G-retrieve-qcarchive-torsiondrive.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -q https://raw.githubusercontent.com/openforcefield/2023-workshop-vignettes/main/colab_setup.ipynb\n",
    "%run colab_setup.ipynb\n",
    "!mamba install qcportal -c conda-forge -yq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%env LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieving Result Collections\n",
    "\n",
    "**Based on the existing example**: https://github.com/openforcefield/openff-qcsubmit/blob/main/examples/retrieving-results.ipynb\n",
    "\n",
    "This example shows how QCSubmit can be used to retrieve the results of quantum chemical (QC) calculations from a [QCFractal] instance such as [QCArchive].\n",
    "\n",
    "In particular, it demonstrates how:\n",
    "\n",
    "* raw torsion drive, optimised geometry and hessian result records can be retrieved from the public\n",
    "  [QCArchive] server and stored in a result collection\n",
    "\n",
    "* the retrieved result records can be filtered and curated using a set of built-in filters\n",
    "\n",
    "* the result collection can be saved and loaded from disk\n",
    "\n",
    "[QCFractal]: http://docs.qcarchive.molssi.org/projects/qcfractal/en/latest/\n",
    "[QCArchive]: https://qcarchive.molssi.org/\n",
    "\n",
    "For the sake of clarity all verbose warnings will be disabled in this tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "\n",
    "logging.getLogger(\"openff.toolkit\").setLevel(logging.ERROR)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Retrieving result collections\n",
    "\n",
    "QCSubmit provides a suite of utilities for retrieving and curating collections of QC results directly from a running QCFractal server, or an already computed QCPortal dataset. This functionality is provided through three main classes:\n",
    "\n",
    "* `BasicResultCollection` - stores references to simple QCPortal result record that may contain energies, gradients, or hessians computed for a molecule in a single conformation.\n",
    "\n",
    "* `OptimizationResultCollection` - stores references to full optimization result records (i.e. `OptimizationRecord`\n",
    "  objects), as well as the final minimised conformer produced by the optimization.\n",
    "\n",
    "* `TorsionDriveResultCollection` - stores references to full torsion drive result records (i.e. `TorsionDriveRecord`\n",
    "  objects), as well as the minimum energy conformer associated with each torsion angle that was scanned.\n",
    "\n",
    "Each of these collections can be generated directly from a running `QCFractal` server using the `from_server` class\n",
    "method.\n",
    "\n",
    "We begin by creating a QCPortal `FractalClient` instance that will allow us to communicate with the running\n",
    "server. By default, `FractalClient` connects to the main QCArchive server:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from qcportal import FractalClient\n",
    "\n",
    "qc_client = FractalClient()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Other servers can be accessed by providing the server's URI.\n",
    "\n",
    "We can then use this to generate our result collections:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entries={'https://api.qcarchive.molssi.org:443/': [TorsionDriveResult(type='torsion', record_id='104348495', cmiles='[H:29][C@@:24]([C:25](=[O:26])[N:33]([H:40])[C@:34]([H:41])([C:35](=[O:36])[N:49]([H:51])[C:50]([H:52])([H:53])[H:54])[C:37]([H:42])([C:38]([H:43])([H:44])[H:45])[C:39]([H:46])([H:47])[H:48])([C:27]([H:30])([H:31])[H:32])[N:23]([H:28])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='JHBKYDIWHUGPSD-NQRAZRSKNA-N'), TorsionDriveResult(type='torsion', record_id='104348544', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:25])[C@:18]([H:26])([C:19](=[O:20])[N:30]([H:35])[C@:31]([H:36])([C:32](=[O:33])[N:40]([H:42])[C:41]([H:43])([H:44])[H:45])[C:34]([H:37])([H:38])[H:39])[C:21]([H:27])([H:28])[C:22](=[O:23])[O:24][H:29])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='DFRSMLTYXUADOY-WMXHRXQGNA-N'), TorsionDriveResult(type='torsion', record_id='104348578', cmiles='[H:32][C@@:24]([C:25](=[O:26])[N:36]([H:43])[C@:37]([H:44])([C:38](=[O:39])[N:52]([H:54])[C:53]([H:55])([H:56])[H:57])[C:40]([H:45])([C:41]([H:46])([H:47])[H:48])[C:42]([H:49])([H:50])[H:51])([C:27]([H:33])([H:34])[C:28](=[O:29])[O:30][H:35])[N:23]([H:31])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='XBRMABZLNHHXPN-SWFFACQVNA-N'), TorsionDriveResult(type='torsion', record_id='104348638', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:25])[C@:18]([H:26])([C:19](=[O:20])[N:31]([H:36])[C@:32]([H:37])([C:33](=[O:34])[N:41]([H:43])[C:42]([H:44])([H:45])[H:46])[C:35]([H:38])([H:39])[H:40])[C:21]([H:27])([H:28])[C:22](=[O:23])[N:24]([H:29])[H:30])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='KYQIKKWSTMGEQA-RIYGDZFYNA-N'), TorsionDriveResult(type='torsion', record_id='104348639', cmiles='[H:32][C@@:24]([C:25](=[O:26])[N:37]([H:44])[C@:38]([H:45])([C:39](=[O:40])[N:53]([H:55])[C:54]([H:56])([H:57])[H:58])[C:41]([H:46])([C:42]([H:47])([H:48])[H:49])[C:43]([H:50])([H:51])[H:52])([C:27]([H:33])([H:34])[C:28](=[O:29])[N:30]([H:35])[H:36])[N:23]([H:31])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='PWDCCXZFTIKFCG-LJWVSBLSNA-N'), TorsionDriveResult(type='torsion', record_id='104348640', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:25])[C@:18]([H:26])([C:19](=[O:20])[N:29]([H:34])[C@:30]([H:35])([C:31](=[O:32])[N:39]([H:41])[C:40]([H:42])([H:43])[H:44])[C:33]([H:36])([H:37])[H:38])[C:21]([H:27])([H:28])[C:22](=[O:24])[O-:23])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='DFRSMLTYXUADOY-YXJNUYKJNA-M'), TorsionDriveResult(type='torsion', record_id='104349083', cmiles='[H:30][C@@:24]([C:25](=[O:26])[N:34]([H:41])[C@:35]([H:42])([C:36](=[O:37])[N:50]([H:52])[C:51]([H:53])([H:54])[H:55])[C:38]([H:43])([C:39]([H:44])([H:45])[H:46])[C:40]([H:47])([H:48])[H:49])([C:27]([H:31])([H:32])[S:28][H:33])[N:23]([H:29])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='NXZIIAGLOSBSES-BCDINFCLNA-N'), TorsionDriveResult(type='torsion', record_id='104350274', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:26])[C@:18]([H:27])([C:19](=[O:20])[N:33]([H:38])[C@:34]([H:39])([C:35](=[O:36])[N:43]([H:45])[C:44]([H:46])([H:47])[H:48])[C:37]([H:40])([H:41])[H:42])[C:21]([H:28])([H:29])[C:22]([H:30])([H:31])[C:23](=[O:24])[O:25][H:32])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='YVWAADJXUPAWMQ-MMEUZQAYNA-N'), TorsionDriveResult(type='torsion', record_id='104350610', cmiles='[H:33][C@@:24]([C:25](=[O:26])[N:39]([H:46])[C@:40]([H:47])([C:41](=[O:42])[N:55]([H:57])[C:56]([H:58])([H:59])[H:60])[C:43]([H:48])([C:44]([H:49])([H:50])[H:51])[C:45]([H:52])([H:53])[H:54])([C:27]([H:34])([H:35])[C:28]([H:36])([H:37])[C:29](=[O:30])[O:31][H:38])[N:23]([H:32])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='AHMFCGIFGJPMNY-UVILHNSZNA-N'), TorsionDriveResult(type='torsion', record_id='104350994', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:26])[C@:18]([H:27])([C:19](=[O:20])[N:34]([H:39])[C@:35]([H:40])([C:36](=[O:37])[N:44]([H:46])[C:45]([H:47])([H:48])[H:49])[C:38]([H:41])([H:42])[H:43])[C:21]([H:28])([H:29])[C:22]([H:30])([H:31])[C:23](=[O:24])[N:25]([H:32])[H:33])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='WGYPYUBMHJDXLX-XZTNJZJKNA-N'), TorsionDriveResult(type='torsion', record_id='104352160', cmiles='[H:33][C@@:24]([C:25](=[O:26])[N:38]([H:45])[C@:39]([H:46])([C:40](=[O:41])[N:54]([H:56])[C:55]([H:57])([H:58])[H:59])[C:42]([H:47])([C:43]([H:48])([H:49])[H:50])[C:44]([H:51])([H:52])[H:53])([C:27]([H:34])([H:35])[C:28]([H:36])([H:37])[C:29](=[O:31])[O-:30])[N:23]([H:32])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='AHMFCGIFGJPMNY-XMTYWNFINA-M'), TorsionDriveResult(type='torsion', record_id='104352631', cmiles='[H:30][C@@:25]([C:26](=[O:27])[N:34]([H:36])[C:35]([H:37])([H:38])[H:39])([C:28]([H:31])([H:32])[H:33])[N:24]([H:29])[C:19](=[O:20])[C:18]([H:22])([H:23])[N:17]([H:21])[C:9](=[O:10])[C@:8]([H:13])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='MSZWGDWDOLQBKL-IABNWRJLNA-N'), TorsionDriveResult(type='torsion', record_id='104352989', cmiles='[H:38][C@@:31]([C:32](=[O:33])[N:46]([H:48])[C:47]([H:49])([H:50])[H:51])([C:34]([H:39])([C:35]([H:40])([H:41])[H:42])[C:36]([H:43])([H:44])[H:45])[N:30]([H:37])[C:25](=[O:26])[C:24]([H:28])([H:29])[N:23]([H:27])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='ZSABDXFLMRTIHS-DLUQSFTLNA-N'), TorsionDriveResult(type='torsion', record_id='104353304', cmiles='[H:32][C:24]1=[C:22]([N:23]([C:25](=[N:26]1)[H:33])[H:31])[C:21]([H:29])([H:30])[C@@:18]([H:28])([C:19](=[O:20])[N:34]([H:39])[C@:35]([H:40])([C:36](=[O:37])[N:44]([H:46])[C:45]([H:47])([H:48])[H:49])[C:38]([H:41])([H:42])[H:43])[N:17]([H:27])[C:9](=[O:10])[C@:8]([H:13])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='AEUGKYSAGBPNIR-BOUJXIRCNA-N'), TorsionDriveResult(type='torsion', record_id='104353308', cmiles='[H:32][C:24]1=[C:22]([N+:23](=[C:25]([N:26]1[H:34])[H:33])[H:31])[C:21]([H:29])([H:30])[C@@:18]([H:28])([C:19](=[O:20])[N:35]([H:40])[C@:36]([H:41])([C:37](=[O:38])[N:45]([H:47])[C:46]([H:48])([H:49])[H:50])[C:39]([H:42])([H:43])[H:44])[N:17]([H:27])[C:9](=[O:10])[C@:8]([H:13])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='AEUGKYSAGBPNIR-ICFOULITNA-O'), TorsionDriveResult(type='torsion', record_id='104353312', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:25])[C@:18]([H:26])([C:19](=[O:20])[N:36]([H:41])[C@:37]([H:42])([C:38](=[O:39])[N:46]([H:48])[C:47]([H:49])([H:50])[H:51])[C:40]([H:43])([H:44])[H:45])[C:21]([H:27])([H:28])[C:22]([H:29])([C:23]([H:30])([H:31])[H:32])[C:24]([H:33])([H:34])[H:35])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='JTPXVQPOMDABBJ-QNCMGGKINA-N'), TorsionDriveResult(type='torsion', record_id='104353313', cmiles='[H:32][C@@:24]([C:25](=[O:26])[N:42]([H:49])[C@:43]([H:50])([C:44](=[O:45])[N:58]([H:60])[C:59]([H:61])([H:62])[H:63])[C:46]([H:51])([C:47]([H:52])([H:53])[H:54])[C:48]([H:55])([H:56])[H:57])([C:27]([H:33])([H:34])[C:28]([H:35])([C:29]([H:36])([H:37])[H:38])[C:30]([H:39])([H:40])[H:41])[N:23]([H:31])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='RKOKEJFGNWXXSA-DPCXNVFSNA-N'), TorsionDriveResult(type='torsion', record_id='104353315', cmiles='[H:33][C@@:24]([C:25](=[O:26])[N:44]([H:51])[C@:45]([H:52])([C:46](=[O:47])[N:60]([H:62])[C:61]([H:63])([H:64])[H:65])[C:48]([H:53])([C:49]([H:54])([H:55])[H:56])[C:50]([H:57])([H:58])[H:59])([C:27]([H:34])([H:35])[C:28]([H:36])([H:37])[C:29]([H:38])([H:39])[C:30]([H:40])([H:41])[N:31]([H:42])[H:43])[N:23]([H:32])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='LPEQMILZHWXGNE-FFDWGNCYNA-N'), TorsionDriveResult(type='torsion', record_id='104353318', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:25])[C@:18]([H:26])([C:19](=[O:20])[N:34]([H:39])[C@:35]([H:40])([C:36](=[O:37])[N:44]([H:46])[C:45]([H:47])([H:48])[H:49])[C:38]([H:41])([H:42])[H:43])[C:21]([H:27])([H:28])[C:22]([H:29])([H:30])[S:23][C:24]([H:31])([H:32])[H:33])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='ZGBZZLLRJFEPFE-SLHFYQOHNA-N'), TorsionDriveResult(type='torsion', record_id='104353319', cmiles='[H:32][C@@:24]([C:25](=[O:26])[N:40]([H:47])[C@:41]([H:48])([C:42](=[O:43])[N:56]([H:58])[C:57]([H:59])([H:60])[H:61])[C:44]([H:49])([C:45]([H:50])([H:51])[H:52])[C:46]([H:53])([H:54])[H:55])([C:27]([H:33])([H:34])[C:28]([H:35])([H:36])[S:29][C:30]([H:37])([H:38])[H:39])[N:23]([H:31])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='DXYKBSQHSLCOEQ-HWOCBHOBNA-N'), TorsionDriveResult(type='torsion', record_id='104353391', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:23])[C@:18]([H:24])([C:19](=[O:20])[N:28]([H:33])[C@:29]([H:34])([C:30](=[O:31])[N:38]([H:40])[C:39]([H:41])([H:42])[H:43])[C:32]([H:35])([H:36])[H:37])[C:21]([H:25])([H:26])[O:22][H:27])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='JNBICFGHFGAMFD-RCPDXPKANA-N'), TorsionDriveResult(type='torsion', record_id='104353392', cmiles='[H:30][C@@:24]([C:25](=[O:26])[N:34]([H:41])[C@:35]([H:42])([C:36](=[O:37])[N:50]([H:52])[C:51]([H:53])([H:54])[H:55])[C:38]([H:43])([C:39]([H:44])([H:45])[H:46])[C:40]([H:47])([H:48])[H:49])([C:27]([H:31])([H:32])[O:28][H:33])[N:23]([H:29])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='VHMKYKVSKWMZLN-BCDINFCLNA-N'), TorsionDriveResult(type='torsion', record_id='104353447', cmiles='[H:31][C@@:24]([C:25](=[O:26])[N:39]([H:46])[C@:40]([H:47])([C:41](=[O:42])[N:55]([H:57])[C:56]([H:58])([H:59])[H:60])[C:43]([H:48])([C:44]([H:49])([H:50])[H:51])[C:45]([H:52])([H:53])[H:54])([C:27]([H:32])([C:28]([H:33])([H:34])[H:35])[C:29]([H:36])([H:37])[H:38])[N:23]([H:30])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='UPOULXONLASJAM-HWOCBHOBNA-N')]} provenance={} type='TorsionDriveResultCollection'\n"
     ]
    }
   ],
   "source": [
    "from openff.qcsubmit.results import (\n",
    "    BasicResultCollection,\n",
    "    OptimizationResultCollection,\n",
    "    TorsionDriveResultCollection,\n",
    ")\n",
    "\n",
    "# Pull down the torsion drive records from the 'OpenFF Rowley Biaryl v1.0' dataset.\n",
    "torsion_drive_result_collection = TorsionDriveResultCollection.from_server(\n",
    "    client=qc_client,\n",
    "    # datasets=\"OpenFF Rowley Biaryl v1.0\",\n",
    "    # datasets=\"OpenFF Protein Capped 3-mer Omega v1.0\",\n",
    "    datasets=\"OpenFF Protein Capped 3-mer Backbones v1.0\",\n",
    "    spec_name=\"default\",\n",
    ")\n",
    "print(torsion_drive_result_collection)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "*Note: currently only complete results are pulled down by the `from_server` method*\n",
    "\n",
    "There are two main inputs to the `from_server` method, in addition to the fractal client:\n",
    "\n",
    "* the name(s) of the existing datasets to retrieve the results of. This can either be the name of a single dataset or a list of dataset names\n",
    "* the name of the specification used to compute the records. Each specification corresponds to a particular basis, method, program and additional settings.\n",
    "\n",
    "Let's print out some basic information about each of these result collections:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===TORSION DRIVE RESULTS===\n",
      "N RESULTS:   23\n",
      "N MOLECULES: 23\n"
     ]
    }
   ],
   "source": [
    "print(\"===TORSION DRIVE RESULTS===\")\n",
    "\n",
    "print(f\"N RESULTS:   {torsion_drive_result_collection.n_results}\")\n",
    "print(f\"N MOLECULES: {torsion_drive_result_collection.n_molecules}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "This allows results generated by multiple different servers (e.g. a local fractal instance and the public QCArchive\n",
    "server) to be stored in a single result collection object.\n",
    "\n",
    "The references to the actual data are then stored in corresponding lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[TorsionDriveResult(type='torsion', record_id='104348495', cmiles='[H:29][C@@:24]([C:25](=[O:26])[N:33]([H:40])[C@:34]([H:41])([C:35](=[O:36])[N:49]([H:51])[C:50]([H:52])([H:53])[H:54])[C:37]([H:42])([C:38]([H:43])([H:44])[H:45])[C:39]([H:46])([H:47])[H:48])([C:27]([H:30])([H:31])[H:32])[N:23]([H:28])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='JHBKYDIWHUGPSD-NQRAZRSKNA-N'),\n",
       " TorsionDriveResult(type='torsion', record_id='104348544', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:25])[C@:18]([H:26])([C:19](=[O:20])[N:30]([H:35])[C@:31]([H:36])([C:32](=[O:33])[N:40]([H:42])[C:41]([H:43])([H:44])[H:45])[C:34]([H:37])([H:38])[H:39])[C:21]([H:27])([H:28])[C:22](=[O:23])[O:24][H:29])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='DFRSMLTYXUADOY-WMXHRXQGNA-N'),\n",
       " TorsionDriveResult(type='torsion', record_id='104348578', cmiles='[H:32][C@@:24]([C:25](=[O:26])[N:36]([H:43])[C@:37]([H:44])([C:38](=[O:39])[N:52]([H:54])[C:53]([H:55])([H:56])[H:57])[C:40]([H:45])([C:41]([H:46])([H:47])[H:48])[C:42]([H:49])([H:50])[H:51])([C:27]([H:33])([H:34])[C:28](=[O:29])[O:30][H:35])[N:23]([H:31])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='XBRMABZLNHHXPN-SWFFACQVNA-N'),\n",
       " TorsionDriveResult(type='torsion', record_id='104348638', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:25])[C@:18]([H:26])([C:19](=[O:20])[N:31]([H:36])[C@:32]([H:37])([C:33](=[O:34])[N:41]([H:43])[C:42]([H:44])([H:45])[H:46])[C:35]([H:38])([H:39])[H:40])[C:21]([H:27])([H:28])[C:22](=[O:23])[N:24]([H:29])[H:30])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='KYQIKKWSTMGEQA-RIYGDZFYNA-N'),\n",
       " TorsionDriveResult(type='torsion', record_id='104348639', cmiles='[H:32][C@@:24]([C:25](=[O:26])[N:37]([H:44])[C@:38]([H:45])([C:39](=[O:40])[N:53]([H:55])[C:54]([H:56])([H:57])[H:58])[C:41]([H:46])([C:42]([H:47])([H:48])[H:49])[C:43]([H:50])([H:51])[H:52])([C:27]([H:33])([H:34])[C:28](=[O:29])[N:30]([H:35])[H:36])[N:23]([H:31])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='PWDCCXZFTIKFCG-LJWVSBLSNA-N'),\n",
       " TorsionDriveResult(type='torsion', record_id='104348640', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:25])[C@:18]([H:26])([C:19](=[O:20])[N:29]([H:34])[C@:30]([H:35])([C:31](=[O:32])[N:39]([H:41])[C:40]([H:42])([H:43])[H:44])[C:33]([H:36])([H:37])[H:38])[C:21]([H:27])([H:28])[C:22](=[O:24])[O-:23])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='DFRSMLTYXUADOY-YXJNUYKJNA-M'),\n",
       " TorsionDriveResult(type='torsion', record_id='104349083', cmiles='[H:30][C@@:24]([C:25](=[O:26])[N:34]([H:41])[C@:35]([H:42])([C:36](=[O:37])[N:50]([H:52])[C:51]([H:53])([H:54])[H:55])[C:38]([H:43])([C:39]([H:44])([H:45])[H:46])[C:40]([H:47])([H:48])[H:49])([C:27]([H:31])([H:32])[S:28][H:33])[N:23]([H:29])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='NXZIIAGLOSBSES-BCDINFCLNA-N'),\n",
       " TorsionDriveResult(type='torsion', record_id='104350274', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:26])[C@:18]([H:27])([C:19](=[O:20])[N:33]([H:38])[C@:34]([H:39])([C:35](=[O:36])[N:43]([H:45])[C:44]([H:46])([H:47])[H:48])[C:37]([H:40])([H:41])[H:42])[C:21]([H:28])([H:29])[C:22]([H:30])([H:31])[C:23](=[O:24])[O:25][H:32])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='YVWAADJXUPAWMQ-MMEUZQAYNA-N'),\n",
       " TorsionDriveResult(type='torsion', record_id='104350610', cmiles='[H:33][C@@:24]([C:25](=[O:26])[N:39]([H:46])[C@:40]([H:47])([C:41](=[O:42])[N:55]([H:57])[C:56]([H:58])([H:59])[H:60])[C:43]([H:48])([C:44]([H:49])([H:50])[H:51])[C:45]([H:52])([H:53])[H:54])([C:27]([H:34])([H:35])[C:28]([H:36])([H:37])[C:29](=[O:30])[O:31][H:38])[N:23]([H:32])[C:9](=[O:10])[C@:8]([H:15])([C:11]([H:16])([C:12]([H:17])([H:18])[H:19])[C:13]([H:20])([H:21])[H:22])[N:7]([H:14])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='AHMFCGIFGJPMNY-UVILHNSZNA-N'),\n",
       " TorsionDriveResult(type='torsion', record_id='104350994', cmiles='[H:13][C@@:8]([C:9](=[O:10])[N:17]([H:26])[C@:18]([H:27])([C:19](=[O:20])[N:34]([H:39])[C@:35]([H:40])([C:36](=[O:37])[N:44]([H:46])[C:45]([H:47])([H:48])[H:49])[C:38]([H:41])([H:42])[H:43])[C:21]([H:28])([H:29])[C:22]([H:30])([H:31])[C:23](=[O:24])[N:25]([H:32])[H:33])([C:11]([H:14])([H:15])[H:16])[N:7]([H:12])[C:1](=[O:2])[C:3]([H:4])([H:5])[H:6]', inchi_key='WGYPYUBMHJDXLX-XZTNJZJKNA-N')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torsion_drive_result_collection.entries[qc_client.address][:10]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "After running the above command, notice that the entries stored in the collection are not the actual result\n",
    "records generated and stored on the server, but rather a reference to them. In particular, the unique ID of the record is stored along with a SMILES depiction of the molecule the result was generated for.\n",
    "\n",
    "The main reason for doing this is that we often would like to be able to state which data we would like to use in\n",
    "an application without having to create multiple copies of the data. Not only can this take up large amounts of disk space, it runs the risk of data becoming out of sync with the original if the format the records are stored in changes or the local copy of the data is accidentally mutated. Storing a reference to the original data and then retrieving it when needed is typically a cleaner and safer solution.\n",
    "\n",
    "## Retrieving the result records\n",
    "\n",
    "The raw result record objects can be easily retrieved using the result collection objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(TorsionDriveRecord(id='104348495', status='COMPLETE'),\n",
       "  Molecule with name '' and SMILES '[H][C@@](C(=O)N([H])[C@]([H])(C(=O)N([H])C([H])([H])[H])C([H])(C([H])([H])[H])C([H])([H])[H])(C([H])([H])[H])N([H])C(=O)[C@]([H])(C([H])(C([H])([H])[H])C([H])([H])[H])N([H])C(=O)C([H])([H])[H]'),\n",
       " (TorsionDriveRecord(id='104348544', status='COMPLETE'),\n",
       "  Molecule with name '' and SMILES '[H][C@@](C(=O)N([H])[C@]([H])(C(=O)N([H])[C@]([H])(C(=O)N([H])C([H])([H])[H])C([H])([H])[H])C([H])([H])C(=O)O[H])(C([H])([H])[H])N([H])C(=O)C([H])([H])[H]'),\n",
       " (TorsionDriveRecord(id='104348578', status='COMPLETE'),\n",
       "  Molecule with name '' and SMILES '[H][C@@](C(=O)N([H])[C@]([H])(C(=O)N([H])C([H])([H])[H])C([H])(C([H])([H])[H])C([H])([H])[H])(C([H])([H])C(=O)O[H])N([H])C(=O)[C@]([H])(C([H])(C([H])([H])[H])C([H])([H])[H])N([H])C(=O)C([H])([H])[H]'),\n",
       " (TorsionDriveRecord(id='104348638', status='COMPLETE'),\n",
       "  Molecule with name '' and SMILES '[H][C@@](C(=O)N([H])[C@]([H])(C(=O)N([H])[C@]([H])(C(=O)N([H])C([H])([H])[H])C([H])([H])[H])C([H])([H])C(=O)N([H])[H])(C([H])([H])[H])N([H])C(=O)C([H])([H])[H]'),\n",
       " (TorsionDriveRecord(id='104348639', status='COMPLETE'),\n",
       "  Molecule with name '' and SMILES '[H][C@@](C(=O)N([H])[C@]([H])(C(=O)N([H])C([H])([H])[H])C([H])(C([H])([H])[H])C([H])([H])[H])(C([H])([H])C(=O)N([H])[H])N([H])C(=O)[C@]([H])(C([H])(C([H])([H])[H])C([H])([H])[H])N([H])C(=O)C([H])([H])[H]')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torsion_drive_records = torsion_drive_result_collection.to_records()\n",
    "torsion_drive_records[:5]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "QCSubmit seamlessly takes care of pulling the data from the server in the most efficient way making sure to take\n",
    "advantage of the pagination that QCFractal provides. Further, it attempts to cache all calls to the server so that\n",
    "multiple calls to `to_records` does not need to constantly query the server.\n",
    "\n",
    "Notice that not only are the raw result records retrieved, but also an OpenFF `Molecule` object is created for each result record. This molecule has the correct ordering and also stores any conformers associated with the\n",
    "result collection. For basic collections, the conformer is the one that was used in any calculations; for optimization collections, it is the final conformer yielded by the optimization; and for torsion drives, it is the lowest energy conformer for each sampled torsion angle.\n",
    "\n",
    "In the case of torsion drive records, we can easily iterate over the grid ID, the associated conformer, and the\n",
    "associated energy in one go:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-165, -165] E=-1148.0092 Ha\n",
      "[-165, -150] E=-1148.0089 Ha\n",
      "[-165, -135] E=-1148.0090 Ha\n",
      "[-165, -120] E=-1148.0085 Ha\n",
      "[-165, -105] E=-1148.0077 Ha\n",
      "[-165, -90] E=-1148.0072 Ha\n",
      "[-165, -75] E=-1148.0075 Ha\n",
      "[-165, -60] E=-1148.0087 Ha\n",
      "[-165, -45] E=-1148.0091 Ha\n",
      "[-165, -30] E=-1148.0088 Ha\n",
      "[-165, -15] E=-1148.0103 Ha\n",
      "[-165, 0] E=-1148.0117 Ha\n",
      "[-165, 15] E=-1148.0125 Ha\n",
      "[-165, 30] E=-1148.0123 Ha\n",
      "[-165, 45] E=-1148.0113 Ha\n",
      "[-165, 60] E=-1148.0100 Ha\n",
      "[-165, 75] E=-1148.0091 Ha\n",
      "[-165, 90] E=-1148.0089 Ha\n",
      "[-165, 105] E=-1148.0092 Ha\n",
      "[-165, 120] E=-1148.0099 Ha\n",
      "[-165, 135] E=-1148.0106 Ha\n",
      "[-165, 150] E=-1148.0111 Ha\n",
      "[-165, 165] E=-1148.0109 Ha\n",
      "[-165, 180] E=-1148.0101 Ha\n",
      "[-150, -165] E=-1148.0087 Ha\n",
      "[-150, -150] E=-1148.0080 Ha\n",
      "[-150, -135] E=-1148.0081 Ha\n",
      "[-150, -120] E=-1148.0079 Ha\n",
      "[-150, -105] E=-1148.0072 Ha\n",
      "[-150, -90] E=-1148.0068 Ha\n",
      "[-150, -75] E=-1148.0073 Ha\n",
      "[-150, -60] E=-1148.0078 Ha\n",
      "[-150, -45] E=-1148.0081 Ha\n",
      "[-150, -30] E=-1148.0096 Ha\n",
      "[-150, -15] E=-1148.0115 Ha\n",
      "[-150, 0] E=-1148.0130 Ha\n",
      "[-150, 15] E=-1148.0136 Ha\n",
      "[-150, 30] E=-1148.0131 Ha\n",
      "[-150, 45] E=-1148.0120 Ha\n",
      "[-150, 60] E=-1148.0107 Ha\n",
      "[-150, 75] E=-1148.0101 Ha\n",
      "[-150, 90] E=-1148.0100 Ha\n",
      "[-150, 105] E=-1148.0101 Ha\n",
      "[-150, 120] E=-1148.0104 Ha\n",
      "[-150, 135] E=-1148.0108 Ha\n",
      "[-150, 150] E=-1148.0109 Ha\n",
      "[-150, 165] E=-1148.0106 Ha\n",
      "[-150, 180] E=-1148.0096 Ha\n",
      "[-135, -165] E=-1148.0077 Ha\n",
      "[-135, -150] E=-1148.0071 Ha\n",
      "[-135, -135] E=-1148.0070 Ha\n",
      "[-135, -120] E=-1148.0069 Ha\n",
      "[-135, -105] E=-1148.0066 Ha\n",
      "[-135, -90] E=-1148.0067 Ha\n",
      "[-135, -75] E=-1148.0070 Ha\n",
      "[-135, -60] E=-1148.0074 Ha\n",
      "[-135, -45] E=-1148.0084 Ha\n",
      "[-135, -30] E=-1148.0105 Ha\n",
      "[-135, -15] E=-1148.0125 Ha\n",
      "[-135, 0] E=-1148.0137 Ha\n",
      "[-135, 15] E=-1148.0139 Ha\n",
      "[-135, 30] E=-1148.0132 Ha\n",
      "[-135, 45] E=-1148.0120 Ha\n",
      "[-135, 60] E=-1148.0112 Ha\n",
      "[-135, 75] E=-1148.0109 Ha\n",
      "[-135, 90] E=-1148.0108 Ha\n",
      "[-135, 105] E=-1148.0105 Ha\n",
      "[-135, 120] E=-1148.0104 Ha\n",
      "[-135, 135] E=-1148.0105 Ha\n",
      "[-135, 150] E=-1148.0103 Ha\n",
      "[-135, 165] E=-1148.0097 Ha\n",
      "[-135, 180] E=-1148.0087 Ha\n",
      "[-120, -165] E=-1148.0070 Ha\n",
      "[-120, -150] E=-1148.0066 Ha\n",
      "[-120, -135] E=-1148.0064 Ha\n",
      "[-120, -120] E=-1148.0062 Ha\n",
      "[-120, -105] E=-1148.0062 Ha\n",
      "[-120, -90] E=-1148.0064 Ha\n",
      "[-120, -75] E=-1148.0068 Ha\n",
      "[-120, -60] E=-1148.0075 Ha\n",
      "[-120, -45] E=-1148.0093 Ha\n",
      "[-120, -30] E=-1148.0115 Ha\n",
      "[-120, -15] E=-1148.0133 Ha\n",
      "[-120, 0] E=-1148.0141 Ha\n",
      "[-120, 15] E=-1148.0139 Ha\n",
      "[-120, 30] E=-1148.0129 Ha\n",
      "[-120, 45] E=-1148.0122 Ha\n",
      "[-120, 60] E=-1148.0123 Ha\n",
      "[-120, 75] E=-1148.0124 Ha\n",
      "[-120, 90] E=-1148.0119 Ha\n",
      "[-120, 105] E=-1148.0112 Ha\n",
      "[-120, 120] E=-1148.0105 Ha\n",
      "[-120, 135] E=-1148.0102 Ha\n",
      "[-120, 150] E=-1148.0097 Ha\n",
      "[-120, 165] E=-1148.0089 Ha\n",
      "[-120, 180] E=-1148.0079 Ha\n",
      "[-105, -165] E=-1148.0067 Ha\n",
      "[-105, -150] E=-1148.0063 Ha\n",
      "[-105, -135] E=-1148.0059 Ha\n",
      "[-105, -120] E=-1148.0057 Ha\n",
      "[-105, -105] E=-1148.0056 Ha\n",
      "[-105, -90] E=-1148.0060 Ha\n",
      "[-105, -75] E=-1148.0069 Ha\n",
      "[-105, -60] E=-1148.0083 Ha\n",
      "[-105, -45] E=-1148.0104 Ha\n",
      "[-105, -30] E=-1148.0127 Ha\n",
      "[-105, -15] E=-1148.0141 Ha\n",
      "[-105, 0] E=-1148.0144 Ha\n",
      "[-105, 15] E=-1148.0136 Ha\n",
      "[-105, 30] E=-1148.0128 Ha\n",
      "[-105, 45] E=-1148.0135 Ha\n",
      "[-105, 60] E=-1148.0146 Ha\n",
      "[-105, 75] E=-1148.0145 Ha\n",
      "[-105, 90] E=-1148.0138 Ha\n",
      "[-105, 105] E=-1148.0124 Ha\n",
      "[-105, 120] E=-1148.0109 Ha\n",
      "[-105, 135] E=-1148.0100 Ha\n",
      "[-105, 150] E=-1148.0094 Ha\n",
      "[-105, 165] E=-1148.0085 Ha\n",
      "[-105, 180] E=-1148.0075 Ha\n",
      "[-90, -165] E=-1148.0064 Ha\n",
      "[-90, -150] E=-1148.0057 Ha\n",
      "[-90, -135] E=-1148.0050 Ha\n",
      "[-90, -120] E=-1148.0045 Ha\n",
      "[-90, -105] E=-1148.0047 Ha\n",
      "[-90, -90] E=-1148.0053 Ha\n",
      "[-90, -75] E=-1148.0067 Ha\n",
      "[-90, -60] E=-1148.0093 Ha\n",
      "[-90, -45] E=-1148.0128 Ha\n",
      "[-90, -30] E=-1148.0151 Ha\n",
      "[-90, -15] E=-1148.0156 Ha\n",
      "[-90, 0] E=-1148.0146 Ha\n",
      "[-90, 15] E=-1148.0131 Ha\n",
      "[-90, 30] E=-1148.0139 Ha\n",
      "[-90, 45] E=-1148.0157 Ha\n",
      "[-90, 60] E=-1148.0164 Ha\n",
      "[-90, 75] E=-1148.0160 Ha\n",
      "[-90, 90] E=-1148.0155 Ha\n",
      "[-90, 105] E=-1148.0137 Ha\n",
      "[-90, 120] E=-1148.0116 Ha\n",
      "[-90, 135] E=-1148.0102 Ha\n",
      "[-90, 150] E=-1148.0093 Ha\n",
      "[-90, 165] E=-1148.0085 Ha\n",
      "[-90, 180] E=-1148.0073 Ha\n",
      "[-75, -165] E=-1148.0058 Ha\n",
      "[-75, -150] E=-1148.0044 Ha\n",
      "[-75, -135] E=-1148.0032 Ha\n",
      "[-75, -120] E=-1148.0027 Ha\n",
      "[-75, -105] E=-1148.0026 Ha\n",
      "[-75, -90] E=-1148.0036 Ha\n",
      "[-75, -75] E=-1148.0064 Ha\n",
      "[-75, -60] E=-1148.0117 Ha\n",
      "[-75, -45] E=-1148.0156 Ha\n",
      "[-75, -30] E=-1148.0172 Ha\n",
      "[-75, -15] E=-1148.0163 Ha\n",
      "[-75, 0] E=-1148.0137 Ha\n",
      "[-75, 15] E=-1148.0125 Ha\n",
      "[-75, 30] E=-1148.0142 Ha\n",
      "[-75, 45] E=-1148.0153 Ha\n",
      "[-75, 60] E=-1148.0152 Ha\n",
      "[-75, 75] E=-1148.0154 Ha\n",
      "[-75, 90] E=-1148.0151 Ha\n",
      "[-75, 105] E=-1148.0136 Ha\n",
      "[-75, 120] E=-1148.0119 Ha\n",
      "[-75, 135] E=-1148.0105 Ha\n",
      "[-75, 150] E=-1148.0096 Ha\n",
      "[-75, 165] E=-1148.0085 Ha\n",
      "[-75, 180] E=-1148.0072 Ha\n",
      "[-60, -165] E=-1148.0035 Ha\n",
      "[-60, -150] E=-1148.0010 Ha\n",
      "[-60, -135] E=-1147.9993 Ha\n",
      "[-60, -120] E=-1147.9988 Ha\n",
      "[-60, -105] E=-1147.9991 Ha\n",
      "[-60, -90] E=-1148.0016 Ha\n",
      "[-60, -75] E=-1148.0079 Ha\n",
      "[-60, -60] E=-1148.0135 Ha\n",
      "[-60, -45] E=-1148.0166 Ha\n",
      "[-60, -30] E=-1148.0167 Ha\n",
      "[-60, -15] E=-1148.0140 Ha\n",
      "[-60, 0] E=-1148.0104 Ha\n",
      "[-60, 15] E=-1148.0112 Ha\n",
      "[-60, 30] E=-1148.0120 Ha\n",
      "[-60, 45] E=-1148.0122 Ha\n",
      "[-60, 60] E=-1148.0122 Ha\n",
      "[-60, 75] E=-1148.0125 Ha\n",
      "[-60, 90] E=-1148.0122 Ha\n",
      "[-60, 105] E=-1148.0116 Ha\n",
      "[-60, 120] E=-1148.0110 Ha\n",
      "[-60, 135] E=-1148.0104 Ha\n",
      "[-60, 150] E=-1148.0094 Ha\n",
      "[-60, 165] E=-1148.0080 Ha\n",
      "[-60, 180] E=-1148.0060 Ha\n",
      "[-45, -165] E=-1147.9979 Ha\n",
      "[-45, -150] E=-1147.9947 Ha\n",
      "[-45, -135] E=-1147.9933 Ha\n",
      "[-45, -120] E=-1147.9932 Ha\n",
      "[-45, -105] E=-1147.9967 Ha\n",
      "[-45, -90] E=-1148.0027 Ha\n",
      "[-45, -75] E=-1148.0089 Ha\n",
      "[-45, -60] E=-1148.0136 Ha\n",
      "[-45, -45] E=-1148.0149 Ha\n",
      "[-45, -30] E=-1148.0129 Ha\n",
      "[-45, -15] E=-1148.0087 Ha\n",
      "[-45, 0] E=-1148.0078 Ha\n",
      "[-45, 15] E=-1148.0084 Ha\n",
      "[-45, 30] E=-1148.0086 Ha\n",
      "[-45, 45] E=-1148.0084 Ha\n",
      "[-45, 60] E=-1148.0083 Ha\n",
      "[-45, 75] E=-1148.0081 Ha\n",
      "[-45, 90] E=-1148.0078 Ha\n",
      "[-45, 105] E=-1148.0082 Ha\n",
      "[-45, 120] E=-1148.0092 Ha\n",
      "[-45, 135] E=-1148.0092 Ha\n",
      "[-45, 150] E=-1148.0079 Ha\n",
      "[-45, 165] E=-1148.0056 Ha\n",
      "[-45, 180] E=-1148.0021 Ha\n",
      "[-30, -165] E=-1147.9898 Ha\n",
      "[-30, -150] E=-1147.9871 Ha\n",
      "[-30, -135] E=-1147.9880 Ha\n",
      "[-30, -120] E=-1147.9939 Ha\n",
      "[-30, -105] E=-1147.9990 Ha\n",
      "[-30, -90] E=-1148.0038 Ha\n",
      "[-30, -75] E=-1148.0090 Ha\n",
      "[-30, -60] E=-1148.0116 Ha\n",
      "[-30, -45] E=-1148.0106 Ha\n",
      "[-30, -30] E=-1148.0067 Ha\n",
      "[-30, -15] E=-1148.0050 Ha\n",
      "[-30, 0] E=-1148.0055 Ha\n",
      "[-30, 15] E=-1148.0057 Ha\n",
      "[-30, 30] E=-1148.0055 Ha\n",
      "[-30, 45] E=-1148.0049 Ha\n",
      "[-30, 60] E=-1148.0039 Ha\n",
      "[-30, 75] E=-1148.0029 Ha\n",
      "[-30, 90] E=-1148.0029 Ha\n",
      "[-30, 105] E=-1148.0055 Ha\n",
      "[-30, 120] E=-1148.0070 Ha\n",
      "[-30, 135] E=-1148.0064 Ha\n",
      "[-30, 150] E=-1148.0042 Ha\n",
      "[-30, 165] E=-1148.0004 Ha\n",
      "[-30, 180] E=-1147.9951 Ha\n",
      "[-15, -165] E=-1147.9817 Ha\n",
      "[-15, -150] E=-1147.9879 Ha\n",
      "[-15, -135] E=-1147.9941 Ha\n",
      "[-15, -120] E=-1147.9985 Ha\n",
      "[-15, -105] E=-1148.0020 Ha\n",
      "[-15, -90] E=-1148.0049 Ha\n",
      "[-15, -75] E=-1148.0076 Ha\n",
      "[-15, -60] E=-1148.0076 Ha\n",
      "[-15, -45] E=-1148.0044 Ha\n",
      "[-15, -30] E=-1148.0031 Ha\n",
      "[-15, -15] E=-1148.0038 Ha\n",
      "[-15, 0] E=-1148.0041 Ha\n",
      "[-15, 15] E=-1148.0039 Ha\n",
      "[-15, 30] E=-1148.0030 Ha\n",
      "[-15, 45] E=-1148.0015 Ha\n",
      "[-15, 60] E=-1147.9996 Ha\n",
      "[-15, 75] E=-1147.9997 Ha\n",
      "[-15, 90] E=-1148.0026 Ha\n",
      "[-15, 105] E=-1148.0040 Ha\n",
      "[-15, 120] E=-1148.0039 Ha\n",
      "[-15, 135] E=-1148.0018 Ha\n",
      "[-15, 150] E=-1147.9982 Ha\n",
      "[-15, 165] E=-1147.9931 Ha\n",
      "[-15, 180] E=-1147.9867 Ha\n",
      "[0, -165] E=-1147.9891 Ha\n",
      "[0, -150] E=-1147.9959 Ha\n",
      "[0, -135] E=-1148.0011 Ha\n",
      "[0, -120] E=-1148.0037 Ha\n",
      "[0, -105] E=-1148.0047 Ha\n",
      "[0, -90] E=-1148.0049 Ha\n",
      "[0, -75] E=-1148.0045 Ha\n",
      "[0, -60] E=-1148.0022 Ha\n",
      "[0, -45] E=-1148.0029 Ha\n",
      "[0, -30] E=-1148.0034 Ha\n",
      "[0, -15] E=-1148.0039 Ha\n",
      "[0, 0] E=-1148.0038 Ha\n",
      "[0, 15] E=-1148.0030 Ha\n",
      "[0, 30] E=-1148.0014 Ha\n",
      "[0, 45] E=-1147.9993 Ha\n",
      "[0, 60] E=-1148.0027 Ha\n",
      "[0, 75] E=-1148.0033 Ha\n",
      "[0, 90] E=-1148.0031 Ha\n",
      "[0, 105] E=-1148.0016 Ha\n",
      "[0, 120] E=-1147.9993 Ha\n",
      "[0, 135] E=-1147.9958 Ha\n",
      "[0, 150] E=-1147.9914 Ha\n",
      "[0, 165] E=-1147.9857 Ha\n",
      "[0, 180] E=-1147.9832 Ha\n",
      "[15, -165] E=-1147.9979 Ha\n",
      "[15, -150] E=-1148.0038 Ha\n",
      "[15, -135] E=-1148.0072 Ha\n",
      "[15, -120] E=-1148.0080 Ha\n",
      "[15, -105] E=-1148.0064 Ha\n",
      "[15, -90] E=-1148.0043 Ha\n",
      "[15, -75] E=-1148.0056 Ha\n",
      "[15, -60] E=-1148.0080 Ha\n",
      "[15, -45] E=-1148.0078 Ha\n",
      "[15, -30] E=-1148.0066 Ha\n",
      "[15, -15] E=-1148.0055 Ha\n",
      "[15, 0] E=-1148.0045 Ha\n",
      "[15, 15] E=-1148.0031 Ha\n",
      "[15, 30] E=-1148.0026 Ha\n",
      "[15, 45] E=-1148.0053 Ha\n",
      "[15, 60] E=-1148.0065 Ha\n",
      "[15, 75] E=-1148.0056 Ha\n",
      "[15, 90] E=-1148.0020 Ha\n",
      "[15, 105] E=-1147.9979 Ha\n",
      "[15, 120] E=-1147.9940 Ha\n",
      "[15, 135] E=-1147.9900 Ha\n",
      "[15, 150] E=-1147.9856 Ha\n",
      "[15, 165] E=-1147.9878 Ha\n",
      "[15, 180] E=-1147.9917 Ha\n",
      "[30, -165] E=-1148.0061 Ha\n",
      "[30, -150] E=-1148.0101 Ha\n",
      "[30, -135] E=-1148.0118 Ha\n",
      "[30, -120] E=-1148.0112 Ha\n",
      "[30, -105] E=-1148.0094 Ha\n",
      "[30, -90] E=-1148.0102 Ha\n",
      "[30, -75] E=-1148.0131 Ha\n",
      "[30, -60] E=-1148.0137 Ha\n",
      "[30, -45] E=-1148.0127 Ha\n",
      "[30, -30] E=-1148.0113 Ha\n",
      "[30, -15] E=-1148.0099 Ha\n",
      "[30, 0] E=-1148.0083 Ha\n",
      "[30, 15] E=-1148.0076 Ha\n",
      "[30, 30] E=-1148.0094 Ha\n",
      "[30, 45] E=-1148.0103 Ha\n",
      "[30, 60] E=-1148.0089 Ha\n",
      "[30, 75] E=-1148.0059 Ha\n",
      "[30, 90] E=-1148.0007 Ha\n",
      "[30, 105] E=-1147.9960 Ha\n",
      "[30, 120] E=-1147.9926 Ha\n",
      "[30, 135] E=-1147.9921 Ha\n",
      "[30, 150] E=-1147.9941 Ha\n",
      "[30, 165] E=-1147.9967 Ha\n",
      "[30, 180] E=-1148.0008 Ha\n",
      "[45, -165] E=-1148.0114 Ha\n",
      "[45, -150] E=-1148.0136 Ha\n",
      "[45, -135] E=-1148.0140 Ha\n",
      "[45, -120] E=-1148.0133 Ha\n",
      "[45, -105] E=-1148.0128 Ha\n",
      "[45, -90] E=-1148.0167 Ha\n",
      "[45, -75] E=-1148.0185 Ha\n",
      "[45, -60] E=-1148.0184 Ha\n",
      "[45, -45] E=-1148.0174 Ha\n",
      "[45, -30] E=-1148.0157 Ha\n",
      "[45, -15] E=-1148.0139 Ha\n",
      "[45, 0] E=-1148.0125 Ha\n",
      "[45, 15] E=-1148.0138 Ha\n",
      "[45, 30] E=-1148.0136 Ha\n",
      "[45, 45] E=-1148.0128 Ha\n",
      "[45, 60] E=-1148.0096 Ha\n",
      "[45, 75] E=-1148.0054 Ha\n",
      "[45, 90] E=-1148.0015 Ha\n",
      "[45, 105] E=-1147.9989 Ha\n",
      "[45, 120] E=-1147.9983 Ha\n",
      "[45, 135] E=-1148.0000 Ha\n",
      "[45, 150] E=-1148.0020 Ha\n",
      "[45, 165] E=-1148.0046 Ha\n",
      "[45, 180] E=-1148.0079 Ha\n",
      "[60, -165] E=-1148.0124 Ha\n",
      "[60, -150] E=-1148.0132 Ha\n",
      "[60, -135] E=-1148.0135 Ha\n",
      "[60, -120] E=-1148.0129 Ha\n",
      "[60, -105] E=-1148.0157 Ha\n",
      "[60, -90] E=-1148.0193 Ha\n",
      "[60, -75] E=-1148.0211 Ha\n",
      "[60, -60] E=-1148.0214 Ha\n",
      "[60, -45] E=-1148.0204 Ha\n",
      "[60, -30] E=-1148.0185 Ha\n",
      "[60, -15] E=-1148.0164 Ha\n",
      "[60, 0] E=-1148.0165 Ha\n",
      "[60, 15] E=-1148.0162 Ha\n",
      "[60, 30] E=-1148.0151 Ha\n",
      "[60, 45] E=-1148.0125 Ha\n",
      "[60, 60] E=-1148.0086 Ha\n",
      "[60, 75] E=-1148.0050 Ha\n",
      "[60, 90] E=-1148.0032 Ha\n",
      "[60, 105] E=-1148.0026 Ha\n",
      "[60, 120] E=-1148.0041 Ha\n",
      "[60, 135] E=-1148.0054 Ha\n",
      "[60, 150] E=-1148.0068 Ha\n",
      "[60, 165] E=-1148.0087 Ha\n",
      "[60, 180] E=-1148.0105 Ha\n",
      "[75, -165] E=-1148.0098 Ha\n",
      "[75, -150] E=-1148.0100 Ha\n",
      "[75, -135] E=-1148.0103 Ha\n",
      "[75, -120] E=-1148.0102 Ha\n",
      "[75, -105] E=-1148.0137 Ha\n",
      "[75, -90] E=-1148.0177 Ha\n",
      "[75, -75] E=-1148.0205 Ha\n",
      "[75, -60] E=-1148.0219 Ha\n",
      "[75, -45] E=-1148.0209 Ha\n",
      "[75, -30] E=-1148.0184 Ha\n",
      "[75, -15] E=-1148.0170 Ha\n",
      "[75, 0] E=-1148.0167 Ha\n",
      "[75, 15] E=-1148.0154 Ha\n",
      "[75, 30] E=-1148.0136 Ha\n",
      "[75, 45] E=-1148.0103 Ha\n",
      "[75, 60] E=-1148.0065 Ha\n",
      "[75, 75] E=-1148.0045 Ha\n",
      "[75, 90] E=-1148.0042 Ha\n",
      "[75, 105] E=-1148.0054 Ha\n",
      "[75, 120] E=-1148.0066 Ha\n",
      "[75, 135] E=-1148.0071 Ha\n",
      "[75, 150] E=-1148.0080 Ha\n",
      "[75, 165] E=-1148.0087 Ha\n",
      "[75, 180] E=-1148.0093 Ha\n",
      "[90, -165] E=-1148.0063 Ha\n",
      "[90, -150] E=-1148.0061 Ha\n",
      "[90, -135] E=-1148.0064 Ha\n",
      "[90, -120] E=-1148.0071 Ha\n",
      "[90, -105] E=-1148.0106 Ha\n",
      "[90, -90] E=-1148.0148 Ha\n",
      "[90, -75] E=-1148.0185 Ha\n",
      "[90, -60] E=-1148.0198 Ha\n",
      "[90, -45] E=-1148.0182 Ha\n",
      "[90, -30] E=-1148.0159 Ha\n",
      "[90, -15] E=-1148.0155 Ha\n",
      "[90, 0] E=-1148.0143 Ha\n",
      "[90, 15] E=-1148.0132 Ha\n",
      "[90, 30] E=-1148.0109 Ha\n",
      "[90, 45] E=-1148.0075 Ha\n",
      "[90, 60] E=-1148.0041 Ha\n",
      "[90, 75] E=-1148.0038 Ha\n",
      "[90, 90] E=-1148.0043 Ha\n",
      "[90, 105] E=-1148.0063 Ha\n",
      "[90, 120] E=-1148.0065 Ha\n",
      "[90, 135] E=-1148.0066 Ha\n",
      "[90, 150] E=-1148.0068 Ha\n",
      "[90, 165] E=-1148.0067 Ha\n",
      "[90, 180] E=-1148.0065 Ha\n",
      "[105, -165] E=-1148.0032 Ha\n",
      "[105, -150] E=-1148.0038 Ha\n",
      "[105, -135] E=-1148.0047 Ha\n",
      "[105, -120] E=-1148.0067 Ha\n",
      "[105, -105] E=-1148.0091 Ha\n",
      "[105, -90] E=-1148.0120 Ha\n",
      "[105, -75] E=-1148.0146 Ha\n",
      "[105, -60] E=-1148.0156 Ha\n",
      "[105, -45] E=-1148.0145 Ha\n",
      "[105, -30] E=-1148.0136 Ha\n",
      "[105, -15] E=-1148.0129 Ha\n",
      "[105, 0] E=-1148.0122 Ha\n",
      "[105, 15] E=-1148.0106 Ha\n",
      "[105, 30] E=-1148.0080 Ha\n",
      "[105, 45] E=-1148.0045 Ha\n",
      "[105, 60] E=-1148.0020 Ha\n",
      "[105, 75] E=-1148.0026 Ha\n",
      "[105, 90] E=-1148.0047 Ha\n",
      "[105, 105] E=-1148.0056 Ha\n",
      "[105, 120] E=-1148.0052 Ha\n",
      "[105, 135] E=-1148.0052 Ha\n",
      "[105, 150] E=-1148.0049 Ha\n",
      "[105, 165] E=-1148.0043 Ha\n",
      "[105, 180] E=-1148.0035 Ha\n",
      "[120, -165] E=-1148.0027 Ha\n",
      "[120, -150] E=-1148.0042 Ha\n",
      "[120, -135] E=-1148.0050 Ha\n",
      "[120, -120] E=-1148.0068 Ha\n",
      "[120, -105] E=-1148.0085 Ha\n",
      "[120, -90] E=-1148.0102 Ha\n",
      "[120, -75] E=-1148.0119 Ha\n",
      "[120, -60] E=-1148.0126 Ha\n",
      "[120, -45] E=-1148.0123 Ha\n",
      "[120, -30] E=-1148.0115 Ha\n",
      "[120, -15] E=-1148.0115 Ha\n",
      "[120, 0] E=-1148.0104 Ha\n",
      "[120, 15] E=-1148.0082 Ha\n",
      "[120, 30] E=-1148.0053 Ha\n",
      "[120, 45] E=-1148.0017 Ha\n",
      "[120, 60] E=-1148.0006 Ha\n",
      "[120, 75] E=-1148.0026 Ha\n",
      "[120, 90] E=-1148.0044 Ha\n",
      "[120, 105] E=-1148.0044 Ha\n",
      "[120, 120] E=-1148.0038 Ha\n",
      "[120, 135] E=-1148.0038 Ha\n",
      "[120, 150] E=-1148.0033 Ha\n",
      "[120, 165] E=-1148.0022 Ha\n",
      "[120, 180] E=-1148.0011 Ha\n",
      "[135, -165] E=-1148.0042 Ha\n",
      "[135, -150] E=-1148.0052 Ha\n",
      "[135, -135] E=-1148.0061 Ha\n",
      "[135, -120] E=-1148.0075 Ha\n",
      "[135, -105] E=-1148.0086 Ha\n",
      "[135, -90] E=-1148.0095 Ha\n",
      "[135, -75] E=-1148.0104 Ha\n",
      "[135, -60] E=-1148.0111 Ha\n",
      "[135, -45] E=-1148.0109 Ha\n",
      "[135, -30] E=-1148.0110 Ha\n",
      "[135, -15] E=-1148.0106 Ha\n",
      "[135, 0] E=-1148.0090 Ha\n",
      "[135, 15] E=-1148.0062 Ha\n",
      "[135, 30] E=-1148.0030 Ha\n",
      "[135, 45] E=-1147.9996 Ha\n",
      "[135, 60] E=-1148.0011 Ha\n",
      "[135, 75] E=-1148.0032 Ha\n",
      "[135, 90] E=-1148.0038 Ha\n",
      "[135, 105] E=-1148.0032 Ha\n",
      "[135, 120] E=-1148.0030 Ha\n",
      "[135, 135] E=-1148.0030 Ha\n",
      "[135, 150] E=-1148.0027 Ha\n",
      "[135, 165] E=-1148.0015 Ha\n",
      "[135, 180] E=-1148.0028 Ha\n",
      "[150, -165] E=-1148.0062 Ha\n",
      "[150, -150] E=-1148.0067 Ha\n",
      "[150, -135] E=-1148.0074 Ha\n",
      "[150, -120] E=-1148.0083 Ha\n",
      "[150, -105] E=-1148.0089 Ha\n",
      "[150, -90] E=-1148.0094 Ha\n",
      "[150, -75] E=-1148.0099 Ha\n",
      "[150, -60] E=-1148.0103 Ha\n",
      "[150, -45] E=-1148.0107 Ha\n",
      "[150, -30] E=-1148.0112 Ha\n",
      "[150, -15] E=-1148.0103 Ha\n",
      "[150, 0] E=-1148.0081 Ha\n",
      "[150, 15] E=-1148.0050 Ha\n",
      "[150, 30] E=-1148.0016 Ha\n",
      "[150, 45] E=-1148.0027 Ha\n",
      "[150, 60] E=-1148.0037 Ha\n",
      "[150, 75] E=-1148.0039 Ha\n",
      "[150, 90] E=-1148.0034 Ha\n",
      "[150, 105] E=-1148.0030 Ha\n",
      "[150, 120] E=-1148.0032 Ha\n",
      "[150, 135] E=-1148.0035 Ha\n",
      "[150, 150] E=-1148.0042 Ha\n",
      "[150, 165] E=-1148.0049 Ha\n",
      "[150, 180] E=-1148.0053 Ha\n",
      "[165, -165] E=-1148.0080 Ha\n",
      "[165, -150] E=-1148.0083 Ha\n",
      "[165, -135] E=-1148.0082 Ha\n",
      "[165, -120] E=-1148.0087 Ha\n",
      "[165, -105] E=-1148.0090 Ha\n",
      "[165, -90] E=-1148.0092 Ha\n",
      "[165, -75] E=-1148.0095 Ha\n",
      "[165, -60] E=-1148.0097 Ha\n",
      "[165, -45] E=-1148.0112 Ha\n",
      "[165, -30] E=-1148.0111 Ha\n",
      "[165, -15] E=-1148.0099 Ha\n",
      "[165, 0] E=-1148.0074 Ha\n",
      "[165, 15] E=-1148.0058 Ha\n",
      "[165, 30] E=-1148.0066 Ha\n",
      "[165, 45] E=-1148.0066 Ha\n",
      "[165, 60] E=-1148.0061 Ha\n",
      "[165, 75] E=-1148.0052 Ha\n",
      "[165, 90] E=-1148.0044 Ha\n",
      "[165, 105] E=-1148.0045 Ha\n",
      "[165, 120] E=-1148.0051 Ha\n",
      "[165, 135] E=-1148.0061 Ha\n",
      "[165, 150] E=-1148.0075 Ha\n",
      "[165, 165] E=-1148.0079 Ha\n",
      "[165, 180] E=-1148.0075 Ha\n",
      "[180, -165] E=-1148.0087 Ha\n",
      "[180, -150] E=-1148.0091 Ha\n",
      "[180, -135] E=-1148.0090 Ha\n",
      "[180, -120] E=-1148.0084 Ha\n",
      "[180, -105] E=-1148.0084 Ha\n",
      "[180, -90] E=-1148.0086 Ha\n",
      "[180, -75] E=-1148.0088 Ha\n",
      "[180, -60] E=-1148.0097 Ha\n",
      "[180, -45] E=-1148.0105 Ha\n",
      "[180, -30] E=-1148.0102 Ha\n",
      "[180, -15] E=-1148.0089 Ha\n",
      "[180, 0] E=-1148.0093 Ha\n",
      "[180, 15] E=-1148.0103 Ha\n",
      "[180, 30] E=-1148.0103 Ha\n",
      "[180, 45] E=-1148.0095 Ha\n",
      "[180, 60] E=-1148.0083 Ha\n",
      "[180, 75] E=-1148.0072 Ha\n",
      "[180, 90] E=-1148.0069 Ha\n",
      "[180, 105] E=-1148.0073 Ha\n",
      "[180, 120] E=-1148.0081 Ha\n",
      "[180, 135] E=-1148.0091 Ha\n",
      "[180, 150] E=-1148.0100 Ha\n",
      "[180, 165] E=-1148.0101 Ha\n",
      "[180, 180] E=-1148.0094 Ha\n"
     ]
    }
   ],
   "source": [
    "torsion_drive_record, molecule = torsion_drive_records[0]\n",
    "import numpy as np\n",
    "from matplotlib import pyplot\n",
    "from openff.units import unit\n",
    "\n",
    "energy_grid = np.zeros((24, 24))\n",
    "for grid_id, qc_conformer in zip(molecule.properties[\"grid_ids\"], molecule.conformers):\n",
    "    qc_energy = torsion_drive_record.final_energy_dict[grid_id]\n",
    "\n",
    "    grid_tuple = [int(i) for i in grid_id.strip(\"[]\").split(\",\")]\n",
    "    phi_bin = (grid_tuple[0] + 165) / 15\n",
    "    psi_bin = (grid_tuple[1] + 165) / 15\n",
    "    energy_grid[int(psi_bin), int(phi_bin)] = qc_energy\n",
    "    print(f\"{grid_id} E={qc_energy:.4f} Ha\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'psi')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy_grid -= energy_grid[0, 0]\n",
    "pyplot.imshow(energy_grid, origin=\"lower\")\n",
    "pyplot.title(\"Val-Ala-Val - central backbone torsiondrive (Hartree)\")\n",
    "pyplot.colorbar()\n",
    "pyplot.yticks([0, 5, 11, 17, 23], [-165, -90, 0, 90, 165])\n",
    "pyplot.xticks([0, 5, 11, 17, 23], [-165, -90, 0, 90, 165])\n",
    "pyplot.ylabel(\"phi\")\n",
    "pyplot.xlabel(\"psi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c5ae9c0db264e2bb8a96d1458ffd10c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0cdf063dd1445669fd7a30214652783",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget(max_frame=575)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "molecule.visualize(\"nglview\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": 1
}