BH76.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "bbe4ef64-c985-49c7-bf3d-bd0358bec342",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymolpro\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6c3db252-0cd1-4a61-bbf6-68ab3f182dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = 'local' \n",
    "project_name = 'BH76_methodtest'\n",
    "parallel = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c93326a5-850e-4cc3-a382-db95485a2844",
   "metadata": {},
   "outputs": [],
   "source": [
    "methods = {\"HF\": \"hf\",\n",
    "\"MP2\": \"mp2\",\n",
    "\"MP3\": \"mp3\",\n",
    "\"MP4\": \"mp4\",\n",
    "\"PAPT2\":\"PAPT;MP2\",\n",
    "\"PAPT3\":\"PAPT;MP3\",\n",
    "\"PAPT4\":\"PAPT;MP4\",\n",
    "\"CCSD\":\"CCSD\",\n",
    "\"CCSD(T)\":\"CCSD(T)\" }\n",
    "bases = ['cc-pVDZ','cc-pVTZ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "582c7d92-df29-452b-8f25-45cd9a523440",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = pymolpro.database.load(\"GMTKN55_BH76\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0eed350d-a331-4901-b5d6-03c0e39de744",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GMTKN55 BH76 (closed shell only) (maximum number of electrons 30):\n",
      "\n",
      "Molecules:\n",
      "ch3f: {'geometry': '           5\\n  \\n    C          0.00000000      0.00000000      0.06556637\\n    F          0.00000000      0.00000000     -1.31562859\\n    H          0.51491633     -0.89186125      0.41668741\\n    H          0.51491633      0.89186125      0.41668741\\n    H         -1.02983267      0.00000000      0.41668741\\n', 'description': 'ch3f'}\n",
      "fch3fcomp: {'geometry': '           6\\n  \\n    F          0.00000000      0.00000000     -1.79137135\\n    C          0.00000000      0.00000000     -0.36561703\\n    H         -0.51179113      0.88644824     -0.01758668\\n    H         -0.51179113     -0.88644824     -0.01758668\\n    H          1.02358226      0.00000000     -0.01758668\\n    F          0.00000000      0.00000000      2.20974841\\n', 'description': 'fch3fcomp', 'charge': -1}\n",
      "fch3fts: {'geometry': '           6\\n  \\n    F          1.82355879      0.00051927      0.00010874\\n    C          0.00014416     -0.00008973     -0.00018227\\n    H          0.00006416     -0.30488877      1.02506923\\n    H          0.00028716      1.04017819     -0.24893240\\n    H         -0.00009484     -0.73625925     -0.77617604\\n    F         -1.82395944      0.00054027      0.00011274\\n', 'description': 'fch3fts', 'charge': -1}\n",
      "hcn: {'geometry': '           3\\n  \\n    C          0.00000000      0.00000000     -0.02902643\\n    N          0.00000000      0.00000000      1.12397830\\n    H          0.00000000      0.00000000     -1.09495187\\n', 'description': 'hcn'}\n",
      "ch3oh: {'geometry': '           6\\n  \\n    C         -0.13926933      0.33472048      0.00000000\\n    O         -0.13926933     -1.08341104      0.00000000\\n    H         -1.17980283      0.64759124      0.00000000\\n    H          0.76774408     -1.38539133      0.00000000\\n    H          0.34529870      0.74324532      0.88954166\\n    H          0.34529870      0.74324532     -0.88954166\\n', 'description': 'ch3oh'}\n",
      "hoch3fcomp2: {'geometry': '           7\\n  \\n    F          2.24953292     -0.16163470     -0.00906451\\n    C          0.83989310      0.02030650      0.00310752\\n    H          0.35178382     -0.87134447     -0.36481550\\n    H          0.52242274      0.21318049      1.01958309\\n    H          0.60022397      0.86229312     -0.63454561\\n    O         -1.81896698     -0.15832046     -0.00598050\\n    H         -2.74488958      0.09551952     -0.00828450\\n', 'description': 'hoch3fcomp2', 'charge': -1}\n",
      "oh-: {'geometry': '           2\\n  \\n    O          0.00000000      0.00000000      0.48102212\\n    H          0.00000000      0.00000000     -0.48102212\\n', 'description': 'oh-', 'charge': -1}\n",
      "hoch3fts: {'geometry': '           7\\n  \\n    F          2.13770264      0.08258780     -0.00019942\\n    C          0.37794222      0.10635278      0.00019758\\n    H          0.32799218      1.17531485     -0.01182043\\n    H          0.32424814     -0.43224626     -0.92301475\\n    H          0.32457115     -0.41169619      0.93505886\\n    O         -1.60571838      0.19903310     -0.00018942\\n    H         -1.88673796     -0.71934608     -0.00003242\\n', 'description': 'hoch3fts', 'charge': -1}\n",
      "hcnts: {'geometry': '           3\\n  \\n    C          0.37482171      0.51783010      0.00000000\\n    N          0.37482171     -0.67052208      0.00000000\\n    H         -0.74964343      0.15269198      0.00000000\\n', 'description': 'hcnts'}\n",
      "f-: {'geometry': '           1\\n  \\n    F          0.00000000      0.00000000      0.00000000\\n', 'description': 'f-', 'charge': -1}\n",
      "hnc: {'geometry': '           3\\n  \\n    C          0.00000000      0.00000000     -1.11118191\\n    N          0.00000000      0.00000000      0.05815587\\n    H          0.00000000      0.00000000      1.05302603\\n', 'description': 'hnc'}\n",
      "hoch3fcomp1: {'geometry': '           7\\n  \\n    C         -0.70348642     -0.15745798     -0.00001606\\n    O          0.11728813      0.96008129      0.00007194\\n    H         -1.75741170      0.15182813     -0.00862109\\n    H         -0.54634194     -0.80376173     -0.87808291\\n    H         -0.55866648     -0.79545365      0.88637925\\n    H          1.10509166      0.60322056      0.00026094\\n    F          2.34352676      0.04154336      0.00000794\\n', 'description': 'hoch3fcomp1', 'charge': -1}\n",
      "\n",
      "Reactions:\n",
      "13: f- + ch3f -> fch3fts, energy = -0.000956160864\n",
      "14: ch3f + f- -> fch3fts, energy = -0.000956160864\n",
      "15: fch3fcomp -> fch3fts, energy = 0.021354259296\n",
      "16: fch3fcomp -> fch3fts, energy = 0.021354259296\n",
      "25: oh- + ch3f -> hoch3fts, energy = -0.004302723888\n",
      "26: ch3oh + f- -> hoch3fts, energy = 0.028047385344000003\n",
      "27: hoch3fcomp2 -> hoch3fts, energy = 0.01752961584\n",
      "28: hoch3fcomp1 -> hoch3fts, energy = 0.076014788688\n",
      "37: hcn -> hcnts, energy = 0.076652229264\n",
      "38: hnc -> hcnts, energy = 0.05258884752\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "small=db.subset(open_shell=False,max_electrons=30)\n",
    "print(small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "93e291a8-29f7-43b0-8330-31993058f00f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HF': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f8eb646f910>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f8eb646e350>}, 'MP2': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f8eb646ce80>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f8eb646c9d0>}, 'MP3': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f8eb646fd60>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f8eb646d6f0>}, 'MP4': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f8f116ff400>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f8ebf44fd60>}, 'PAPT2': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f8eb64cf130>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f8eb6df7730>}, 'PAPT3': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f8ec7ed74c0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f8ebf4504f0>}, 'PAPT4': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f8eb645fa60>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f8eb6c41ae0>}, 'CCSD': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f8eb64bbf10>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f8eb645c610>}, 'CCSD(T)': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f8eb6c7b0a0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f8eb6c7bfa0>}}\n"
     ]
    }
   ],
   "source": [
    "results = {}\n",
    "for method in methods:\n",
    "    results[method] = {}\n",
    "    for basis in bases:\n",
    "        results[method][basis] = pymolpro.database.run(small, methods[method], basis, location=project_name,\n",
    "                                                       backend=backend,\n",
    "                                                       preamble=\"core,small\", parallel=parallel)\n",
    "        if results[method][basis].failed: print(method, basis, 'failed', results[method][basis].project_directory)\n",
    "        \n",
    "print(results)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8754fdf3-1bcc-4b6f-b935-0ac9753f1fa9",
   "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>HF/cc-pVTZ</th>\n",
       "      <th>MP2/cc-pVTZ</th>\n",
       "      <th>MP3/cc-pVTZ</th>\n",
       "      <th>MP4/cc-pVTZ</th>\n",
       "      <th>PAPT;MP2/cc-pVTZ</th>\n",
       "      <th>PAPT;MP3/cc-pVTZ</th>\n",
       "      <th>PAPT;MP4/cc-pVTZ</th>\n",
       "      <th>CCSD/cc-pVTZ</th>\n",
       "      <th>CCSD(T)/cc-pVTZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAD</th>\n",
       "      <td>0.006212</td>\n",
       "      <td>0.008084</td>\n",
       "      <td>0.005266</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.005371</td>\n",
       "      <td>0.005375</td>\n",
       "      <td>0.006716</td>\n",
       "      <td>0.005618</td>\n",
       "      <td>0.007548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAXD</th>\n",
       "      <td>0.012808</td>\n",
       "      <td>0.015859</td>\n",
       "      <td>0.013608</td>\n",
       "      <td>0.018357</td>\n",
       "      <td>0.012308</td>\n",
       "      <td>0.012325</td>\n",
       "      <td>0.014324</td>\n",
       "      <td>0.010992</td>\n",
       "      <td>0.015926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSD</th>\n",
       "      <td>0.007084</td>\n",
       "      <td>0.009585</td>\n",
       "      <td>0.006171</td>\n",
       "      <td>0.011399</td>\n",
       "      <td>0.006242</td>\n",
       "      <td>0.006240</td>\n",
       "      <td>0.008643</td>\n",
       "      <td>0.006836</td>\n",
       "      <td>0.009574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSD</th>\n",
       "      <td>0.006035</td>\n",
       "      <td>-0.004139</td>\n",
       "      <td>0.000817</td>\n",
       "      <td>-0.007468</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>-0.004581</td>\n",
       "      <td>-0.002366</td>\n",
       "      <td>-0.005856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STDEVD</th>\n",
       "      <td>0.003912</td>\n",
       "      <td>0.009112</td>\n",
       "      <td>0.006448</td>\n",
       "      <td>0.009078</td>\n",
       "      <td>0.006579</td>\n",
       "      <td>0.006578</td>\n",
       "      <td>0.007726</td>\n",
       "      <td>0.006761</td>\n",
       "      <td>0.007984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        HF/cc-pVTZ  MP2/cc-pVTZ  MP3/cc-pVTZ  MP4/cc-pVTZ  PAPT;MP2/cc-pVTZ   \n",
       "MAD       0.006212     0.008084     0.005266     0.009312          0.005371  \\\n",
       "MAXD      0.012808     0.015859     0.013608     0.018357          0.012308   \n",
       "RMSD      0.007084     0.009585     0.006171     0.011399          0.006242   \n",
       "MSD       0.006035    -0.004139     0.000817    -0.007468         -0.000010   \n",
       "STDEVD    0.003912     0.009112     0.006448     0.009078          0.006579   \n",
       "\n",
       "        PAPT;MP3/cc-pVTZ  PAPT;MP4/cc-pVTZ  CCSD/cc-pVTZ  CCSD(T)/cc-pVTZ  \n",
       "MAD             0.005375          0.006716      0.005618         0.007548  \n",
       "MAXD            0.012325          0.014324      0.010992         0.015926  \n",
       "RMSD            0.006240          0.008643      0.006836         0.009574  \n",
       "MSD             0.000016         -0.004581     -0.002366        -0.005856  \n",
       "STDEVD          0.006578          0.007726      0.006761         0.007984  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyse=pymolpro.database.analyse([result['cc-pVTZ'] for result in results.values()],small)\n",
    "analyse['reaction statistics']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e35b1188-3a64-4c49-bfde-c79c76b67225",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "methods_pruned = [method for method in methods if (method != 'HF') and (method != 'CCSD(T)')]\n",
    "bases_pruned = ['cc-pVTZ', 'cc-pVDZ']\n",
    "fig, panes = plt.subplots(nrows=1, ncols=len(bases_pruned), sharey=True, figsize=(18, 6))\n",
    "\n",
    "for pane in range(len(bases_pruned)):\n",
    "    data = []\n",
    "    for method in methods_pruned:\n",
    "        data.append(\n",
    "            pymolpro.database.analyse(results[method][bases_pruned[pane]],\n",
    "                                      results['CCSD(T)']['cc-pVTZ'],'kJ/mol')['reaction energy deviations'].to_numpy()[:, 0]\n",
    "        )\n",
    "    panes[pane].violinplot(data, showmeans=True, showextrema=True, vert=True, bw_method='silverman')\n",
    "    panes[pane].set_xticks(range(1, len(methods_pruned) + 1), labels=methods_pruned, rotation=-90)\n",
    "    panes[pane].set_title(bases_pruned[pane])\n",
    "panes[0].set_ylabel('Error / kJ/mol')\n",
    "plt.savefig(project_name + \".violin.pdf\")\n",
    "df = pd.DataFrame(data)\n",
    "df.to_excel(\"PAPT.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "769892ba-0bd5-4fce-b73e-7a5bf7f15d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<pymolpro.database.Database at 0x7f8eb6ace260>]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrapolate=pymolpro.database.basis_extrapolate(results[method].values(), results[\"HF\"].values())\n",
    "extrapolate"
   ]
  }
 ],
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