{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "05a81f03-503e-414a-877b-4f8837d37eb5",
   "metadata": {},
   "source": "## Initial imports"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3fe257a7-2374-49d6-94a2-d648a30c3ad4",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:12:13.135364Z",
     "iopub.execute_input": "2024-05-31T09:12:13.135513Z",
     "shell.execute_reply": "2024-05-31T09:12:18.754369Z",
     "iopub.status.idle": "2024-05-31T09:12:18.755851Z"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Current AMS version: 2024.102\n05-31 11:12:18 m3gnet is installed: M3GNet ML Backend v[0.2.4] - build:0 [06668e0a45ce742d8f66ff23484b8a1e]\n05-31 11:12:18 qe is installed: Quantum ESPRESSO (AMSPIPE) v[7.1] - build:115 [777d72eb480fe4d632a003cc62e9c1cb]\n"
    }
   ],
   "source": [
    "import scm.plams as plams\n",
    "from scm.params import ResultsImporter\n",
    "from scm.plams import Settings, AMSJob, log, Molecule\n",
    "\n",
    "# common_ru_h.py must exist in the current working directory\n",
    "from common_ru_h import (\n",
    "    rotation,\n",
    "    dft_settings,\n",
    "    QEKPointsConfig,\n",
    "    lattice_optimization_settings,\n",
    "    plot_pesscan,\n",
    "    check_installation,\n",
    ")\n",
    "\n",
    "# register dependencies for AMSjobs, to support submitting this notebook directly to a cluster in AMS2025+ \n",
    "# dependency: {}  common_ru_h.py\n",
    "\n",
    "check_installation()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c19c0f9-001b-4ced-96ce-19a0878f9806",
   "metadata": {},
   "source": "## Initialize PLAMS working directory"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81d242b7-b776-491d-a463-cca1b62a70c0",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:12:18.760901Z",
     "iopub.execute_input": "2024-05-31T09:12:18.761105Z",
     "shell.execute_reply": "2024-05-31T09:12:18.795580Z",
     "iopub.status.idle": "2024-05-31T09:12:18.796951Z"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "PLAMS working folder: /home/hellstrom/SALRuH/fix2024/plams_workdir\n"
    }
   ],
   "source": "plams.init()"
  },
  {
   "cell_type": "markdown",
   "id": "31ab389e-ad8d-4753-b0a2-ea8d434ad794",
   "metadata": {},
   "source": "## Bulk structure: hcp Ru"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "380b978e-ee27-493e-ad72-61e59c719c6f",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:12:18.802077Z",
     "iopub.execute_input": "2024-05-31T09:12:18.802417Z",
     "shell.execute_reply": "2024-05-31T09:12:18.814667Z",
     "iopub.status.idle": "2024-05-31T09:12:18.815761Z"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[31.05|11:12:18] Initial structure\n[31.05|11:12:18]   Atoms: \n    1        Ru       0.000000       0.000000       0.000000 \n    2        Ru       0.000000       1.558846       2.138400 \n  Lattice:\n        2.7000000000     0.0000000000     0.0000000000\n       -1.3500000000     2.3382685902     0.0000000000\n        0.0000000000     0.0000000000     4.2768000000\n\n"
    }
   ],
   "source": "initial_bulk = plams.Molecule()\na = 2.7  # hexagonal lattice parameter, angstrom\nc = 4.2768  # hexagonal lattice parameter, angstrom\ninitial_bulk.add_atom(plams.Atom(symbol=\"Ru\", coords=(0.0, 0.0, 0.0)))\ninitial_bulk.add_atom(plams.Atom(symbol=\"Ru\", coords=(0.0, a / 3**0.5, c / 2)))\ninitial_bulk.lattice = [[a, 0, 0], [-a / 2, a * 3**0.5 / 2, 0], [0, 0, c]]\nlog(\"Initial structure\")\nlog(initial_bulk)"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7d103e0-7d71-40d0-9dad-a6ed8e665644",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:12:18.819685Z",
     "iopub.execute_input": "2024-05-31T09:12:18.819944Z",
     "iopub.status.idle": "2024-05-31T09:12:19.179183Z",
     "shell.execute_reply": "2024-05-31T09:12:19.178533Z"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {
      "needs_background": "light"
     },
     "data": {
      "text/plain": "<Figure size 144x144 with 1 Axes>",
      "image/png": "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\n"
     }
    }
   ],
   "source": "plams.plot_molecule(initial_bulk, rotation=rotation)"
  },
  {
   "cell_type": "markdown",
   "id": "9276b837-20bb-4fa9-a223-ba736f7f4d39",
   "metadata": {},
   "source": "## Lattice optimization of bulk Ru with DFT"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8c8218f-36a9-4795-9076-ed4da03cc87a",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:12:19.182187Z",
     "iopub.execute_input": "2024-05-31T09:12:19.182429Z",
     "iopub.status.idle": "2024-05-31T09:12:45.920127Z",
     "shell.execute_reply": "2024-05-31T09:12:45.919558Z"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[31.05|11:12:19] JOB hcp_lattopt_Ru_dft STARTED\n[31.05|11:12:19] JOB hcp_lattopt_Ru_dft RUNNING\n[31.05|11:12:44] JOB hcp_lattopt_Ru_dft FINISHED\n[31.05|11:12:45] JOB hcp_lattopt_Ru_dft SUCCESSFUL\n"
    }
   ],
   "source": "lattopt_job = plams.AMSJob(\n    settings=dft_settings(QEKPointsConfig(11, 11, 11)) + lattice_optimization_settings(),\n    name=\"hcp_lattopt_Ru_dft\",\n    molecule=initial_bulk,\n)\nlattopt_job.run();"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2cde1dee-689c-453b-b68e-2c8d90e95111",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:12:45.923711Z",
     "iopub.execute_input": "2024-05-31T09:12:45.923831Z",
     "shell.execute_reply": "2024-05-31T09:12:45.930723Z",
     "iopub.status.idle": "2024-05-31T09:12:45.931165Z"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[31.05|11:12:45]   Atoms: \n    1        Ru       0.000000       0.000000       0.000000 \n    2        Ru      -0.000000       1.561640       2.136759 \n  Lattice:\n        2.7048396424     0.0000000000     0.0000000000\n       -1.3524198212     2.3424598435     0.0000000000\n        0.0000000000     0.0000000000     4.2735187675\n\n[31.05|11:12:45] Density: 12396.59 kg/m^3\n"
    }
   ],
   "source": "optimized_bulk: Molecule = lattopt_job.results.get_main_molecule()  # type: ignore\nlog(optimized_bulk)\nlog(f\"Density: {optimized_bulk.get_density():.2f} kg/m^3\")"
  },
  {
   "cell_type": "markdown",
   "id": "8dbb546c-2557-4696-8d7e-736a67a557f0",
   "metadata": {},
   "source": "## Volume scan of bulk Ru with DFT"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13257df7-5324-4530-823e-898140db3562",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:12:45.934958Z",
     "iopub.execute_input": "2024-05-31T09:12:45.935071Z",
     "iopub.status.idle": "2024-05-31T09:14:18.019247Z",
     "shell.execute_reply": "2024-05-31T09:14:18.018691Z"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[31.05|11:12:45] JOB bulk_hcp_Ru_volume_scan_dft STARTED\n[31.05|11:12:45] JOB bulk_hcp_Ru_volume_scan_dft RUNNING\n[31.05|11:14:17] JOB bulk_hcp_Ru_volume_scan_dft FINISHED\n[31.05|11:14:18] JOB bulk_hcp_Ru_volume_scan_dft SUCCESSFUL\n"
    }
   ],
   "source": "from common_ru_h import (\n    dft_settings,\n    QEKPointsConfig,\n    pesscan_settings,\n    CellVolumeScalingRangeScanCoordinate,\n)\n\ns = dft_settings(QEKPointsConfig(11, 11, 11))\ns += pesscan_settings([CellVolumeScalingRangeScanCoordinate(0.85, 1.15)], n_points=7)\nvolume_scan_job = AMSJob(\n    settings=s,\n    molecule=optimized_bulk,\n    name=\"bulk_hcp_Ru_volume_scan_dft\",\n)\nvolume_scan_job.run();"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71c6254e",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:14:18.023160Z",
     "iopub.execute_input": "2024-05-31T09:14:18.023319Z",
     "shell.execute_reply": "2024-05-31T09:14:18.131447Z",
     "iopub.status.idle": "2024-05-31T09:14:18.131978Z"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {
      "needs_background": "light"
     },
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     }
    }
   ],
   "source": "plot_pesscan(volume_scan_job);"
  },
  {
   "cell_type": "markdown",
   "id": "3b5c3f82",
   "metadata": {},
   "source": "## Bond scan of H2 with DFT"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84f94c16",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:14:18.135403Z",
     "iopub.execute_input": "2024-05-31T09:14:18.135580Z",
     "iopub.status.idle": "2024-05-31T09:14:18.494849Z",
     "shell.execute_reply": "2024-05-31T09:14:18.494367Z"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {
      "needs_background": "light"
     },
     "data": {
      "text/plain": "<Figure size 144x144 with 1 Axes>",
      "image/png": "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\n"
     }
    }
   ],
   "source": "h2_mol = plams.from_smiles(\"[HH]\")\nh2_mol.lattice = [[5, 0, 0], [0, 5, 0], [0, 0, 5]]\nplams.plot_molecule(h2_mol, rotation=rotation)"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "321779b0",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:14:18.496847Z",
     "iopub.execute_input": "2024-05-31T09:14:18.497146Z",
     "iopub.status.idle": "2024-05-31T09:15:19.797093Z",
     "shell.execute_reply": "2024-05-31T09:15:19.796391Z"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[31.05|11:14:18] JOB h2_bond_scan_dft STARTED\n[31.05|11:14:18] JOB h2_bond_scan_dft RUNNING\n[31.05|11:15:19] JOB h2_bond_scan_dft FINISHED\n[31.05|11:15:19] Job h2_bond_scan_dft reported warnings. Please check the output\n[31.05|11:15:19] JOB h2_bond_scan_dft SUCCESSFUL\n"
    }
   ],
   "source": "from common_ru_h import (\n    dft_settings,\n    QEKPointsConfig,\n    pesscan_settings,\n    DistanceScanCoordinate,\n)\n\nscan_coordinate = DistanceScanCoordinate(atom1=1, atom2=2, start=0.55, end=1.0)\ns = dft_settings(QEKPointsConfig(1, 1, 1))\ns += pesscan_settings([scan_coordinate], n_points=7)\n\nh2_bond_scan_job = AMSJob(settings=s, molecule=h2_mol, name=\"h2_bond_scan_dft\")\n\nh2_bond_scan_job.run();"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ad6c5b32",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:15:19.799555Z",
     "iopub.execute_input": "2024-05-31T09:15:19.799685Z",
     "shell.execute_reply": "2024-05-31T09:15:19.887260Z",
     "iopub.status.idle": "2024-05-31T09:15:19.888285Z"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {
      "needs_background": "light"
     },
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     }
    }
   ],
   "source": "plot_pesscan(h2_bond_scan_job);"
  },
  {
   "cell_type": "markdown",
   "id": "fd78639c",
   "metadata": {},
   "source": "## Store results"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a6cb661",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-31T09:15:19.891110Z",
     "iopub.execute_input": "2024-05-31T09:15:19.891304Z",
     "shell.execute_reply": "2024-05-31T09:15:20.159337Z",
     "iopub.status.idle": "2024-05-31T09:15:20.160328Z"
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "['reference_data_1/job_collection.yaml',\n 'reference_data_1/results_importer_settings.yaml',\n 'reference_data_1/training_set.yaml']"
     },
     "execution_count": 12
    }
   ],
   "source": "ri = ResultsImporter.from_ase()\nproperties = [\"energy\", \"forces\"]\nri.add_pesscan_singlepoints(volume_scan_job, properties=properties)\nri.add_pesscan_singlepoints(h2_bond_scan_job, properties=properties)\nri.add_singlejob(lattopt_job, task=\"SinglePoint\", properties=properties)\n\n# Also add as PES Scans - these will not be used during training but\n# will plot the energy-volume curve and bond-scan curve at the end\n# of the training\nri.add_singlejob(volume_scan_job, task=\"PESScan\", properties=[\"pes\"])\nri.add_singlejob(h2_bond_scan_job, task=\"PESScan\", properties=[\"pes\"])\n\nri.store(\"reference_data_1\")"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.8.12",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}