{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "26023df2-77a5-45be-bd6b-ac4eb028591f",
   "metadata": {},
   "source": [
    "## Requirements\n",
    "\n",
    "The package m3net can be installed with `amspackages`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4163a004-6028-483d-b2ee-d15f711c3ec1",
   "metadata": {},
   "source": [
    "## Purpose\n",
    "\n",
    "Use the M3GNet ML potential with AMS."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9a4166-86d0-4763-abf3-2485f8489d18",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed35131e-0bd6-4d85-8119-08f21182f0d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scm.plams import *\n",
    "\n",
    "# this line is not required in AMS2025+\n",
    "init()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da4bdbf6-5ef1-451c-9024-7112141e5b46",
   "metadata": {},
   "source": [
    "## Setup and run job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d36d8e41-4b34-427a-8059-1cdd36edbb83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[04.03|15:36:21] JOB ams_with_m3gnet STARTED\n",
      "[04.03|15:36:21] Renaming job ams_with_m3gnet to ams_with_m3gnet.002\n",
      "[04.03|15:36:21] Job ams_with_m3gnet.002 previously run as ams_with_m3gnet, using old results\n",
      "[04.03|15:36:21] JOB ams_with_m3gnet.002 COPIED\n",
      "M3GNet: final energy -14.077 eV\n"
     ]
    }
   ],
   "source": [
    "mol = from_smiles(\"O\")\n",
    "mol.lattice = [\n",
    "    [\n",
    "        3.0,\n",
    "        0.0,\n",
    "        0.0,\n",
    "    ],\n",
    "    [0.0, 3.0, 0.0],\n",
    "    [0.0, 0.0, 3.0],\n",
    "]\n",
    "\n",
    "s = Settings()\n",
    "s.runscript.nproc = 1\n",
    "s.input.ams.task = \"GeometryOptimization\"\n",
    "s.input.ams.GeometryOptimization.Convergence.Gradients = 0.01  # hartree/ang\n",
    "\n",
    "s.input.MLPotential.Model = \"M3GNet-UP-2022\"\n",
    "# If you have trained a custom M3GNet model yourself, you can use:\n",
    "# s.input.MLPotential.Model = 'Custom'\n",
    "# s.input.MLPotential.Backend = 'm3gnet'\n",
    "# s.input.MLPotential.ParameterDir = '<path to directory containing the M3GNet model>'\n",
    "\n",
    "job = AMSJob(settings=s, molecule=mol, name=\"ams_with_m3gnet\")\n",
    "job.run()\n",
    "\n",
    "energy = job.results.get_energy(unit=\"eV\")\n",
    "print(f\"M3GNet: final energy {energy:.3f} eV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c709008-c714-4763-86f2-2eaeadc445c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}