{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24f5f801-df17-4df2-ac82-37fc1dd73823",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import MLatom as a Python package.\n",
    "import mlatom as ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4f494b1-ecb0-4f9a-9cb6-0cde37bd8dfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model loaded from fulvene_tutorial.npz\n"
     ]
    }
   ],
   "source": [
    "#First, we define a MS-ANI model\n",
    "model = ml.models.msani(model_file='fulvene_tutorial.npz',nstates=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9f432e3-c06b-4fd7-aa3f-49086490d72b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#then, we load the training data as a MLatom molecular database. This is a small dataset for\n",
    "#demo purposes.\n",
    "train_data = ml.data.molecular_database.load(\"materials/tutorial_data.json\", format=\"json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac1d383d-04f6-4a01-a41f-f6b31ff886fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataParallel(\n",
      "  (module): StateInputNet(\n",
      "    (AEV_computer): AEVComputer()\n",
      "    (network): ANIModel(\n",
      "      (C): Sequential(\n",
      "        (0): Linear(in_features=129, out_features=160, bias=True)\n",
      "        (1): CELU(alpha=0.1)\n",
      "        (2): Linear(in_features=160, out_features=128, bias=True)\n",
      "        (3): CELU(alpha=0.1)\n",
      "        (4): Linear(in_features=128, out_features=96, bias=True)\n",
      "        (5): CELU(alpha=0.1)\n",
      "        (6): Linear(in_features=96, out_features=1, bias=True)\n",
      "      )\n",
      "      (H): Sequential(\n",
      "        (0): Linear(in_features=129, out_features=160, bias=True)\n",
      "        (1): CELU(alpha=0.1)\n",
      "        (2): Linear(in_features=160, out_features=128, bias=True)\n",
      "        (3): CELU(alpha=0.1)\n",
      "        (4): Linear(in_features=128, out_features=96, bias=True)\n",
      "        (5): CELU(alpha=0.1)\n",
      "        (6): Linear(in_features=96, out_features=1, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      ")\n",
      "training starting from epoch 1\n",
      "validation MSE: 0.0008031397592276335 at epoch 1\n",
      "learning_rate: 0.001\n",
      "model saved in fulvene_tutorial.npz\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "epoch 1: 100%|██████████| 5/5 [00:00<00:00, 21.23it/s]"
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    {
     "name": "stdout",
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     "text": [
      "validation MSE: 0.1126839853823185 at epoch 2\n"
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     "text": [
      "\n"
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     "text": [
      "learning_rate: 0.001\n"
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     "text": [
      "epoch 2: 100%|██████████| 5/5 [00:00<00:00, 23.62it/s]"
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     "text": [
      "validation MSE: 0.04905819706618786 at epoch 3\n"
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     "text": [
      "\n"
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     "text": [
      "learning_rate: 0.001\n"
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     "text": [
      "epoch 3: 100%|██████████| 5/5 [00:00<00:00, 24.73it/s]"
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     "text": [
      "validation MSE: 0.04490021988749504 at epoch 4\n"
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     "text": [
      "learning_rate: 0.001\n"
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     "text": [
      "epoch 4: 100%|██████████| 5/5 [00:00<00:00, 23.00it/s]"
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     "text": [
      "validation MSE: 0.01783759333193302 at epoch 5\n"
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      "learning_rate: 0.001\n"
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     "text": [
      "epoch 5: 100%|██████████| 5/5 [00:00<00:00, 23.94it/s]"
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     "text": [
      "validation MSE: 0.011365967337042093 at epoch 6\n"
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     "text": [
      "learning_rate: 0.001\n"
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     "text": [
      "epoch 6: 100%|██████████| 5/5 [00:00<00:00, 22.87it/s]"
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     "text": [
      "validation MSE: 0.006692196475341916 at epoch 7\n"
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     "text": [
      "epoch 7: 100%|██████████| 5/5 [00:00<00:00, 22.53it/s]"
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     "text": [
      "validation MSE: 0.003546430729329586 at epoch 8\n"
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      "epoch 8: 100%|██████████| 5/5 [00:00<00:00, 22.87it/s]"
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      "validation MSE: 0.0035988513845950365 at epoch 9\n"
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     "text": [
      "epoch 9: 100%|██████████| 5/5 [00:00<00:00, 24.89it/s]"
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      "validation MSE: 0.002651902032084763 at epoch 10\n"
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      "epoch 10: 100%|██████████| 5/5 [00:00<00:00, 24.38it/s]"
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     "text": [
      "validation MSE: 0.0025488075334578753 at epoch 11\n"
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      "validation MSE: 0.0017832341836765409 at epoch 12\n"
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      "epoch 12: 100%|██████████| 5/5 [00:00<00:00, 24.85it/s]"
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      "validation MSE: 0.0017284172354266047 at epoch 13\n"
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      "epoch 13: 100%|██████████| 5/5 [00:00<00:00, 23.59it/s]"
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      "validation MSE: 0.0016279628034681082 at epoch 14\n"
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     "text": [
      "learning_rate: 0.001\n"
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     "text": [
      "epoch 14: 100%|██████████| 5/5 [00:00<00:00, 25.11it/s]\n"
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      "validation MSE: 0.0014372477307915688 at epoch 15\n",
      "learning_rate: 0.001\n"
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      "epoch 15: 100%|██████████| 5/5 [00:00<00:00, 22.94it/s]"
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     "text": [
      "validation MSE: 0.0013517180923372507 at epoch 16\n"
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      "epoch 16: 100%|██████████| 5/5 [00:00<00:00, 22.44it/s]"
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      "validation MSE: 0.0012920504668727517 at epoch 17\n"
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      "epoch 17: 100%|██████████| 5/5 [00:00<00:00, 23.42it/s]"
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     "text": [
      "validation MSE: 0.001292596512939781 at epoch 18\n"
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      "validation MSE: 0.0012291798484511673 at epoch 19\n"
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      "validation MSE: 0.0011914747301489115 at epoch 20\n"
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     "text": [
      "validation MSE: 0.0011758446344174445 at epoch 21\n"
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      "epoch 21: 100%|██████████| 5/5 [00:00<00:00, 24.12it/s]"
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      "validation MSE: 0.0011379430070519447 at epoch 22\n"
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     "text": [
      "epoch 22: 100%|██████████| 5/5 [00:00<00:00, 25.22it/s]\n"
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      "validation MSE: 0.0011154452804476023 at epoch 23\n",
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      "validation MSE: 0.0011075155343860388 at epoch 24\n"
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      "validation MSE: 0.0010876927408389747 at epoch 25\n"
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      "validation MSE: 0.0010700172279030085 at epoch 26\n"
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      "validation MSE: 0.0010618061933200806 at epoch 27\n"
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      "validation MSE: 0.0010476874886080623 at epoch 28\n"
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      "validation MSE: 0.0010262619762215763 at epoch 30\n"
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      "validation MSE: 0.0010143694235011935 at epoch 31\n"
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     "text": [
      "epoch 100: 100%|██████████| 5/5 [00:00<00:00, 22.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model loaded from fulvene_tutorial.npz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Now we can train the model.\n",
    "model.train(molecular_database=train_data,\n",
    "            property_to_learn='energy',\n",
    "            xyz_derivative_property_to_learn='energy_gradients',\n",
    "            hyperparameters={'max_epochs': 100}) #100 epochs is not enough, only for demo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7c0c814-454e-422d-a573-b7ae5c577336",
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The model can be used to make single-point predictions\n",
    "#First we need to load a fulvene molecule from .xyz\n",
    "mol = ml.data.molecule()\n",
    "mol.read_from_xyz_file('materials/fulvene.xyz')\n",
    "mol.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f6766aa9-6f95-4946-bab5-19511598bed1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-230.5708011228777\n",
      "-230.50524622837077\n",
      "The S1-S0 gap is 1.78384097772432 eV\n"
     ]
    }
   ],
   "source": [
    "#Now we can make a prediction\n",
    "model.predict(molecule=mol, nstates=2, current_state=0,\n",
    "              calculate_energy=True, calculate_energy_gradients=True)\n",
    "\n",
    "#And print the results\n",
    "for state in mol.electronic_states:\n",
    "    print(state.energy)\n",
    "gap = (mol.electronic_states[1].energy-mol.electronic_states[0].energy)*ml.constants.Hartree2eV\n",
    "print(\"The S1-S0 gap is {} eV\".format(gap))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0a622412-701f-4bc8-9698-0a663ba8395f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model loaded from materials/fulvene_energy_iteration_19.npz\n"
     ]
    }
   ],
   "source": [
    "#Finally, we can use the MS-ANI model to run NAMD.\n",
    "#For that we will load a model trained in the AL loop.\n",
    "model_namd = ml.models.msani(model_file='materials/fulvene_energy_iteration_19.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3d89dec-f58b-4e8c-957f-2958ee34e5a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load an initial conditions database\n",
    "init_db = ml.data.molecular_database.load(\"materials/init_cond_db.json\", format=\"json\")\n",
    "#And define NAMD arguments\n",
    "timemax = 60 # fs\n",
    "namd_kwargs = {\n",
    "            'model': model_namd,\n",
    "            'time_step': 0.1, # fs\n",
    "            'maximum_propagation_time': timemax,\n",
    "            'hopping_algorithm': 'LZBL',\n",
    "            'nstates': 2,\n",
    "            'reduce_kinetic_energy': True,\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8fca76e7-e0c4-4d15-b69f-7883ecc4ec51",
   "metadata": {},
   "outputs": [],
   "source": [
    "#And finally run the NAMD. This should take about a minute.\n",
    "dyns = ml.simulations.run_in_parallel(molecular_database=init_db[:4], task=ml.namd.surface_hopping_md, task_kwargs=namd_kwargs, create_and_keep_temp_directories=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e31a998-54b1-461e-8e7e-04c59a7486d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now we can plot the results\n",
    "trajs = [d.molecular_trajectory for d in dyns]\n",
    "ml.namd.plot_population(trajectories=trajs, time_step=0.1,\n",
    "                        max_propagation_time=timemax, nstates=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2ade16a-70e1-450a-8fdc-811f5c7793b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#MLatom trajectories can be analyzed and visualized using simple Python code. An example below.\n",
    "energies = [[],[]]\n",
    "time = []\n",
    "gap = []\n",
    "for step in trajs[0].steps:\n",
    "    energies[0].append(step.molecule.electronic_states[0].energy)\n",
    "    energies[1].append(step.molecule.electronic_states[1].energy)\n",
    "    time.append(step.time)\n",
    "    gap.append(step.molecule.electronic_states[1].energy-step.molecule.electronic_states[0].energy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cdeda988-5ea0-4185-8e98-4446b377056f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2ae60d7b0490>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.plot(time, np.transpose(energies))\n",
    "plt.xlabel(\"Time (fs)\")\n",
    "plt.ylabel(\"Energy (Hartree)\")\n",
    "plt.legend(['S0', 'S1'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6679c83b-3c2b-4580-98fa-5b21454480fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "viewer_17258712895612643.render();\n",
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    "trajs[0].view()"
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   "id": "5c6701cb-6dc9-432a-9365-a1bf1daa86c8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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