WCPT18.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70b4ee67-7922-4cc8-9928-65d1a5df85fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mamba upgrade -y pymolpro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9ff0dc04-3d3d-4f39-9ca6-61dcea10363a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymolpro\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8c664f3b-8cbe-4b53-b026-c4fd388d8f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = 'local' \n",
    "project_name = 'WCPT18_methodtest'\n",
    "parallel = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2a0711a2-9721-42b4-8052-61b674ee754a",
   "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": 69,
   "id": "5db52bea-7126-4bea-83ff-65ac05451316",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = pymolpro.database.load(\"GMTKN55_WCPT18\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a58cdb14-9de8-4ef8-bb23-b6c820cfdbc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GMTKN55 WCPT18 (closed shell only) (maximum number of electrons 30):\n",
      "\n",
      "Molecules:\n",
      "ts5: {'geometry': '8\\n \\n C -0.077016 0.551986 0.027408\\n H -0.238295 1.625354 0.118269\\n C 1.139606 -0.193208 -0.031496\\n H 1.355044 -0.698094 0.908034\\n H -0.073907 -1.134900 -0.245811\\n H 2.028949 0.223228 -0.494171\\n N -1.065094 -0.300004 -0.078808\\n H -1.991673 -0.068229 0.289860\\n', 'description': 'ts5'}\n",
      "ts2: {'geometry': '6\\n \\n C 0.000000 0.509558 0.000000\\n H 0.037758 1.595479 0.000000\\n O -1.099586 -0.147769 0.000000\\n H 1.969129 -0.087220 0.000000\\n H -0.131827 -1.064473 0.000000\\n N 0.988804 -0.331283 0.000000\\n', 'description': 'ts2'}\n",
      "ts3: {'geometry': '7\\n \\n C -0.110274 0.529476 0.046417\\n H -0.295808 1.601222 0.124294\\n O -1.106394 -0.268578 -0.020297\\n C 1.104116 -0.175658 -0.047198\\n H 1.271359 -0.824225 0.811942\\n H -0.098402 -1.028334 -0.338969\\n H 2.01095         0.277049 -0.430205\\n', 'description': 'ts3'}\n",
      "ts4: {'geometry': '7\\n \\n C 0.000000 0.000000 0.523306\\n H 0.000000 0.000000 1.609625\\n N 0.000000 1.054818 -0.264887\\n H -0.314690 1.964618 0.041770\\n H 0.000000 0.000000 -1.124591\\n N 0.000000 -1.054818 -0.264887\\n H 0.314690 -1.964618 0.041770\\n', 'description': 'ts4'}\n",
      "reac2: {'geometry': '6\\n \\n C 0.000000 0.416575 0.000000\\n H -0.263416 1.473336 0.000000\\n O -1.110471 -0.343273 0.000000\\n H 1.876501 0.630870 0.000000\\n H -0.825348 -1.269969 0.000000\\n N 1.156576 -0.083929 0.000000\\n', 'description': 'reac2'}\n",
      "reac5: {'geometry': '8\\n \\n C -0.069675 0.426757 -0.000517\\n H -0.029832 1.509908 -0.008099\\n C -1.249182 -0.196033 0.013868\\n H -1.330339 -1.275694 0.016609\\n H -2.164964 0.373561 0.006734\\n H 1.220598 -1.153069 0.137248\\n N 1.181782 -0.169417 -0.079545\\n H 1.945210 0.346868 0.324216\\n', 'description': 'reac5'}\n",
      "reac4: {'geometry': '7\\n \\n C -0.122877 0.390310 -0.000787\\n H -0.118122 1.483913 -0.004726\\n N -1.171301 -0.329866 0.010781\\n H -2.008655 0.241760 0.002981\\n H 1.213885 -1.140955 0.095493\\n N 1.132820 -0.147882 -0.053220\\n H 1.919523 0.417657 0.208046\\n', 'description': 'reac4'}\n",
      "reac3: {'geometry': '7\\n \\n C 0.000000 0.441567 0.000000\\n H -0.160761 1.511265 0.000000\\n O -1.190786 -0.218672 0.000000\\n C 1.211912 -0.100389 0.000000\\n H 1.366272 -1.172267 0.000000\\n H 2.084631 0.533570 0.000000\\n H -1.035326 -1.170266 0.000000\\n', 'description': 'reac3'}\n",
      "ts1: {'geometry': '5\\n \\n C 0.000000 0.000000 0.489758\\n H 0.000000 0.000000 1.576776\\n H 0.000000 0.000000 -1.018930\\n O 0.000000 1.047477 -0.218525\\n O 0.000000 -1.047477 -0.218525\\n', 'description': 'ts1'}\n",
      "ts6: {'geometry': '9\\n \\n C 0.057227 -0.404548 0.000000\\n H 0.094881 -1.490374 0.000000\\n C 0.057227 0.193471 1.290465\\n H 0.016071 1.265997 1.410672\\n H -1.068272 -0.071437 0.000000\\n H -0.044418 -0.432272 2.160920\\n C 0.057227 0.193471 -1.290465\\n H -0.044418 -0.432272 -2.160920\\n H 0.016071 1.265997 -1.410672\\n', 'description': 'ts6'}\n",
      "reac6: {'geometry': '9\\n \\n C 0.000000 0.471708 0.000000\\n H -0.276412 1.522311 0.000000\\n C -1.134329 -0.504429 0.000000\\n H -0.776700 -1.534190 0.000000\\n H -1.772470 -0.363824 0.876336\\n H -1.772470 -0.363824 -0.876336\\n C 1.287947 0.151466 0.000000\\n H 2.060505 0.908567 0.000000\\n H 1.615839 -0.881516 0.000000\\n', 'description': 'reac6'}\n",
      "reac1: {'geometry': '5\\n \\n C 0.000000 0.421035 0.000000\\n H -0.383063 1.448042 0.000000\\n H -0.659556 -1.343644 0.000000\\n O 1.158518 0.117088 0.000000\\n O -1.028191 -0.445914 0.000000\\n', 'description': 'reac1'}\n",
      "\n",
      "Reactions:\n",
      "1: reac1 -> ts1, energy = 0.0585807889344\n",
      "2: reac2 -> ts2, energy = 0.057704308142400004\n",
      "3: reac3 -> ts3, energy = 0.097130007768\n",
      "4: reac4 -> ts4, energy = 0.0757279404288\n",
      "5: reac5 -> ts5, energy = 0.10466774257920002\n",
      "6: reac6 -> ts6, energy = 0.1294641809856\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "small=db.subset(open_shell=False,max_electrons=30)\n",
    "print(small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f1829671-6488-426f-8e59-0854283b6333",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HF': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f087a920f40>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f09014aa5f0>}, 'MP2': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f090c4d8b50>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f08587009a0>}, 'MP3': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f090151d540>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f090150ece0>}, 'MP4': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f087a903af0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f0900805300>}, 'PAPT2': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f08e3fd69b0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f0900806fe0>}, 'PAPT3': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f0900a13e20>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f0900805120>}, 'PAPT4': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f09008063b0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f08e3fa7940>}, 'CCSD': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f09009472e0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f0900a27a30>}, 'CCSD(T)': {'cc-pVDZ': <pymolpro.database.Database object at 0x7f0900a25000>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7f0900a27610>}}\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": 72,
   "id": "88145d40-9eda-46e5-8d7f-525e006367b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>0.024625</td>\n",
       "      <td>0.002931</td>\n",
       "      <td>0.006630</td>\n",
       "      <td>0.002418</td>\n",
       "      <td>0.006033</td>\n",
       "      <td>0.006008</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.005344</td>\n",
       "      <td>0.000502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAXD</th>\n",
       "      <td>0.038738</td>\n",
       "      <td>0.003974</td>\n",
       "      <td>0.009090</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.009355</td>\n",
       "      <td>0.009056</td>\n",
       "      <td>0.000807</td>\n",
       "      <td>0.010078</td>\n",
       "      <td>0.000889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSD</th>\n",
       "      <td>0.025433</td>\n",
       "      <td>0.003199</td>\n",
       "      <td>0.006744</td>\n",
       "      <td>0.002725</td>\n",
       "      <td>0.006224</td>\n",
       "      <td>0.006173</td>\n",
       "      <td>0.000582</td>\n",
       "      <td>0.005757</td>\n",
       "      <td>0.000580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSD</th>\n",
       "      <td>0.024625</td>\n",
       "      <td>-0.002931</td>\n",
       "      <td>0.006630</td>\n",
       "      <td>-0.002418</td>\n",
       "      <td>0.006033</td>\n",
       "      <td>0.006008</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.005344</td>\n",
       "      <td>-0.000123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STDEVD</th>\n",
       "      <td>0.006964</td>\n",
       "      <td>0.001406</td>\n",
       "      <td>0.001355</td>\n",
       "      <td>0.001377</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.001550</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.002346</td>\n",
       "      <td>0.000621</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       0.024625     0.002931     0.006630     0.002418          0.006033  \\\n",
       "MAXD      0.038738     0.003974     0.009090     0.004060          0.009355   \n",
       "RMSD      0.025433     0.003199     0.006744     0.002725          0.006224   \n",
       "MSD       0.024625    -0.002931     0.006630    -0.002418          0.006033   \n",
       "STDEVD    0.006964     0.001406     0.001355     0.001377          0.001677   \n",
       "\n",
       "        PAPT;MP3/cc-pVTZ  PAPT;MP4/cc-pVTZ  CCSD/cc-pVTZ  CCSD(T)/cc-pVTZ  \n",
       "MAD             0.006008          0.000507      0.005344         0.000502  \n",
       "MAXD            0.009056          0.000807      0.010078         0.000889  \n",
       "RMSD            0.006173          0.000582      0.005757         0.000580  \n",
       "MSD             0.006008          0.000296      0.005344        -0.000123  \n",
       "STDEVD          0.001550          0.000549      0.002346         0.000621  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyse=pymolpro.database.analyse([result['cc-pVTZ'] for result in results.values()],db)\n",
    "analyse['reaction statistics']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "df61126d-d853-43c4-84e0-3c29a7d06951",
   "metadata": {},
   "outputs": [
    {
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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\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6fc55236-da3b-4769-ab35-11b08ee31307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<pymolpro.database.Database at 0x7f08e3c26050>]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrapolate=pymolpro.database.basis_extrapolate(results[method].values(), results[\"HF\"].values())\n",
    "extrapolate"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:persistent] *",
   "language": "python",
   "name": "conda-env-persistent-py"
  },
  "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
}