{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2b61f5b3",
   "metadata": {},
   "source": [
    "# Collision Cross Section\n",
    "\n",
    "Collision cross sections (CCS) are an important parameter that characterizes the ion mobility behavior of molecules in the gas phase. \n",
    "CCS values can provide complementary information to the mass-to-charge ratio (m/z) for molecule identification. \n",
    "To predict CCS values, we require a geometry and ion type as input. \n",
    "ISiCLE takes these input and can sequentially generate adducts, conformers, and DFT optimized geometries to obtain Boltzmann-weighted CCS values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5356df47",
   "metadata": {},
   "source": [
    "### Loading a molecule\n",
    "We can load directly from SMILES or InChI strings, MOL, XYZ, and PDB formats. For simplicity, we'll load Butanol from a SMILES string. To view the loaded molecule, simply call its `view` method (note that `view` only works within a notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f4a0528",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2ae1c8122ea0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import isicle\n",
    "\n",
    "geom = isicle.load(\"C1=CC=C2C(=C1)C=CC3=C2C=CC=C3O\")\n",
    "geom.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df54f691-1ad8-4d6b-8216-71dcf60b4a7a",
   "metadata": {},
   "source": [
    "We can additionally perform an initial forcefield optimization to produce 3D, rather than 2D, coordinates for the molecule.\n",
    "Keyword arguments include whether to `embed` the molecule (only specify `False` if the molecule has already been embedded), the `forcefield` used to perform the optimization (here, `UFF`), and the number of forcefield iterations, `ff_iter`, here 200.\n",
    "Because our selected molecule is planar, differences will be minimal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc66bf13-ee8b-4132-87ed-bcebce56a3d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2ae1c8122f80>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geom = geom.initial_optimize(embed=True, forcefield=\"UFF\", ff_iter=200)\n",
    "geom.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5a531a3-4b58-444a-9e6c-a7fbfc9a13cd",
   "metadata": {},
   "source": [
    "### Adduct generation\n",
    "To generate adducts, the ionic form of the molecule observed under an ion mobility experiment, we ionize the molecule with H+ using the `explicit` method to explicitly ionize indicated sites with indicated ions. \n",
    "We specify the ion(s) in the `ion_list` and the atom types to be ionized in `element_list`.\n",
    "Multiple ionizations by monoatomic ions is possible by repeating the ionize function on a given molecule. At this time, polyatomic ions are not supported. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3082ba56-5e53-470a-b37c-5b8d211769c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H-': {0: <isicle.geometry.Geometry object at 0x2ae1c7eb4950>}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ionize oxygen\n",
    "adducts = geom.ionize(method='explicit', ion_list=['H-'], element_list=['O'])\n",
    "adducts.get_adducts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c1adbac-e1a5-4855-9aad-2ad4f8cae9c8",
   "metadata": {},
   "source": [
    "Note that `get_adducts` returns a nested dictionary keyed by ion type (here `H-`) and unique index to distinguish multiple ion sites (here, `0`).\n",
    "Alternatively, `get_structures` returns a flattened list of all adduct structures in the object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12fcb587-70d2-4b9e-a21e-33c31010f1b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.RWMol at 0x2ae1c8123e60>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adduct = adducts.get_adducts()[\"H-\"][0]\n",
    "\n",
    "# Alternatively\n",
    "adduct = adducts.get_structures()[0]\n",
    "\n",
    "adduct.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d44afc-bf60-4fd4-8645-11b084169116",
   "metadata": {},
   "source": [
    "### Semiemprical optimization\n",
    "While we must obtain 3D coordinates for subsequent steps, use of a classic forcefield is not the most rigorous way to do so.\n",
    "Instead, we can leverage semiemprical approaches developed within xTB (extended tight binding), specifically the GFN2-xTB forcefield.\n",
    "In this case, the result of classic forcefield and semiempircal methods are quite similar, but expect these to diverge with more complex structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0ffeeb1-6df2-4529-b938-d8497890f527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['out', 'charges', 'wbo', 'log', 'xtbrestart', 'mol', 'final'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md = isicle.md.md(adduct, task='optimize', forcefield='gfn2', optlevel='vtight')\n",
    "md.result.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb8c2a26-4857-4f0a-b00c-99883a150ec9",
   "metadata": {},
   "source": [
    "ISiCLE runs the relevant xTB code in a temporary directory, returning to the user a dictionary with all raw outputs stored. \n",
    "To parse this information into more a more readily usable form, we can initialize and run the associated file parser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "27bf0c0c-9243-49ad-8a17-5bed6089ad2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['protocol', 'geometry', 'timing', 'energy'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md_parsed = md.parse()\n",
    "md_parsed.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dfe155d-8e12-4df8-a8c0-27f30266d4be",
   "metadata": {},
   "source": [
    "We can now view the xTB protocol, timing information, energy, and the optimized geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f2d4664-c846-44fa-8d8e-54b3deb8da0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "protocol:   xtb GTBXZWADMKOZQJ-UHFFFAOYSA-N.xyz --opt vtight --gfn2 --chrg -1\n",
      "timing:  {'Total wall time': '0d,0h,0min,0.206sec', 'SCF wall time': '0d,0h,0min,0.026sec', 'ANC optimizer wall time': '0d,0h,0min,0.206sec'}\n",
      "energy:  {'Total energy': '-38.854921356681 Hartrees'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.RWMol at 0x2ae1c8123990>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('protocol: ', md_parsed['protocol'])\n",
    "print('timing: ', md_parsed['timing'])\n",
    "print('energy: ', md_parsed['energy'])\n",
    "md_parsed['geometry'].view()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9007fce-c9b7-41a6-9d50-70b62c21290a",
   "metadata": {},
   "source": [
    "Finally, we'll reassign our `adduct` variable with the MD-optimized adduct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d3909e3-ad49-4dde-a40c-c19b3b4e7879",
   "metadata": {},
   "outputs": [],
   "source": [
    "adduct = md_parsed['geometry']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4df90b",
   "metadata": {},
   "source": [
    "### Conformational Ensembles\n",
    "We use xTB's Conformer Rotamer Ensemble Sampling Tool, CREST, to predict conformational ensembles.\n",
    "CREST is already a simple-to-use, standalone commandline tool, but we have created a Python interface to call upon some tools CREST has made available, as well as functionality to obtain relevant outputs.\n",
    "For example, we can generate conformers using a 3 kcal/mol energy window using the GFN2 forcefield."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fd16accd",
   "metadata": {},
   "outputs": [],
   "source": [
    "crest = isicle.md.md(\n",
    "    adduct, \n",
    "    task='conformer',\n",
    "    forcefield='gfn2',\n",
    "    optlevel='normal',\n",
    "    ewin=3,\n",
    "    processes=8\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9a054b7-a253-44bf-8b37-f0dd480a1d22",
   "metadata": {},
   "source": [
    "Raw output returned as a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b83e656f-8548-48bd-a07c-59ca48ea8816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['best', 'conformers', 'rotamers', 'out', 'coord', 'cre_members', 'energies', 'restart', 'mdrestart', 'trj', 'log', 'gfnff_adjacency', 'gfnff_topo', 'wbo'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crest.result.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae2bcf08-b772-4c68-bfc2-650353ba30fc",
   "metadata": {},
   "source": [
    "Parse the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ffb45f5-b624-4bd1-ac40-7a112036f257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['protocol', 'geometry', 'timing', 'energy'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crest_parsed = crest.parse()\n",
    "crest_parsed.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3ea2c79c-716a-4f3f-8664-3c6dc42b9980",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "protocol:   $ crest /scratch/colb804/isicle/tmp50vh4igd/GTBXZWADMKOZQJ-UHFFFAOYSA-N.xyz -chrg -1 --ewin 3 --optlevel normal -gfn2 -T 8\n",
      "timing:  {'CREST runtime (total)': {'days': 0, 'hours': 0, 'minutes': 3, 'seconds': 6.93}, 'Trial metadynamics (MTD)': {'days': 0, 'hours': 0, 'minutes': 0, 'seconds': 3.649}, 'Metadynamics (MTD)': {'days': 0, 'hours': 0, 'minutes': 1, 'seconds': 18.813}, 'Geometry optimization': {'days': 0, 'hours': 0, 'minutes': 0, 'seconds': 46.338}, 'Molecular dynamics (MD)': {'days': 0, 'hours': 0, 'minutes': 0, 'seconds': 57.897}, 'Genetic crossing (GC)': {'days': 0, 'hours': 0, 'minutes': 0, 'seconds': 0.0}, 'I/O and setup': {'days': 0, 'hours': 0, 'minutes': 0, 'seconds': 0.232}}\n",
      "energy:  {'relative energies': [], 'total energies': [], 'population': []}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.RWMol at 0x2ae1c8123ae0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('protocol: ', crest_parsed['protocol'])\n",
    "print('timing: ', crest_parsed['timing'])\n",
    "print('energy: ', crest_parsed['energy'])\n",
    "crest_parsed['geometry']['best'].view()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f32db050-e3f7-4132-a8be-45ea013f12d1",
   "metadata": {},
   "source": [
    "Finally, we'll grab all generated conformers for the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "63b043ba-8377-4e41-becf-3c8e6fc53a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<isicle.geometry.Geometry at 0x2ae1c7255700>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conformers = crest_parsed['geometry']['conformers']\n",
    "conformers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea7af42",
   "metadata": {},
   "source": [
    "### Density Functional Theory Calculations\n",
    "\n",
    "We now calculate DFT single point energies for each conformer using open-source quantum chemical software, ORCA.\n",
    "Specifications are customizable to any functional or basis set available, but defaults are set to B3LYP and 6-31G(d), respectively.\n",
    "\n",
    "Here we calculate the geometry optimizations and single point energies using the hybrid functional, B3LYP, and double zeta basis set, def2-SVP, along with some other optimizations.\n",
    "We can either loop through each CREST conformer, invoking `isicle.qm.dft`, or make use of `ConformationalEnsemble` to map over conformers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fcc20316-3a8c-45ee-a3d3-ac0ab8a4b17a",
   "metadata": {},
   "outputs": [],
   "source": [
    "conformers = isicle.conformers.ConformationalEnsemble([conformers])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0378edfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "dft = conformers.apply(\n",
    "    func=isicle.qm.dft,\n",
    "    backend='orca',\n",
    "    simple_input=[\n",
    "        'b3lyp',      # Functional\n",
    "        'def2-svp',   # Basis set\n",
    "        'def2/j',     # Auxillary basis set\n",
    "        'opt',        # Perform geometry optimization\n",
    "        'd3bj',       # Dispersion correction\n",
    "    ],\n",
    "    spin_multiplicity=1,\n",
    "    processes=8\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fccab1b8-ab9e-473f-8bb4-7143e1f4380e",
   "metadata": {},
   "source": [
    "Raw outputs include the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e3d3ec26-f650-4783-843f-1924741d17c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['xyz', 'inp', 'out', 'log', 'txt', 'gbw', 'opt', 'bibtex', 'densities', 'densitiesinfo', 'trj', 'engrad'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft[0].result.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd48262c-da91-4023-80cb-bf89797a7f07",
   "metadata": {},
   "source": [
    "Parse the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2f134559-b434-498f-a303-6fd0de617564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['protocol', 'geometry', 'energy', 'orbital_energies', 'charge', 'timing', 'success'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft_parsed = [x.parse() for x in dft]\n",
    "dft_parsed[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2d318ed5-d744-4fa9-b48b-912581bc26e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "protocol:  ! b3lyp def2-svp def2/j opt d3bj\n",
      "%PAL NPROCS 8 END\n",
      "* xyzfile -1 1 /scratch/colb804/isicle/tmp7t6d6p2s/GTBXZWADMKOZQJ-UHFFFAOYSA-N.xyz\n",
      "\n",
      "timing:  {'Sum of individual times': 131.993, 'Startup calculation': 10.562, 'SCF iterations': 71.833, 'Property calculations': 0.569, 'SCF Gradient evaluation': 48.99, 'Geometry relaxation': 0.039, 'Total run time': {'days': 0, 'hours': 0, 'minutes': 2, 'seconds': 14, 'msec': 710}}\n",
      "energy:  -613.443313909566\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.RWMol at 0x2ae1ac55e500>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('protocol: ', dft_parsed[0]['protocol'])\n",
    "print('timing: ', dft_parsed[0]['timing'])\n",
    "print('energy: ', dft_parsed[0]['energy'])\n",
    "dft_parsed[0]['geometry'].view()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b723926-68e9-49ec-b177-3e0c2215ff43",
   "metadata": {},
   "source": [
    "Grab all of the geometries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b4299aff-4785-406d-91fd-377a63cd0dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "conformers = isicle.conformers.ConformationalEnsemble([x[\"geometry\"] for x in dft_parsed])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "644e69d1",
   "metadata": {},
   "source": [
    "### Collision Cross Section Calculations\n",
    "\n",
    "Next, we calculate the collision cross section for our optimized geometry produced from the DFT step using using a refactored version of MOBCAL for trajectory-based mobility calculations.\n",
    "This version is included in the with installation of ISiCLE and improves upon the original verion's computational efficiency by two orders of magnitude. (Note: currently only supported on Linux).\n",
    "We conduct the calculation in nitrogen gas at room temperature using the Lennard-Jones potential."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "14a8e6ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "mobility = conformers.apply(\n",
    "    isicle.mobility.calculate_ccs,\n",
    "    lennard_jones='default',\n",
    "    i2=5013489,\n",
    "    buffer_gas='nitrogen',\n",
    "    buffer_gas_mass=28.014,\n",
    "    temp=300,\n",
    "    ipr=1000,\n",
    "    itn=10,\n",
    "    inp=48,\n",
    "    imp=1024,\n",
    "    processes=8,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1870b921-887f-4263-a29b-3d1a65845297",
   "metadata": {},
   "source": [
    "Raw outputs include the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d602086a-354a-41be-96f6-d764cc0dd4e0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['mfj', 'in', 'params', 'log', 'out', 'geometry'])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility[0].result.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b64457a2-bee0-46a1-845c-e2a4084656ba",
   "metadata": {},
   "source": [
    "Parse the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2612be08-1bd8-4b19-af42-fc48d07b69b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['ccs', 'geometry'])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobility_parsed = [x.parse() for x in mobility]\n",
    "mobility_parsed[0].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f622be-834d-47e1-a763-10eb7da3850d",
   "metadata": {},
   "source": [
    "Grab all of the geometries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8c0c96da-57d3-40b5-9638-7c47deecd2bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "conformers = isicle.conformers.ConformationalEnsemble([x[\"geometry\"] for x in mobility_parsed])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64e0bd0d",
   "metadata": {},
   "source": [
    "### Boltzmann Averaging CCS Values\n",
    "To obtain our Boltzmann weighted CCS values, we Boltzmann average across the different conformers by their DFT energies. This is accomplished using the `conformers` module. Because we only have a single conformer in this example, standard deviation is zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1f38463c-589b-4780-a924-a54d490b1cbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean    137.3965\n",
       "std       0.0000\n",
       "n         1.0000\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccs = conformers.reduce(\"ccs\", func='boltzmann')\n",
    "ccs"
   ]
  }
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