{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88a4b6e6",
   "metadata": {},
   "source": [
    "# Tutorial 2 – Training Energies and Forces\n",
    "\n",
    "To predict potential energy surfaces (PESs) for multiple electronic states, we include the energies and forces of the respective electronic states in the training.\n",
    "The forces ($\\mathbf{F}_i$) are of shape $N_{atoms} \\times N_{states} \\times 3$ and describe the cartesian force acting on each atom ($i$) due to the potential energy ($E$), which has a shape $1 \\times N_{states}$. \n",
    "Thus, forces are defined as the negative gradient of the respective potential energy of each electronic state with respect to the nuclear position of each atom $\\mathbf{R}_i$.\n",
    "\n",
    "\\begin{equation}\n",
    "\\displaystyle \\mathbf{F}_i = - \\frac{\\partial E}{\\partial \\mathbf{R}_i}\n",
    "\\end{equation}\n",
    "\n",
    "In the machine learning model, the forces are implemented accordingly, *i.e.*, by defining a model for energy and taking the respective derivative.\n",
    "\n",
    "In the following we show how to train a model for a PES on energies and forces.\n",
    "\n",
    "## Load and Preprocess Data\n",
    "\n",
    "To process and prepare data for training, we begin with importing all relevant packages and specifying the location of an existing ASE (Atomic Simulation Envirnonment) database. Please return to the first tutorial to learn how to convert original quantum chemical data into an ASE database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a46cd29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "\n",
    "import torch\n",
    "import torchmetrics\n",
    "import pytorch_lightning as pl\n",
    "import schnetpack as spk\n",
    "\n",
    "import spainn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f47df7",
   "metadata": {},
   "source": [
    "Initially, we establish the pathway for reaching the database that we intend to employ for training the neural network (NN). \n",
    "It is essential to ensure that `'_distance_unit'`, `'atomrefs'`, and `'_property_unit_dict'` are all retained within the metadata. \n",
    "You can verify this by referring to the provided example below:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0708bea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_singlets': 3,\n",
       " 'n_triplets': 0,\n",
       " 'states': 'S S S ',\n",
       " 'phasecorrected': False,\n",
       " 'ReferenceMethod': 'MR-CISD(6,4)/aug-cc-pVDZ, program: COLUMBUS',\n",
       " 'atomrefs': {},\n",
       " '_distance_unit': 'Bohr',\n",
       " '_property_unit_dict': {'energy': 'Hartree',\n",
       "  'forces': 'Hartree/Bohr',\n",
       "  'nacs': '1',\n",
       "  'smooth_nacs': '1'}}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ase.db import connect\n",
    "dbname = 'spainn_ch2nh2+.db'\n",
    "datapath = os.path.join(os.getcwd(), 'data', dbname)\n",
    "db = connect(datapath)\n",
    "\n",
    "db.metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91edb6eb",
   "metadata": {},
   "source": [
    "Next the data is loaded form the database `spainn_ch2nh2+.db` from the data folder, which contains among others the energies and forces of the three lowest singlet states, *i.e.*, $S_0$, $S_1$, and $S_2$ (`nstates=3`) of 99 displaced geometries of the methylenimmonium cation. \n",
    "For this we use the ``SPAINN`` class that inhertits the ``AtomsDataModule`` class provided by SchNetPack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8125c081",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:00<00:00, 151.08it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'energy': (tensor([-15.7640, -15.7357, -15.7201], dtype=torch.float64), tensor([0.0095, 0.0057, 0.0057], dtype=torch.float64))}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%rm ./train/split.npz\n",
    "\n",
    "data_module = spainn.SPAINN(\n",
    "    n_states = 3, # singlet states 0, 1, 2\n",
    "    n_nacs = 3, # couplings: 01, 02, 12\n",
    "    datapath=datapath, # path to database\n",
    "    batch_size=2,\n",
    "    num_train=0.7, # 70% of databse for training\n",
    "    num_val=0.1, # 10% of database for validation\n",
    "    split_file=os.path.join('train', 'split.npz'), # path to model (training folder)\n",
    "    splitting=spk.data.splitting.RandomSplit(),\n",
    "    load_properties=['energy', 'forces'],\n",
    "    transforms=[\n",
    "        # remove mean of energy in every electronic state\n",
    "        spk.transform.RemoveOffsets(spainn.SPAINN.energy, remove_mean=True, remove_atomrefs=False),\n",
    "        spk.transform.ASENeighborList(cutoff=5.0),\n",
    "        spk.transform.CastTo32(),\n",
    "            ],\n",
    ")\n",
    "\n",
    "# setup everything\n",
    "data_module.prepare_data()\n",
    "data_module.setup()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6beb0449",
   "metadata": {},
   "source": [
    "Prior to constructing the model, we eliminate energy offsets to ensure favorable initial conditions. \n",
    "The necessary data for this is obtained from the training dataset. \n",
    "This process is facilitated by the `RemoveOffsets` transformation, which operates automatically and employs the energy-key attribute of the **SPaiNN** (`spainn.SPAINN.energy`) framework. \n",
    "To illustrate, one can access the names and dimensions of loaded properties through the data module using the provided approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e30d6e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded properties:\n",
      " _idx\ttorch.Size([1])\n",
      " energy\ttorch.Size([1, 3])\n",
      " forces\ttorch.Size([6, 3, 3])\n",
      " _n_atoms\ttorch.Size([1])\n",
      " _atomic_numbers\ttorch.Size([6])\n",
      " _positions\ttorch.Size([6, 3])\n",
      " _cell\ttorch.Size([1, 3, 3])\n",
      " _pbc\ttorch.Size([3])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "properties = data_module.dataset[0]\n",
    "print('Loaded properties:\\n', *[str(i)+'\\t'+str(properties[i].shape)+'\\n' for i in properties.keys()])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff6f93d",
   "metadata": {},
   "source": [
    "## Define the Model\n",
    "\n",
    "After the data preparation, we now build a model for training the NN PESs. \n",
    "This is done in three core steps:\n",
    "\n",
    "1. Defining input modules (pairwise distance)\n",
    "2. Building a representation (SchNet representation)\n",
    "3. Defining output modules (module for forces and energies)\n",
    "\n",
    "### Defining Input Modules\n",
    "\n",
    "Here we use a paiwise distance as input module and use the SchNet representation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "321b5af9",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_atom_basis = 30\n",
    "cutoff = 10.0\n",
    "\n",
    "# input module: calculates pairwise distances between atoms\n",
    "pairwise_distance = spk.atomistic.PairwiseDistances()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ede65f4b",
   "metadata": {},
   "source": [
    "### Building a Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33df7538",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SchNet Representation\n",
    "radial_basis = spk.nn.GaussianRBF(n_rbf=20, cutoff=cutoff)\n",
    "\n",
    "schnet = spk.representation.SchNet(\n",
    "    n_atom_basis=n_atom_basis,\n",
    "    n_interactions=3,\n",
    "    radial_basis=radial_basis,\n",
    "    cutoff_fn=spk.nn.CosineCutoff(cutoff)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c75960b",
   "metadata": {},
   "source": [
    "### Defining Output Modules\n",
    "\n",
    "As we want to model energies and forces, we need two output modules for predicting energies and forces. \n",
    "We will use an `Atomwise` module to predict the energies of the electronic states as implemented in SchNetPack, by adjusting the number of outputs to the number of electronic states, *e.g.*, `n_out=3`. \n",
    "To describe the forces as derivative of the energy for multiple electronic states, we use the `MultiStateForces` module of SPaiNN, that computes the negative derivative of the energy for every electronic state with respect to the nuclear positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d309db60",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_energy = spainn.Atomwise(\n",
    "    n_in=n_atom_basis,\n",
    "    n_out=3,\n",
    "    output_key=spainn.SPAINN.energy,\n",
    "    n_layers=3,\n",
    ")\n",
    "\n",
    "pred_forces = spainn.Forces(\n",
    "    energy_key=spainn.SPAINN.energy,\n",
    "    force_key=spainn.SPAINN.forces,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030e0ec1",
   "metadata": {},
   "source": [
    "In the following the representaon as well as input and output modules are assembled to a NN potential. \n",
    "In this step, we also specify a post-processor, that adds the mean, which we removed as offset in the data pre-processing, to the predictions to obtain energies on the scale of the reference data. \n",
    "This is done with the `AddMultipleOffsets` post-processor of **SPaiNN**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b86112ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "nnpot = spk.model.NeuralNetworkPotential(\n",
    "    representation=schnet,\n",
    "    input_modules=[pairwise_distance],\n",
    "    output_modules=[pred_energy, pred_forces],\n",
    "    input_dtype_str='float32',\n",
    "    do_postprocessing=True,\n",
    "    postprocessors=[\n",
    "        spk.transform.CastTo64(),\n",
    "        spk.transform.AddOffsets(spainn.SPAINN.energy, add_mean=True, add_atomrefs=False),\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deac0e04",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "For training a model, the training task, *i.e.*, model, loss-functions and optimizers have to be defined. First the output of the models are connected to loss functions using the `ModelOutput` class of SchNetPack. Here, the combined loss function reads as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\displaystyle \\mathcal{L}(E_{ref},\\mathbf{F}_{ref},E_{NN},\\mathbf{F}_{NN}) = -\\frac{1}{n_{train}}\\sum_{n=1}^{n_{train}} \\left( \\rho_1 \\mathcal{L}_E + \\rho_2 \\mathcal{L}_F \\right)\n",
    "\\end{equation}\n",
    "\n",
    "The loss weights $\\rho_1$ and $\\rho_2$ are introduced to control the tradeoff between energies and forces.\n",
    "For predicting energies and forces, we employ the standard mean-square error loss functions, that read as\n",
    "\\begin{equation}\n",
    "\\displaystyle \\mathcal{L}_E(E_{ref},E_{NN}) = ||E_{ref} - E_{NN}||_2~,~\\text{and}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "\\displaystyle \\mathcal{L}_F(\\mathbf{F_{ref}},-\\nabla_{\\mathbf{R}}E_{NN}) = ||\\mathbf{F_{ref}} - \\left( -\\nabla_{\\mathbf{R}}E_{NN}\\right) ||_2~.\n",
    "\\end{equation}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "896d01b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_energy = spk.task.ModelOutput(\n",
    "    name=spainn.SPAINN.energy,\n",
    "    loss_fn=torch.nn.MSELoss(),\n",
    "    loss_weight=0.01,\n",
    "    metrics={\n",
    "        \"MAE\": torchmetrics.MeanAbsoluteError(),\n",
    "    },\n",
    ")\n",
    "\n",
    "output_forces = spk.task.ModelOutput(\n",
    "    name=spainn.SPAINN.forces,\n",
    "    loss_fn=torch.nn.MSELoss(),\n",
    "    loss_weight=0.99,\n",
    "    metrics={\n",
    "        \"MAE\": torchmetrics.MeanAbsoluteError(),\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ecdfa0",
   "metadata": {},
   "source": [
    "In the next step we set up the task and trainer, *i.e.*, a PyTorch Lightning `Trainer` and train for 100 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56f6059b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/carolin/anaconda3/envs/spainn/lib/python3.9/site-packages/pytorch_lightning/utilities/parsing.py:197: UserWarning: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n",
      "  rank_zero_warn(\n",
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name    | Type                   | Params\n",
      "---------------------------------------------------\n",
      "0 | model   | NeuralNetworkPotential | 16.6 K\n",
      "1 | outputs | ModuleList             | 0     \n",
      "---------------------------------------------------\n",
      "16.6 K    Trainable params\n",
      "0         Non-trainable params\n",
      "16.6 K    Total params\n",
      "0.066     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2cd519db9c674110aa4db82dfda302d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=100` reached.\n"
     ]
    }
   ],
   "source": [
    "task = spk.task.AtomisticTask(\n",
    "    model=nnpot,\n",
    "    outputs=[output_energy, output_forces],\n",
    "    optimizer_cls=torch.optim.AdamW,\n",
    "    optimizer_args={\"lr\": 1e-4},\n",
    "    scheduler_monitor=\"val_loss\",\n",
    ")\n",
    "\n",
    "modelpath = os.path.join(os.getcwd(), 'train')\n",
    "callbacks = [\n",
    "    spk.train.ModelCheckpoint(\n",
    "        model_path=os.path.join(modelpath, \"best_model_E_F\"),\n",
    "        save_top_k=1,\n",
    "        monitor=\"val_loss\"\n",
    "    )\n",
    "]\n",
    "\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "trainer = pl.Trainer(\n",
    "    log_every_n_steps=1,\n",
    "    callbacks=callbacks,\n",
    "    logger=pl.loggers.TensorBoardLogger(save_dir=modelpath),\n",
    "    default_root_dir=modelpath,\n",
    "    max_epochs=100, # for testing, we restrict the number of epochs\n",
    ")\n",
    "\n",
    "trainer.fit(task, datamodule=data_module)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "970ca10e",
   "metadata": {},
   "source": [
    "## Using the Model\n",
    "\n",
    "To use the trained model, we load the best_model file and, *e.g.*, evaluate on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bf29da8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import spainn.plotting as sp\n",
    "\n",
    "plotter = sp.PlotMAE(\n",
    "    database = datapath,\n",
    "    split_file = os.path.join(os.getcwd(), 'train', 'split.npz'),\n",
    "    model_file = os.path.join(os.getcwd(), 'train', 'best_model_E_F'),\n",
    "    cutoff = cutoff,\n",
    "    properties2plot=['energy', 'forces'],\n",
    "    subset2plot=['test', 'train'],\n",
    ")\n",
    "\n",
    "plotter.plot()"
   ]
  }
 ],
 "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}