{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ac8e72a",
   "metadata": {},
   "source": [
    "## Initial imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a70d016",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scm.plams import *\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import List"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d8fccf2",
   "metadata": {},
   "source": [
    "## Function definitions\n",
    "\n",
    "The ``addition()`` function \n",
    "\n",
    "* performs a preliminary MD simulation with UFF to arrange the two reactant molecules at an approximate transition state,\n",
    "\n",
    "* then does a transition state search with DFTB, specifying the known reaction coordinate (bond formation),\n",
    "\n",
    "* then uses the PESExploration LandscapeRefinement tool to get the two corresponding minima and energy landscape. Alternatively, one could also do an IRC (intrinsic reaction coordinate) calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c91b17ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def addition(\n",
    "    mol1: Molecule,\n",
    "    mol2: Molecule,\n",
    "    covalent_radius_multiplier: float = 1.2,\n",
    "):\n",
    "    mol1_ind = get_active_i(mol1)\n",
    "    mol2_ind = get_active_i(mol2)\n",
    "    if len(mol1_ind) != 2:\n",
    "        raise ValueError(\n",
    "            f\"Set at.properties.active_bond for two atoms in mol1. Current mol1_ind: {mol1_ind}\"\n",
    "        )\n",
    "    if len(mol2_ind) != 2:\n",
    "        raise ValueError(\n",
    "            f\"Set at.properties.active_bond for two atoms in mol2. Current mol2_ind: {mol2_ind}\"\n",
    "        )\n",
    "\n",
    "    target_distances = []\n",
    "\n",
    "    for i1, i2 in zip(mol1_ind, mol2_ind):\n",
    "        r = get_atom_radius(mol1[i1]) + get_atom_radius(mol2[i2])\n",
    "        target_distances.append(r * covalent_radius_multiplier)\n",
    "\n",
    "    combined = mol1.copy()\n",
    "    combined.add_molecule(mol2, margin=2)\n",
    "    combined.properties.charge = mol1.properties.get(\"charge\", 0) + mol2.properties.get(\n",
    "        \"charge\", 0\n",
    "    )\n",
    "\n",
    "    mol2_ind = [x + len(mol1) for x in mol2_ind]\n",
    "\n",
    "    preliminary_md_results = preliminary_md(\n",
    "        combined,\n",
    "        mol1_ind,\n",
    "        mol2_ind,\n",
    "        target_distances=target_distances,\n",
    "        temperature=600,\n",
    "        nsteps=20000,\n",
    "    )\n",
    "    mol = preliminary_md_results.get_main_molecule()\n",
    "\n",
    "    ts_search_results = ts_search(mol, mol1_ind, mol2_ind)\n",
    "    mol = ts_search_results.get_main_molecule()\n",
    "\n",
    "    relax_from_saddle_results = relax_from_saddle(mol)\n",
    "\n",
    "    return preliminary_md_results, ts_search_results, relax_from_saddle_results\n",
    "\n",
    "\n",
    "def ts_search(\n",
    "    molecule, atom_indices_1: List[int], atom_indices_2: List[int], settings: Settings = None\n",
    ") -> AMSResults:\n",
    "    if settings is None:\n",
    "        settings = Settings()\n",
    "        settings.input.DFTB\n",
    "        settings.input.ams.GeometryOptimization.InitialHessian.Type = \"Calculate\"\n",
    "        settings.input.ams.Properties.NormalModes = \"Yes\"\n",
    "        settings.input.ams.GeometryOptimization.Convergence.Quality = \"Good\"\n",
    "\n",
    "    s = settings.copy()\n",
    "    s.input.ams.task = \"TransitionStateSearch\"\n",
    "    s.input.ams.TransitionStateSearch.ReactionCoordinate.Distance = [\n",
    "        f\"{a1} {a2} 1.0\" for a1, a2 in zip(atom_indices_1, atom_indices_2)\n",
    "    ]\n",
    "\n",
    "    job = AMSJob(settings=s, name=\"ts_search\", molecule=molecule)\n",
    "    job.run()\n",
    "\n",
    "    return job.results\n",
    "\n",
    "\n",
    "def relax_from_saddle(molecule: Molecule, settings: Settings = None) -> AMSResults:\n",
    "    if settings is None:\n",
    "        settings = Settings()\n",
    "        settings.input.DFTB\n",
    "        settings.input.ams.GeometryOptimization.InitialHessian.Type = \"Calculate\"\n",
    "\n",
    "    s = settings.copy()\n",
    "    s.input.ams.task = \"PESExploration\"\n",
    "    s.input.ams.PESExploration.Job = \"LandscapeRefinement\"\n",
    "    s.input.ams.PESExploration.LandscapeRefinement.RelaxFromSaddlePoint = \"T\"\n",
    "\n",
    "    m = {\"state1 ts=Yes\": molecule}\n",
    "\n",
    "    job = AMSJob(settings=s, name=\"refinement\", molecule=m)\n",
    "    job.run()\n",
    "\n",
    "    return job.results\n",
    "\n",
    "\n",
    "def irc(molecule: Molecule, settings: Settings = None):\n",
    "    if settings is None:\n",
    "        settings = Settings()\n",
    "        settings.input.DFTB\n",
    "\n",
    "    s = settings.copy()\n",
    "    s.input.ams.task = \"IRC\"\n",
    "    s.input.ams.IRC.MinEnergyProfile = \"Yes\"\n",
    "\n",
    "    job = AMSJob(settings=s, name=\"irc\", molecule=molecule)\n",
    "    job.run()\n",
    "\n",
    "    converged = job.results.get_history_property(\"Converged\")\n",
    "    direction = job.results.get_history_property(\"IRCDirection\")\n",
    "    energies = job.results.get_history_property(\"Energy\")\n",
    "    ind = dict()\n",
    "    energy = dict()\n",
    "    for i, (c, d, e) in enumerate(zip(converged, direction, energies)):\n",
    "        if c:\n",
    "            ind[d] = i + 1\n",
    "            energy[d] = e\n",
    "\n",
    "    min1 = job.results.get_history_molecule(ind[1])\n",
    "    min2 = job.results.get_history_molecule(ind[2])\n",
    "    ts = job.results.get_input_molecule()\n",
    "\n",
    "    return min1, energy[1], min2, energy[2], ts, energies[0]\n",
    "\n",
    "\n",
    "def preliminary_md(\n",
    "    molecule: Molecule,\n",
    "    atom_indices_1: List[int],\n",
    "    atom_indices_2: List[int],\n",
    "    target_distances: List[float],\n",
    "    nsteps: int = 10000,\n",
    "    kappa: float = 100000,\n",
    "    settings: Settings = None,\n",
    "    temperature: float = 300,\n",
    ") -> Molecule:\n",
    "    if settings is None:\n",
    "        settings = Settings()\n",
    "        settings.input.ForceField.Type = \"UFF\"\n",
    "        settings.runscript.nproc = 1\n",
    "\n",
    "    plumed_input = \"\\n\"\n",
    "    for a1, a2, d in zip(atom_indices_1, atom_indices_2, target_distances):\n",
    "        plumed_input += f\"DISTANCE ATOMS={a1},{a2} LABEL=d_{a1}_{a2}\\n\"\n",
    "        plumed_input += f\"MOVINGRESTRAINT ARG=d_{a1}_{a2}\"\n",
    "        plumed_input += f\" STEP0=1 AT0={d * 0.1} KAPPA0=0\"\n",
    "        plumed_input += f\" STEP1={1 * nsteps // 4} KAPPA1={kappa / 1000}\"\n",
    "        plumed_input += f\" STEP2={2 * nsteps // 4} KAPPA2={kappa / 100}\"\n",
    "        plumed_input += f\" STEP3={3 * nsteps // 4} KAPPA3={kappa / 10}\"\n",
    "        plumed_input += f\" STEP4={4 * nsteps // 4} KAPPA4={kappa}\"\n",
    "        plumed_input += f\"\\n\"\n",
    "\n",
    "    plumed_input += \"   End\"\n",
    "    settings.input.ams.MolecularDynamics.Plumed.Input = plumed_input\n",
    "\n",
    "    job = AMSNVTJob(\n",
    "        name=\"preliminary_md\",\n",
    "        settings=settings,\n",
    "        molecule=molecule,\n",
    "        nsteps=nsteps,\n",
    "        temperature=3 * [temperature] + [1],\n",
    "    )\n",
    "    job.run()\n",
    "\n",
    "    return job.results\n",
    "\n",
    "\n",
    "def set_active(mol: Molecule, indices: List[int]):\n",
    "    for at in mol:\n",
    "        if \"active_bond\" in at.properties:\n",
    "            del at.properties[\"active_bond\"]\n",
    "    for i, ind in enumerate(indices, 1):\n",
    "        mol[ind].properties.active_bond = i\n",
    "\n",
    "\n",
    "def get_active_i(mol: Molecule) -> List[int]:\n",
    "    d = {}\n",
    "    for i, at in enumerate(mol, 1):\n",
    "        if \"active_bond\" in at.properties and at.properties.active_bond:\n",
    "            d[i] = at.properties.active_bond\n",
    "\n",
    "    return sorted(d, key=lambda x: d[x])\n",
    "\n",
    "\n",
    "def get_atom_radius(at: Atom) -> float:\n",
    "    return PeriodicTable.get_radius(at.symbol)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2361a67",
   "metadata": {},
   "source": [
    "## Run the calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71ae5463",
   "metadata": {},
   "outputs": [],
   "source": [
    "diene_smiles = \"C1C=CC=C1\"\n",
    "diene = from_smiles(\n",
    "    diene_smiles\n",
    ")  # carbon 2, 4 will form bonds. Do diene.write('diene.xyz') and open diene.xyz in the AMS GUI to find out which atom indices are correct.\n",
    "set_active(diene, [2, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8942fcbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "dienophile = from_smiles(\"N#CC=C\")  # carbon 1, 2 will form bonds\n",
    "set_active(dienophile, [1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2dfa41a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_image_grid(\n",
    "    {\"Diene (cyclopentadiene)\": view(diene), \"Dienophile (acrylonitrile)\": view(dienophile)}\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ddb7e3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08.04|17:57:07] JOB preliminary_md STARTED\n",
      "[08.04|17:57:07] JOB preliminary_md RUNNING\n",
      "[08.04|17:57:10] JOB preliminary_md FINISHED\n",
      "[08.04|17:57:10] JOB preliminary_md SUCCESSFUL\n",
      "[08.04|17:57:10] JOB ts_search STARTED\n",
      "[08.04|17:57:10] JOB ts_search RUNNING\n",
      "[08.04|17:57:12] JOB ts_search FINISHED\n",
      "[08.04|17:57:12] JOB ts_search SUCCESSFUL\n",
      "[08.04|17:57:12] JOB refinement STARTED\n",
      "[08.04|17:57:12] JOB refinement RUNNING\n",
      "[08.04|17:57:15] JOB refinement FINISHED\n",
      "[08.04|17:57:15] JOB refinement SUCCESSFUL\n"
     ]
    }
   ],
   "source": [
    "preliminary_md_results, ts_search_results, relax_from_saddle_results = addition(diene, dienophile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f0b95fa",
   "metadata": {},
   "source": [
    "## Preliminary biased MD (UFF) & TS search (DFTB) results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "019c7732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_md_system = preliminary_md_results.get_main_molecule()\n",
    "final_ts_system = ts_search_results.get_main_molecule()\n",
    "plot_image_grid(\n",
    "    {\n",
    "        \"Final system from preliminary biased MD\": view(final_md_system),\n",
    "        \"DFTB-optimized transition state\": view(final_ts_system),\n",
    "    }\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d68e613c",
   "metadata": {},
   "source": [
    "## Energy landscape refinement results (DFTB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64525ce9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All stationary points:\n",
      "======================\n",
      "State 1: C8H9N local minimum @ -24.90376083 Hartree (found 1 times, results on Refined1_MIN)\n",
      "State 2: C8H9N local minimum @ -24.83422283 Hartree (found 1 times, results on Refined2_MIN)\n",
      "State 3: C8H9N transition state @ -24.82034339 Hartree (found 1 times, results on Refined3_TS_1-2)\n",
      "  +- Reactants: State 1: C8H9N local minimum @ -24.90376083 Hartree (found 1 times, results on Refined1_MIN)\n",
      "     Products:  State 2: C8H9N local minimum @ -24.83422283 Hartree (found 1 times, results on Refined2_MIN)\n",
      "     Prefactors: 0.000E+00:0.000E+00 s^-1\n",
      "     Barriers: 2.270:0.378 eV\n"
     ]
    }
   ],
   "source": [
    "landscape = relax_from_saddle_results.get_energy_landscape()\n",
    "print(landscape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccde7597",
   "metadata": {},
   "source": [
    "Above we see that the forward and backward barriers are 2.27 and 0.39 eV, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1e21b3c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ha2eV = Units.convert(1.0, \"hartree\", \"eV\")\n",
    "energies = landscape[1].energy, landscape[3].energy, landscape[2].energy\n",
    "energies = (np.array(energies) - landscape[1].energy) * Ha2eV\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(energies)\n",
    "ax.set_ylabel(\"Relative energy (eV)\")\n",
    "ax.set_xticks([0, 1, 2], [\"State 1 (min)\", \"State 3 (TS)\", \"State 2 (min)\"])\n",
    "ax;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4a9e99c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_image_grid(\n",
    "    {\n",
    "        \"State 1 (minimum)\": view(landscape[1].molecule),\n",
    "        \"State 3 (Transition state)\": view(landscape[3].molecule),\n",
    "        \"State 2 (minimum)\": view(landscape[2].molecule),\n",
    "    },\n",
    "    rows=1,\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe91c191-4569-4047-9f5b-9acf056354e3",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}