BHDIV10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "52523e2a-5114-4afd-ab7c-7c71e73c3ded",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymolpro\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1335753c-8ba3-4c4c-be16-2fe626102890",
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = 'local' \n",
    "project_name = 'BHDIV10_methodtest'\n",
    "parallel = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e6920658-2ea5-4047-b8c2-0bb7217c8bfc",
   "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": 111,
   "id": "1d079691-3970-47fe-90a6-85be0c12fdc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = pymolpro.database.load(\"GMTKN55_BHDIV10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "15ae8ce9-7011-4500-b7a8-4ecd468411f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GMTKN55 BHDIV10 (closed shell only) (maximum number of electrons 30):\n",
      "\n",
      "Molecules:\n",
      "ts5: {'geometry': '10\\n  \\n B     0.0000000    0.0000000   -2.3759884 \\n H     0.5905364    1.0228391   -2.5936411 \\n H     0.5905364   -1.0228391   -2.5936411 \\n H    -1.1810728    0.0000000   -2.5936411 \\n P    -0.0000000    0.0000000    1.7684168 \\n H     0.6165007    1.0678105    2.4225352 \\n H     0.6165007   -1.0678105    2.4225352 \\n H    -1.2330014    0.0000000    2.4225352 \\n H     0.0000000    0.0000000   -1.0070281 \\n H    -0.0000000    0.0000000   -0.1475438 \\n', 'description': 'ts5'}\n",
      "ts4: {'geometry': '11\\n  \\n C    -0.2859289    0.9184703    1.0736652 \\n C    -0.4034021   -0.6529505   -0.3445861 \\n C     0.7266145   -0.7363334   -1.2029949 \\n H    -0.1370899    1.9111760    0.6466858 \\n H     0.4520114    0.7052124    1.8459804 \\n H     0.4779536    0.2142783   -0.1301686 \\n H    -1.2954707    0.8256305    1.4690274 \\n H    -1.3320510   -0.2613236   -0.7376201 \\n H    -0.5171754   -1.3832741    0.4455098 \\n H     0.7429527   -0.1922166   -2.1336196 \\n H     1.5715858   -1.3486694   -0.9318795 \\n', 'description': 'ts4'}\n",
      "ed4: {'geometry': '11\\n  \\n C    -0.2692331    1.5323856    1.6480625 \\n C    -0.3350059   -1.3093287   -1.0219199 \\n C     0.7843782   -1.2453943   -1.7259045 \\n H    -0.2270821    2.5841057    1.3754968 \\n H     0.3752031    1.3551361    2.5057916 \\n H     0.0655748    0.9253778    0.8098806 \\n H    -1.2922149    1.2660609    1.9032646 \\n H    -1.2276984   -0.7725013   -1.3185237 \\n H    -0.4079332   -1.9000042   -0.1170863 \\n H     0.8570528   -0.6540376   -2.6302551 \\n H     1.6769587   -1.7817999   -1.4288065 \\n', 'description': 'ed4'}\n",
      "ed5: {'geometry': '10\\n  \\n B     0.0000000    0.0000000   -3.0934966 \\n H     0.5950210    1.0306066   -3.1108480 \\n H     0.5950210   -1.0306066   -3.1108480 \\n H    -1.1900420    0.0000000   -3.1108480 \\n P     0.0000000    0.0000000    2.8489335 \\n H     0.5938246    1.0285344    3.6029291 \\n H     0.5938246   -1.0285344    3.6029291 \\n H    -1.1876492    0.0000000    3.6029291 \\n H     0.0000000    0.0000000   -0.9896633 \\n H     0.0000000    0.0000000   -0.2420169 \\n', 'description': 'ed5'}\n",
      "ed10: {'geometry': '6\\n  \\n C    -0.0000000    0.0000000   -0.4861410 \\n C     0.0000000    0.0000000    0.9653746 \\n N     0.0000000    0.0000000    2.1145426 \\n H    -0.5102145    0.8837175   -0.8645921 \\n H    -0.5102145   -0.8837175   -0.8645921 \\n H     1.0204291    0.0000000   -0.8645921 \\n', 'description': 'ed10'}\n",
      "ts10: {'geometry': '6\\n  \\n C    -0.3457134   -0.1728909    0.0000000 \\n C     1.3843767   -0.0520074    0.0000000 \\n N     0.9800815    1.0640658    0.0000000 \\n H    -1.0272588    0.6727741    0.0000000 \\n H    -0.4957430   -0.7559709    0.9004849 \\n H    -0.4957430   -0.7559709   -0.9004849 \\n', 'description': 'ts10'}\n",
      "\n",
      "Reactions:\n",
      "4: ed4 -> ts4, energy = 0.1532566504848\n",
      "5: ed5 -> ts5, energy = 0.0254020069536\n",
      "10: ed10 -> ts10, energy = 0.10347254149920002\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "small=db.subset(open_shell=False,max_electrons=30)\n",
    "print(small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8b9e674b-1f0c-4797-8388-fb527bd2c995",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HF': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f2f9ee9bf10>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f2f9edee170>}, 'MP2': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f2f9e7c1bd0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f2f9edeeb30>}, 'MP3': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f2f9ee9a650>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f2f9e5e05e0>}, 'MP4': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f2f9f120d90>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f2f9e843190>}, 'PAPT2': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f2f9e843850>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f302d1323e0>}, 'PAPT3': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f2f9e3234c0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f304c6543d0>}, 'PAPT4': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f30655d2230>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f30655d24a0>}, 'CCSD': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f2f9e8eacb0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f2f9ee9edd0>}, 'CCSD(T)': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f2f9ee9f2b0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f2f9dee7580>}}\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": 114,
   "id": "24382018-4810-4188-aee1-0b23c2bd79f2",
   "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>0.019471</td>\n",
       "      <td>0.004558</td>\n",
       "      <td>0.003433</td>\n",
       "      <td>0.002391</td>\n",
       "      <td>0.003533</td>\n",
       "      <td>0.003664</td>\n",
       "      <td>0.001654</td>\n",
       "      <td>0.004123</td>\n",
       "      <td>0.001688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAXD</th>\n",
       "      <td>0.036320</td>\n",
       "      <td>0.007710</td>\n",
       "      <td>0.005642</td>\n",
       "      <td>0.003798</td>\n",
       "      <td>0.007188</td>\n",
       "      <td>0.007417</td>\n",
       "      <td>0.002744</td>\n",
       "      <td>0.007807</td>\n",
       "      <td>0.002024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSD</th>\n",
       "      <td>0.024249</td>\n",
       "      <td>0.005081</td>\n",
       "      <td>0.003865</td>\n",
       "      <td>0.002702</td>\n",
       "      <td>0.004506</td>\n",
       "      <td>0.004618</td>\n",
       "      <td>0.001896</td>\n",
       "      <td>0.005001</td>\n",
       "      <td>0.001744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSD</th>\n",
       "      <td>0.018789</td>\n",
       "      <td>0.000582</td>\n",
       "      <td>0.002568</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.003533</td>\n",
       "      <td>0.003664</td>\n",
       "      <td>-0.000176</td>\n",
       "      <td>0.004123</td>\n",
       "      <td>0.000339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STDEVD</th>\n",
       "      <td>0.018776</td>\n",
       "      <td>0.006182</td>\n",
       "      <td>0.003537</td>\n",
       "      <td>0.003305</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>0.003443</td>\n",
       "      <td>0.002312</td>\n",
       "      <td>0.003467</td>\n",
       "      <td>0.002096</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       0.019471     0.004558     0.003433     0.002391          0.003533  \\\n",
       "MAXD      0.036320     0.007710     0.005642     0.003798          0.007188   \n",
       "RMSD      0.024249     0.005081     0.003865     0.002702          0.004506   \n",
       "MSD       0.018789     0.000582     0.002568     0.000141          0.003533   \n",
       "STDEVD    0.018776     0.006182     0.003537     0.003305          0.003426   \n",
       "\n",
       "        PAPT;MP3/cc-pVTZ  PAPT;MP4/cc-pVTZ  CCSD/cc-pVTZ  CCSD(T)/cc-pVTZ  \n",
       "MAD             0.003664          0.001654      0.004123         0.001688  \n",
       "MAXD            0.007417          0.002744      0.007807         0.002024  \n",
       "RMSD            0.004618          0.001896      0.005001         0.001744  \n",
       "MSD             0.003664         -0.000176      0.004123         0.000339  \n",
       "STDEVD          0.003443          0.002312      0.003467         0.002096  "
      ]
     },
     "execution_count": 114,
     "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": 122,
   "id": "de5ce205-4a4f-4240-8cc3-cdc81425c50e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "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": 117,
   "id": "6725f031-2fd6-4d85-a997-82b37ae89f17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<pymolpro.database.Database at 0x7f30653ffd30>]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrapolate=pymolpro.database.basis_extrapolate(results[method].values(), results[\"HF\"].values())\n",
    "extrapolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22807025-a2e5-4588-b138-e0accfe98cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0b99d7b5-0e3e-48fc-ab13-bf07092e9673",
   "metadata": {
    "tags": []
   },
   "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>PAPT;MP2/cc-pVTZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.365416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.733909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PAPT;MP2/cc-pVTZ\n",
       "4          16.074325\n",
       "5           6.365416\n",
       "10          2.733909"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_table=pymolpro.database.analyse(results['PAPT;MP2']['cc-pVTZ'],results['CCSD(T)']['cc-pVTZ'],'kJ/mol')['reaction energy deviations']\n",
    "error_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "145f80fd-308f-406c-9704-d9ebd5c385ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_max=error_table['PAPT;MP2/cc-pVTZ'].idxmax()\n",
    "key_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d8e49308-e697-4c1f-9726-8c659ffa8d77",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'reaction energies':     PAPT;MP2/cc-pVTZ\n",
       " 4         421.248022\n",
       " 5          67.696341\n",
       " 10        279.582580,\n",
       " 'reaction energy deviations':     PAPT;MP2/cc-pVTZ\n",
       " 4          16.074325\n",
       " 5           6.365416\n",
       " 10          2.733909,\n",
       " 'reaction statistics':         PAPT;MP2/cc-pVTZ\n",
       " MAD             8.391217\n",
       " MAXD           16.074325\n",
       " RMSD           10.105720\n",
       " MSD             8.391217\n",
       " STDEVD          6.897070,\n",
       " 'molecule energies':       PAPT;MP2/cc-pVTZ\n",
       " ts5     -972564.307807\n",
       " ts4     -311775.620698\n",
       " ed4     -312196.868721\n",
       " ed5     -972632.004148\n",
       " ed10    -348008.684205\n",
       " ts10    -347729.101625,\n",
       " 'molecule energy deviations':       PAPT;MP2/cc-pVTZ\n",
       " ts5          21.223866\n",
       " ts4          60.743016\n",
       " ed4          44.668691\n",
       " ed5          14.858450\n",
       " ed10         62.324754\n",
       " ts10         65.058663,\n",
       " 'molecule statistics':         PAPT;MP2/cc-pVTZ\n",
       " MAD            44.812907\n",
       " MAXD           65.058663\n",
       " RMSD           49.114215\n",
       " MSD            44.812907\n",
       " STDEVD         22.018433}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pymolpro.database.analyse(results['PAPT;MP2']['cc-pVTZ'],results['CCSD(T)']['cc-pVTZ'],'kJ/mol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5333399-c053-4643-857c-cec11ba0305b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Value'].idxmax()]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}