{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cd021dc5",
   "metadata": {},
   "source": [
    "## Purpose\n",
    "Example showing the `UseLowestEnergy` feature of the Hybrid engine and the `OptimizeSpinRound` feature of the ADF engine.\n",
    "\n",
    "A bond scan is performed on hydrogen peroxide with UFF. The resulting\n",
    "structures are then recalculated with DFT using four different setups. In\n",
    "particular, one setup uses the Hybrid engine with DynamicFactors=UseLowestEnergy.\n",
    "Another uses the ADF engine with `OptimizeSpinRound` in combination with an `ElectronicTemperature`.\n",
    "\n",
    "In the end, the results of energy vs. bond length are plotted.\n",
    "\n",
    "## Initial imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "738dcc04",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scm.plams import from_smiles, Settings, AMSJob, Units"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f4a64cf",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df799e49",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initial_pesscan():\n",
    "    \"\"\"Run a bond scan for hydrogen peroxide with UFF. Returns the finished job\"\"\"\n",
    "    mol = from_smiles(\"OO\")  # hydrogen peroxide\n",
    "    s = Settings()\n",
    "    s.input.ams.Task = \"PESScan\"\n",
    "    s.input.ams.PESScan.ScanCoordinate.nPoints = 7\n",
    "    # Scan O-O bond length (atoms 1 and 2) between 1.2 and 2.5 Å\n",
    "    s.input.ams.PESScan.ScanCoordinate.Distance = \"2 1 1.2 2.5\"\n",
    "    s.input.ForceField.Type = \"UFF\"\n",
    "    job = AMSJob(settings=s, molecule=mol, name=\"initial_pesscan\")\n",
    "    job.run()\n",
    "    return job\n",
    "\n",
    "\n",
    "def singlet_settings(header=\"\"):\n",
    "    s = Settings()\n",
    "    s.Unrestricted = \"No\"\n",
    "    s.XC.GGA = \"PBE\"\n",
    "    s.Basis.Type = \"DZP\"\n",
    "    s.SCF.Iterations = 100\n",
    "    s._h = header\n",
    "    return s\n",
    "\n",
    "\n",
    "def triplet_settings(header=\"\"):\n",
    "    s = Settings()\n",
    "    s.Unrestricted = \"Yes\"\n",
    "    s.SpinPolarization = 2\n",
    "    s.XC.GGA = \"PBE\"\n",
    "    s.Basis.Type = \"DZP\"\n",
    "    s.SCF.Iterations = 100\n",
    "    s._h = header\n",
    "    return s\n",
    "\n",
    "\n",
    "def hybrid_settings():\n",
    "    \"\"\"Look at the file plams_workdir/hybrid/hybrid.in to see how the below is translated into text input for the hybrid engine\"\"\"\n",
    "    s = Settings()\n",
    "    s.input.Hybrid.Energy.DynamicFactors = \"UseLowestEnergy\"\n",
    "\n",
    "    s.input.Hybrid.Energy.Term = [\"Region=* EngineID=Singlet\", \"Region=* EngineID=Triplet\"]\n",
    "\n",
    "    s.input.Hybrid.Engine = [singlet_settings(\"ADF Singlet\"), triplet_settings(\"ADF Triplet\")]\n",
    "    return s\n",
    "\n",
    "\n",
    "def replay_job(rkf, engine=\"hybrid\"):\n",
    "    \"\"\"Replay the structures from the UFF pesscan with ADF. Use three different engines:\n",
    "    Hybrid: This will run all structures with both Singlet and Triplet and pick the lowest energy\n",
    "    Singlet: This runs all structures using the Singlet settings\n",
    "    Triplet: This runs all structures using the Triplet settings\n",
    "\n",
    "    Returns the finished job\n",
    "    \"\"\"\n",
    "    s = Settings()\n",
    "    if engine == \"hybrid\":\n",
    "        s = hybrid_settings()\n",
    "    elif engine == \"singlet\":\n",
    "        s.input.adf = singlet_settings()\n",
    "    elif engine == \"triplet\":\n",
    "        s.input.adf = triplet_settings()\n",
    "    elif engine == \"optimizespin\":\n",
    "        s.input.adf = triplet_settings()\n",
    "        s.input.adf.Occupations = \"ElectronicTemperature=300 OptimizeSpinRound=0.05\"\n",
    "\n",
    "    s.input.ams.Task = \"Replay\"\n",
    "    s.input.ams.Replay.File = rkf\n",
    "\n",
    "    job = AMSJob(settings=s, name=engine)\n",
    "    job.run()\n",
    "    return job\n",
    "\n",
    "\n",
    "def plot_results(singlet_job, triplet_job, hybrid_job, optimizespin_job):\n",
    "    \"\"\"\n",
    "    Generate a plot of the energy vs. bond length for the three different jobs.  Saves a plot to pesplot.png.\n",
    "    \"\"\"\n",
    "    bondlengths = singlet_job.results.get_pesscan_results()[\"RaveledPESCoords\"][0]\n",
    "    bondlengths = Units.convert(bondlengths, \"bohr\", \"angstrom\")\n",
    "\n",
    "    singlet_pes = singlet_job.results.get_pesscan_results()[\"PES\"]\n",
    "    triplet_pes = triplet_job.results.get_pesscan_results()[\"PES\"]\n",
    "    hybrid_pes = hybrid_job.results.get_pesscan_results()[\"PES\"]\n",
    "    hybrid_pes = [\n",
    "        x - 0.005 for x in hybrid_pes\n",
    "    ]  # slightly downshift for visual clarity when plotting\n",
    "    optimizespin_pes = optimizespin_job.results.get_pesscan_results()[\"PES\"]\n",
    "    optimizespin_pes = [\n",
    "        x - 0.010 for x in optimizespin_pes\n",
    "    ]  # slightly downshift for visual clarity when plotting\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(bondlengths, singlet_pes)\n",
    "    ax.plot(bondlengths, triplet_pes)\n",
    "    ax.plot(bondlengths, hybrid_pes)\n",
    "    ax.plot(bondlengths, optimizespin_pes)\n",
    "    ax.set_title(\"PES Scan for hydrogen peroxide\")\n",
    "    ax.set_xlabel(\"O-O bond length (Å)\")\n",
    "    ax.set_ylabel(\"Energy (hartree)\")\n",
    "    ax.legend(\n",
    "        [\n",
    "            \"Singlet\",\n",
    "            \"Triplet\",\n",
    "            \"Hybrid: Lowest energy (slightly shifted)\",\n",
    "            \"ADF: optimized spin (slightly shifted)\",\n",
    "        ]\n",
    "    )\n",
    "    fig.savefig(\"pesplot.png\")\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c37068c0",
   "metadata": {},
   "source": [
    "## Run the job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4d54388",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.04|14:14:45] JOB initial_pesscan STARTED\n",
      "[10.04|14:14:45] JOB initial_pesscan RUNNING\n",
      "[10.04|14:14:46] JOB initial_pesscan FINISHED\n",
      "[10.04|14:14:46] Job initial_pesscan reported warnings. Please check the output\n",
      "[10.04|14:14:46] JOB initial_pesscan SUCCESSFUL\n",
      "[10.04|14:14:46] Job initial_pesscan reported warnings. Please check the output\n",
      "[10.04|14:14:46] Job initial_pesscan reported warnings. Please check the output\n",
      "[10.04|14:14:46] Job initial_pesscan reported warnings. Please check the output\n",
      "[10.04|14:14:46] JOB singlet STARTED\n",
      "[10.04|14:14:46] JOB singlet RUNNING\n",
      "[10.04|14:14:54] JOB singlet FINISHED\n",
      "[10.04|14:14:54] JOB singlet SUCCESSFUL\n",
      "[10.04|14:14:54] JOB triplet STARTED\n",
      "[10.04|14:14:54] JOB triplet RUNNING\n",
      "[10.04|14:15:02] JOB triplet FINISHED\n",
      "[10.04|14:15:02] JOB triplet SUCCESSFUL\n",
      "[10.04|14:15:02] JOB hybrid STARTED\n",
      "[10.04|14:15:02] JOB hybrid RUNNING\n",
      "[10.04|14:15:17] JOB hybrid FINISHED\n",
      "[10.04|14:15:17] Job hybrid reported warnings. Please check the output\n",
      "[10.04|14:15:17] JOB hybrid SUCCESSFUL\n",
      "[10.04|14:15:17] Job hybrid reported warnings. Please check the output\n",
      "[10.04|14:15:17] Job hybrid reported warnings. Please check the output\n",
      "[10.04|14:15:17] Job hybrid reported warnings. Please check the output\n",
      "[10.04|14:15:17] JOB optimizespin STARTED\n",
      "[10.04|14:15:17] JOB optimizespin RUNNING\n",
      "[10.04|14:15:36] JOB optimizespin FINISHED\n",
      "[10.04|14:15:36] JOB optimizespin SUCCESSFUL\n"
     ]
    }
   ],
   "source": [
    "pesscan_job = initial_pesscan()\n",
    "rkf = pesscan_job.results.rkfpath()\n",
    "singlet_job = replay_job(rkf, \"singlet\")\n",
    "triplet_job = replay_job(rkf, \"triplet\")\n",
    "hybrid_job = replay_job(rkf, \"hybrid\")\n",
    "optimizespin_job = replay_job(rkf, \"optimizespin\")\n",
    "# or load the finished jobs from disk:\n",
    "# pesscan_job = AMSJob.load_external('plams_workdir.002/initial_pesscan/ams.rkf')\n",
    "# rkf = pesscan_job.results.rkfpath()\n",
    "# singlet_job = AMSJob.load_external('plams_workdir.002/singlet/ams.rkf')\n",
    "# triplet_job = AMSJob.load_external('plams_workdir.002/triplet/ams.rkf')\n",
    "# hybrid_job = AMSJob.load_external('plams_workdir.002/hybrid/ams.rkf')\n",
    "# optimizespin_job = AMSJob.load_external('plams_workdir.002/optimizespin/ams.rkf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395360bc",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Plot the result\n",
    "\n",
    "Two equivalent to follow the lowest energy of the singlet and the triplet state, with either the Hybrid engine (green) or the optimize spin option (red) from ADF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "55203cb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(singlet_job, triplet_job, hybrid_job, optimizespin_job);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63bde0a8",
   "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
}