n",
    "from ase import Atoms\n",
    "from ase.build import fcc111, add_adsorbate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d0c426-63d1-4a60-8868-8f63bdd9889a",
   "metadata": {},
   "source": [
    "## Build Surface & Fragments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35b58534-0180-4028-99fd-8eb8a0bb9539",
   "metadata": {},
   "source": [
    "We first build a gold surface and add the hydrogen adsorbate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7157a638-eb62-447d-85b3-4965b0c8f7fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=400x400>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol = fcc111(\"Au\", size=(2, 2, 3))\n",
    "add_adsorbate(mol, \"H\", 1.5, \"ontop\")\n",
    "mol.center(vacuum=10.0, axis=2)\n",
    "view(fromASE(mol), padding=-4, width=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae35b03e-7d99-494f-a2c7-d7c7c4a182b7",
   "metadata": {},
   "source": [
    "The system is then split into two fragments based on the species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c25aeb8-4893-4389-b373-ae12d9ed3a8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "surface = mol.copy()\n",
    "symbols = surface.get_chemical_symbols()\n",
    "del surface[[i for i in range(len(symbols)) if symbols[i] != \"Au\"]]\n",
    "adsorbate = mol.copy()\n",
    "del adsorbate[[i for i in range(len(symbols)) if symbols[i] == \"Au\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1245bc4b-2914-44c7-b281-74fd3f70f6d3",
   "metadata": {},
   "source": [
    "If available, optimized fragments can also be loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6243ece2-bfe2-4864-8e46-052b8c13ef43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from ase import io\n",
    "# surface_opt = io.read(\"surface_opt.xyz\")\n",
    "# adsorbate_opt = io.read(\"adsorbate_opt.xyz\")\n",
    "# assert len(surface_opt) == len(surface)\n",
    "# assert len(adsorbate_opt) == len(adsorbate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8361589-3a1b-4aa0-9c0d-491420cb5ca0",
   "metadata": {},
   "source": [
    "## Set Up & Run Job"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cece4839-9caf-4489-a8a0-2580dae86918",
   "metadata": {},
   "source": [
    "For efficiency in this example, we use a minimal basis and reduced computational details to run the job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c1d0da8d-195b-4e9a-9b72-1fa113f52c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_settings = Settings()\n",
    "base_settings.input.ams.task = \"SinglePoint\"\n",
    "base_settings.input.band.basis.type = \"SZ\"\n",
    "base_settings.input.band.basis.core = \"Large\"\n",
    "base_settings.input.band.dos.calcdos = \"No\"\n",
    "base_settings.input.band.kspace.regular.numberofpoints = \"3 3\"\n",
    "base_settings.input.band.beckegrid.quality = \"Basic\"\n",
    "base_settings.input.band.zlmfit.quality = \"Basic\"\n",
    "base_settings.input.band.usesymmetry = \"No\"\n",
    "base_settings.input.band.xc.gga = \"PBE\"\n",
    "base_settings.input.band.xc.dispersion = \"Grimme4\"\n",
    "\n",
    "eda_settings = Settings()\n",
    "eda_settings.input.band.peda = \"\"\n",
    "\n",
    "eda_job = BANDFragmentJob(\n",
    "    fragment1=fromASE(surface),\n",
    "    fragment2=fromASE(adsorbate),\n",
    "    settings=base_settings,\n",
    "    full_settings=eda_settings,\n",
    "    #    fragment1_opt=fromASE(surface_opt),\n",
    "    #    fragment2_opt=fromASE(adsorbate_opt),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c5b3e43-5ff9-472e-8bb0-bdfba6138231",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23.03|17:02:01] JOB plamsjob STARTED\n",
      "[23.03|17:02:01] JOB plamsjob RUNNING\n",
      "[23.03|17:02:01] JOB plamsjob/frag1 STARTED\n",
      "[23.03|17:02:01] JOB plamsjob/frag1 RUNNING\n",
      "[23.03|17:02:53] JOB plamsjob/frag1 FINISHED\n",
      "[23.03|17:02:53] JOB plamsjob/frag1 SUCCESSFUL\n",
      "[23.03|17:02:53] JOB plamsjob/frag2 STARTED\n",
      "[23.03|17:02:53] JOB plamsjob/frag2 RUNNING\n",
      "[23.03|17:02:56] JOB plamsjob/frag2 FINISHED\n",
      "[23.03|17:02:56] JOB plamsjob/frag2 SUCCESSFUL\n",
      "[23.03|17:02:56] JOB plamsjob/full STARTED\n",
      "[23.03|17:02:56] JOB plamsjob/full RUNNING\n",
      "[23.03|17:03:36] JOB plamsjob/full FINISHED\n",
      "[23.03|17:03:36] JOB plamsjob/full SUCCESSFUL\n",
      "[23.03|17:03:36] JOB plamsjob FINISHED\n",
      "[23.03|17:03:36] JOB plamsjob SUCCESSFUL\n"
     ]
    }
   ],
   "source": [
    "eda_job.run();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408bad71-c651-4ed0-85c6-977893425841",
   "metadata": {},
   "source": [
    "## Energy Decomposition Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "965ab5b8-e58e-4bbb-80ff-654993c6082d",
   "metadata": {},
   "source": [
    "Finally, we extract the results of the energy decomposition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f4935d02-6269-49c3-91de-47291bb35f83",
   "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>Term</th>\n",
       "      <th>Energy [eV]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E_int</td>\n",
       "      <td>-1.886021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E_int_disp</td>\n",
       "      <td>-0.094696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E_Pauli</td>\n",
       "      <td>11.635861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E_elstat</td>\n",
       "      <td>-5.387854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E_orb</td>\n",
       "      <td>-8.039060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E_1</td>\n",
       "      <td>-19.288663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E_2</td>\n",
       "      <td>-0.018088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Term  Energy [eV]\n",
       "0       E_int    -1.886021\n",
       "1  E_int_disp    -0.094696\n",
       "2     E_Pauli    11.635861\n",
       "3    E_elstat    -5.387854\n",
       "4       E_orb    -8.039060\n",
       "5         E_1   -19.288663\n",
       "6         E_2    -0.018088"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "results = eda_job.results\n",
    "eda_res = eda_job.results.get_energy_decomposition(unit=\"eV\")\n",
    "\n",
    "df = pd.DataFrame([{\"Term\": t, \"Energy [eV]\": e} for t, e in eda_res.items()])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6afae1c7-e638-453f-bc00-9719fead81c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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