FH51.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bbe4ef64-c985-49c7-bf3d-bd0358bec342",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymolpro\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6c3db252-0cd1-4a61-bbf6-68ab3f182dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = 'local' \n",
    "project_name = 'FH51_methodtest'\n",
    "parallel = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 33,
   "id": "582c7d92-df29-452b-8f25-45cd9a523440",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = pymolpro.database.load(\"GMTKN55_FH51\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0eed350d-a331-4901-b5d6-03c0e39de744",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GMTKN55 FH51 (closed shell only) (maximum number of electrons 40):\n",
      "\n",
      "Molecules:\n",
      "H2O: {'geometry': '           3\\n  \\n    O         -1.09989026     -0.36122310      0.00000000\\n    H         -0.12964877     -0.31792312      0.00000000\\n    H         -1.38294827      0.56782190      0.00000000\\n', 'description': 'H2O'}\n",
      "1-pentene: {'geometry': '          15\\n  \\n    C         -3.68308053      0.94452211     -0.01656012\\n    H         -3.68590013     -0.15545023      0.01932632\\n    H         -3.15545960      1.24410275     -0.93472391\\n    H         -4.72649383      1.27817511     -0.10830711\\n    C         -3.01404275      1.53769362      1.22534673\\n    H         -3.04848053      2.63830850      1.18144679\\n    H         -3.58173640      1.25027510      2.12610967\\n    C         -1.54958284      1.09017006      1.38850510\\n    H         -1.52589130     -0.01495244      1.41907116\\n    H         -0.96721377      1.39104970      0.50204330\\n    C         -0.89999612      1.63188771      2.62799409\\n    H         -1.38186039      1.36273802      3.57573515\\n    C          0.18641143      2.40913614      2.65699083\\n    H          0.60540367      2.77523978      3.59524962\\n    H          0.70012704      2.70592330      1.73893155\\n', 'description': '1-pentene'}\n",
      "H2: {'geometry': '           2\\n  \\n    H          0.07172858     -0.99088844      0.00000000\\n    H         -0.67901788     -0.99088844      0.00000000\\n', 'description': 'H2'}\n",
      "2-pentyne: {'geometry': '          13\\n  \\n    C         -1.15652361      2.93352478      1.38638873\\n    H         -1.85899517      3.64036578      1.84778981\\n    H         -0.88476369      3.31466237      0.39188227\\n    H         -0.25010039      2.90547455      2.00563584\\n    C         -1.78583109      1.53234273      1.27366635\\n    H         -2.67209324      1.57854710      0.61845852\\n    H         -1.07752237      0.85015202      0.77388167\\n    C         -2.16996609      0.96729694      2.56450663\\n    C         -2.47846590      0.52534993      3.65250700\\n    C         -2.85195129     -0.01527234      4.95275370\\n    H         -2.42391460     -1.01780635      5.10694049\\n    H         -2.49335749      0.62859056      5.77043736\\n    H         -3.94494133     -0.10110164      5.05177549\\n', 'description': '2-pentyne'}\n",
      "trans-2-pentene: {'geometry': '          15\\n  \\n    C         -5.76745308     -0.23855881      0.00098197\\n    H         -5.43699475     -1.27862615     -0.13962232\\n    H         -5.26493952      0.37563760     -0.75972990\\n    H         -6.84967954     -0.20282894     -0.19007522\\n    C         -5.43686852      0.26196541      1.41687754\\n    H         -5.78556007      1.30004079      1.53903509\\n    H         -6.00279075     -0.34487505      2.14602077\\n    C         -3.97190847      0.18462218      1.73816540\\n    H         -3.52089719     -0.81551063      1.68982361\\n    C         -3.19033013      1.22351380      2.05655312\\n    H         -3.64348235      2.22177651      2.10034189\\n    C         -1.72602012      1.14530619      2.36798530\\n    H         -1.13539318      1.76498143      1.67365476\\n    H         -1.51110898      1.52289784      3.38101944\\n    H         -1.35458411      0.11292488      2.30385288\\n', 'description': 'trans-2-pentene'}\n",
      "C2H5CO2H: {'geometry': '          11\\n  \\n    C          0.84569904      0.12713761      0.13946157\\n    O          2.05989851      0.13279236      0.15296094\\n    C         -0.03711667     -1.08753667     -0.04275033\\n    H         -0.64680710     -1.19719475      0.86842957\\n    H          0.62855514     -1.95668832     -0.10873027\\n    C         -0.95678165     -0.99781798     -1.27012836\\n    H         -1.56228772     -1.91016058     -1.35559181\\n    H         -1.63656465     -0.13999836     -1.19437542\\n    H         -0.37318621     -0.89300682     -2.19519053\\n    O          0.10954038      1.27039295      0.29899113\\n    H          0.76324433      1.99277608      0.40588619\\n', 'description': 'C2H5CO2H'}\n",
      "NH3: {'geometry': '           4\\n  \\n    N         -1.04378426     -0.09487228      0.00000000\\n    H         -0.65570104     -1.04047940      0.00000000\\n    H         -0.65567745      0.37796757      0.81892116\\n    H         -0.65567745      0.37796757     -0.81892116\\n', 'description': 'NH3'}\n",
      "cis-2-pentene: {'geometry': '          15\\n  \\n    C         -0.50973845     -4.57409193      4.82078256\\n    H          0.12390675     -4.81524791      5.68716964\\n    H          0.15526735     -4.38166597      3.96691327\\n    H         -1.11467319     -5.46282563      4.58988793\\n    C         -1.40199201     -3.35538275      5.11048399\\n    H         -2.04014145     -3.14723348      4.23950113\\n    H         -2.08767775     -3.61035058      5.93828216\\n    C         -0.60901681     -2.13869195      5.49603240\\n    H          0.06148939     -2.28076413      6.35155563\\n    C         -0.61956324     -0.92707393      4.91947698\\n    H          0.03865347     -0.16350647      5.34781832\\n    C         -1.43816619     -0.47482642      3.74666948\\n    H         -2.11673732      0.34474903      4.03474932\\n    H         -0.79185021     -0.07516951      2.94871813\\n    H         -2.04903062     -1.27818940      3.31602797\\n', 'description': 'cis-2-pentene'}\n",
      "C2H5CONH2: {'geometry': '          12\\n  \\n    C          0.70338207      0.41072619     -0.43358139\\n    O          1.38149083      0.42641087     -1.45571311\\n    C         -0.26121262     -0.72702314     -0.09377997\\n    H         -1.04013345     -0.37584786      0.60160992\\n    H          0.32785958     -1.48083385      0.45638787\\n    N          0.78095173      1.40866259      0.50263688\\n    H          0.23673184      1.39536412      1.35566886\\n    H          1.45273932      2.15458782      0.35799207\\n    C         -0.88042004     -1.35872278     -1.34035635\\n    H         -1.46910601     -2.24747287     -1.07535319\\n    H         -1.54325977     -0.65017538     -1.85613339\\n    H         -0.09397911     -1.65231338     -2.04644039\\n', 'description': 'C2H5CONH2'}\n",
      "\n",
      "Reactions:\n",
      "13: 2-pentyne + H2 -> trans-2-pentene, energy = -0.0714252165408\n",
      "14: 2-pentyne + H2 -> cis-2-pentene, energy = -0.0695925748848\n",
      "45: 1-pentene -> trans-2-pentene, energy = -0.0033624990384\n",
      "47: cis-2-pentene -> trans-2-pentene, energy = -0.0018326416559999999\n",
      "49: C2H5CO2H + NH3 -> C2H5CONH2 + H2O, energy = -0.0006852486192\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "small=db.subset(open_shell=False,max_electrons=40)\n",
    "print(small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "93e291a8-29f7-43b0-8330-31993058f00f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HF': {'cc-pVDZ': <pymolpro.database.Database object at 0x7fef7b0b5b70>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7fef8a63f3a0>}, 'MP2': {'cc-pVDZ': <pymolpro.database.Database object at 0x7fef7b12ab00>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7fef7b0b6710>}, 'MP3': {'cc-pVDZ': <pymolpro.database.Database object at 0x7ff00bf160b0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7ff00be99720>}, 'MP4': {'cc-pVDZ': <pymolpro.database.Database object at 0x7fef7b0b61a0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7fef7b0b5510>}, 'PAPT2': {'cc-pVDZ': <pymolpro.database.Database object at 0x7fef7b0b4df0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7fef7b172b00>}, 'PAPT3': {'cc-pVDZ': <pymolpro.database.Database object at 0x7ff024a046a0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7fef7b0d9f30>}, 'PAPT4': {'cc-pVDZ': <pymolpro.database.Database object at 0x7ff024781660>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7fef7b0db8e0>}, 'CCSD': {'cc-pVDZ': <pymolpro.database.Database object at 0x7fef7b100df0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7fef899cfc70>}, 'CCSD(T)': {'cc-pVDZ': <pymolpro.database.Database object at 0x7fef899cc9d0>, 'cc-pVTZ': <pymolpro.database.Database object at 0x7fef7b101d50>}}\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": 36,
   "id": "8754fdf3-1bcc-4b6f-b935-0ac9753f1fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "\n",
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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>0.001406</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.002552</td>\n",
       "      <td>0.001011</td>\n",
       "      <td>0.002014</td>\n",
       "      <td>0.001963</td>\n",
       "      <td>0.001374</td>\n",
       "      <td>0.001436</td>\n",
       "      <td>0.001126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAXD</th>\n",
       "      <td>0.002823</td>\n",
       "      <td>0.002750</td>\n",
       "      <td>0.005729</td>\n",
       "      <td>0.002652</td>\n",
       "      <td>0.004084</td>\n",
       "      <td>0.003969</td>\n",
       "      <td>0.002193</td>\n",
       "      <td>0.002499</td>\n",
       "      <td>0.002192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSD</th>\n",
       "      <td>0.001689</td>\n",
       "      <td>0.001763</td>\n",
       "      <td>0.003537</td>\n",
       "      <td>0.001323</td>\n",
       "      <td>0.002554</td>\n",
       "      <td>0.002486</td>\n",
       "      <td>0.001644</td>\n",
       "      <td>0.001719</td>\n",
       "      <td>0.001353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSD</th>\n",
       "      <td>0.000961</td>\n",
       "      <td>0.001035</td>\n",
       "      <td>-0.002174</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>-0.001395</td>\n",
       "      <td>-0.001339</td>\n",
       "      <td>-0.000509</td>\n",
       "      <td>-0.000656</td>\n",
       "      <td>-0.000249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STDEVD</th>\n",
       "      <td>0.001553</td>\n",
       "      <td>0.001596</td>\n",
       "      <td>0.003119</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>0.002391</td>\n",
       "      <td>0.002342</td>\n",
       "      <td>0.001748</td>\n",
       "      <td>0.001777</td>\n",
       "      <td>0.001486</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       0.001406     0.001451     0.002552     0.001011          0.002014  \\\n",
       "MAXD      0.002823     0.002750     0.005729     0.002652          0.004084   \n",
       "RMSD      0.001689     0.001763     0.003537     0.001323          0.002554   \n",
       "MSD       0.000961     0.001035    -0.002174     0.000717         -0.001395   \n",
       "STDEVD    0.001553     0.001596     0.003119     0.001244          0.002391   \n",
       "\n",
       "        PAPT;MP3/cc-pVTZ  PAPT;MP4/cc-pVTZ  CCSD/cc-pVTZ  CCSD(T)/cc-pVTZ  \n",
       "MAD             0.001963          0.001374      0.001436         0.001126  \n",
       "MAXD            0.003969          0.002193      0.002499         0.002192  \n",
       "RMSD            0.002486          0.001644      0.001719         0.001353  \n",
       "MSD            -0.001339         -0.000509     -0.000656        -0.000249  \n",
       "STDEVD          0.002342          0.001748      0.001777         0.001486  "
      ]
     },
     "execution_count": 36,
     "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": 38,
   "id": "e35b1188-3a64-4c49-bfde-c79c76b67225",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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07w9/+IP23Xdf9fb2pl0KAABAqpjjJ4M5fvyY4yNqhOAAAMC1zZs36z/+4z80d+7ctEsBAAAAEAHm+IgaITgAAAAAAAAAILMKaRcAAAAAAAAAAEBcCMEBAAAAAAAAAJlFCA4AAAAAAAAAyCxCcAAAAAAAAABAZhGCAwAAAAAAAAAyixAcAAAAAAAAAJBZhOAAAAAAAAAAgMwiBAcAAAAAAAAAZBYhOAAAAAAAAAAgswjBAQAAAAAAAACZ9f8BWTzWyJ0Vuo0AAAAASUVORK5CYII=\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "31665975-be87-41dd-9a4a-6e79f0691b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<pymolpro.database.Database at 0x7ff00bb2a710>]"
      ]
     },
     "execution_count": 39,
     "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
}