notebooks/showcase_energy_force_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8bf28f0c",
   "metadata": {},
   "source": [
    "# Molecular dynamics from NN potentials"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c14ef85",
   "metadata": {},
   "source": [
    "## 1. Datset with energy and forces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9039a4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from kgcnn.data.datasets.MD17RevisedDataset import MD17RevisedDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "968d1d52",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:kgcnn.data.download:Checking and possibly downloading dataset with name MD17Revised\n",
      "INFO:kgcnn.data.download:Dataset directory located at C:\\Users\\patri\\.kgcnn\\datasets\n",
      "INFO:kgcnn.data.download:Dataset directory found. Done.\n",
      "INFO:kgcnn.data.download:Dataset found. Done.\n",
      "INFO:kgcnn.data.download:Directory for extraction exists. Done.\n",
      "INFO:kgcnn.data.download:Not extracting tar File. Stopped.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of steps 100000\n"
     ]
    }
   ],
   "source": [
    "dataset = MD17RevisedDataset(\"aspirin\")\n",
    "print(\"Number of steps\", len(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "31504d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['old_indices', 'old_energies', 'old_forces', 'energy', 'force', 'atomic_number', 'node_coordinates', 'range_indices', 'range_attributes', 'total_nodes', 'total_ranges'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.rename_property_on_graphs(old_property_name=\"energies\", new_property_name=\"energy\")\n",
    "dataset.rename_property_on_graphs(old_property_name=\"forces\", new_property_name=\"force\")\n",
    "dataset.rename_property_on_graphs(old_property_name=\"nuclear_charges\", new_property_name=\"atomic_number\")\n",
    "dataset.rename_property_on_graphs(old_property_name=\"coords\", new_property_name=\"node_coordinates\")\n",
    "data = dataset[:500]\n",
    "data.map_list(method=\"set_range\", node_coordinates=\"node_coordinates\")\n",
    "data.map_list(method=\"count_nodes_and_edges\", total_edges=\"total_ranges\", count_edges=\"range_indices\", \n",
    "    count_nodes=\"atomic_number\", total_nodes=\"total_nodes\")\n",
    "data[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b4fce4b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((500, 1), array([-17469.90164208]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eng = np.array(data.get(\"energy\"))*0.043  # kcal/mol to eV\n",
    "eng.shape, eng[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b0e78fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.74137341,  2.67700611, -0.75129579],\n",
       "       [-0.90631249, -0.85888416,  2.00726686],\n",
       "       [ 3.08056969, -2.19163918, -0.22727128],\n",
       "       [-0.37739256,  1.81854455, -0.04203819],\n",
       "       [ 0.06849278,  1.909797  ,  0.88471585],\n",
       "       [-0.57261954,  0.14867004, -0.18995724],\n",
       "       [ 0.22410948,  0.77632092, -2.1626258 ],\n",
       "       [-0.33297714, -1.23917868, -0.10066419],\n",
       "       [ 0.52634416,  3.8645722 ,  2.08451895],\n",
       "       [-0.72435461,  1.99949058,  0.7122311 ],\n",
       "       [ 1.25228829, -1.18763783,  0.57869244],\n",
       "       [ 0.61277482, -3.80501328, -2.03832495],\n",
       "       [ 1.67075583, -0.99873548, -0.52365172],\n",
       "       [-0.30834985, -0.53698349,  0.41058368],\n",
       "       [ 1.24551168, -1.9163207 ,  1.40335446],\n",
       "       [ 0.13337422,  0.1260334 , -0.94909162],\n",
       "       [-1.12054561,  0.36520689, -0.17419492],\n",
       "       [-0.46182796,  0.5091511 , -0.24017999],\n",
       "       [-0.29275676, -1.11448041, -0.71522209],\n",
       "       [-0.47046873,  0.30675033, -0.52878298],\n",
       "       [-0.50524218, -0.65266984,  0.56193748]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forces = data.tensor({\"name\": \"force\", \"shape\": (None, 3)})*0.043  # kcal/mol to eV\n",
    "atoms = data.get(\"atomic_number\")\n",
    "forces[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "44e093c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.09150233],\n",
       "       [ 0.09685228],\n",
       "       [-0.32295041]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from kgcnn.data.transform.scaler.force import EnergyForceExtensiveLabelScaler\n",
    "scaler = EnergyForceExtensiveLabelScaler(standardize_coordinates=False, standardize_scale=False)  # For testing no scale.\n",
    "eng, _ = scaler.fit_transform(X=None, y=eng, force=forces, atomic_number=atoms)\n",
    "eng[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8970c9a2",
   "metadata": {},
   "source": [
    "## 2. `EnergyForceModel` container"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a4e73b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kgcnn.models.force import EnergyForceModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "893c1b09",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:kgcnn.models.utils:Model kwargs: Overwriting dictionary of output_mlp with None\n",
      "INFO:kgcnn.models.utils:Updated model kwargs: '{'name': 'Schnet', 'inputs': [{'shape': [None], 'name': 'atomic_number', 'dtype': 'int32'}, {'shape': [None, 3], 'name': 'node_coordinates', 'dtype': 'float32'}, {'shape': [None, 2], 'name': 'range_indices', 'dtype': 'int64'}, {'shape': (), 'name': 'total_nodes', 'dtype': 'int64'}, {'shape': (), 'name': 'total_ranges', 'dtype': 'int64'}], 'input_tensor_type': 'padded', 'input_embedding': None, 'cast_disjoint_kwargs': {'padded_disjoint': False}, 'input_node_embedding': {'input_dim': 95, 'output_dim': 64}, 'make_distance': True, 'expand_distance': True, 'interaction_args': {'units': 128, 'use_bias': True, 'activation': {'class_name': 'function', 'config': 'kgcnn>shifted_softplus'}, 'cfconv_pool': 'scatter_sum'}, 'node_pooling_args': {'pooling_method': 'scatter_sum'}, 'depth': 4, 'gauss_args': {'bins': 20, 'distance': 4, 'offset': 0.0, 'sigma': 0.4}, 'verbose': 10, 'last_mlp': {'use_bias': [True, True, True], 'units': [128, 64, 1], 'activation': [{'class_name': 'function', 'config': 'kgcnn>shifted_softplus'}, {'class_name': 'function', 'config': 'kgcnn>shifted_softplus'}, 'linear']}, 'output_embedding': 'graph', 'output_to_tensor': None, 'use_output_mlp': False, 'output_tensor_type': 'padded', 'output_scaling': None, 'output_mlp': None}'.\n"
     ]
    }
   ],
   "source": [
    "model_config = {\n",
    "    \"name\": \"Schnet\",\n",
    "    \"coordinate_input\": 1,\n",
    "    \"inputs\": [\n",
    "        {\"shape\": [None], \"name\": \"node_number\", \"dtype\": \"int32\"},\n",
    "        {\"shape\": [None, 3], \"name\": \"node_coordinates\", \"dtype\": \"float32\"},\n",
    "        {\"shape\": [None, 2], \"name\": \"range_indices\", \"dtype\": \"int64\"},\n",
    "        {\"shape\": (), \"name\": \"total_nodes\", \"dtype\": \"int64\"},\n",
    "        {\"shape\": (), \"name\": \"total_ranges\", \"dtype\": \"int64\"}\n",
    "    ],\n",
    "    \"nested_model_config\": True,\n",
    "    \"output_to_tensor\": True,\n",
    "    \"output_squeeze_states\": True,\n",
    "    \"model_energy\": {\n",
    "        \"class_name\": \"make_model\",\n",
    "        \"module_name\": \"kgcnn.literature.Schnet\",\n",
    "        \"config\": {\n",
    "            \"name\": \"Schnet\",\n",
    "            \"inputs\": [\n",
    "                {\"shape\": [None], \"name\": \"atomic_number\", \"dtype\": \"int32\"},\n",
    "                {\"shape\": [None, 3], \"name\": \"node_coordinates\", \"dtype\": \"float32\"},\n",
    "                {\"shape\": [None, 2], \"name\": \"range_indices\", \"dtype\": \"int64\"},\n",
    "                {\"shape\": (), \"name\": \"total_nodes\", \"dtype\": \"int64\"},\n",
    "                {\"shape\": (), \"name\": \"total_ranges\", \"dtype\": \"int64\"}\n",
    "            ],\n",
    "            \"cast_disjoint_kwargs\": {\"padded_disjoint\": False},\n",
    "            \"input_node_embedding\": {\"input_dim\": 95, \"output_dim\": 64},\n",
    "            \"last_mlp\": {\"use_bias\": [True, True, True], \"units\": [128, 64, 1],\n",
    "                         \"activation\": [\n",
    "                             {\"class_name\": \"function\", \"config\": \"kgcnn>shifted_softplus\"}, \n",
    "                             {\"class_name\": \"function\", \"config\": \"kgcnn>shifted_softplus\"}, \n",
    "                             'linear'\n",
    "                         ]},\n",
    "            \"interaction_args\": {\n",
    "                \"units\": 128, \"use_bias\": True, \n",
    "                \"activation\": {\"class_name\": \"function\", \"config\": \"kgcnn>shifted_softplus\"},\n",
    "                \"cfconv_pool\": \"scatter_sum\"\n",
    "            },\n",
    "            \"node_pooling_args\": {\"pooling_method\": \"scatter_sum\"},\n",
    "            \"depth\": 4,\n",
    "            \"gauss_args\": {\"bins\": 20, \"distance\": 4, \"offset\": 0.0, \"sigma\": 0.4}, \"verbose\": 10,\n",
    "            \"output_embedding\": \"graph\",\n",
    "            \"use_output_mlp\": False,\n",
    "            \"output_mlp\": None\n",
    "        }\n",
    "    },\n",
    "    \"outputs\": {\"energy\": {\"name\": \"energy\", \"shape\": (1,)},\n",
    "                \"forces\": {\"name\": \"force\", \"shape\": (None, 3)}}\n",
    "\n",
    "}\n",
    "model = EnergyForceModel(**model_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95618ef0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(500, 21), (500, 21, 3), (500, 232, 2), (500,), (500,)]\n"
     ]
    }
   ],
   "source": [
    "x_tensor = data.tensor(model_config[\"model_energy\"][\"config\"][\"inputs\"])\n",
    "print([x.shape for x in x_tensor])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4c51344b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step\n"
     ]
    }
   ],
   "source": [
    "test_out = model.predict(x_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c031e38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(500, 1), (500, 21, 3)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i.shape for i in test_out.values()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9d43121c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "654166f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss={\"energy\": \"mean_absolute_error\", \"force\": \"mean_absolute_error\"},\n",
    "    optimizer=Adam(learning_rate=5e-04),\n",
    "    metrics=None,\n",
    "    loss_weights={\"energy\":1.0, \"force\":20.0},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2b8f903a-1c54-4132-8133-704859832bcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n"
     ]
    }
   ],
   "source": [
    "model.predict(x_tensor, batch_size=2, steps=2)\n",
    "model._compile_loss.build({\"energy\": eng, \"force\": forces}, {\"energy\": eng, \"force\": forces})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4747a58b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1294/1500\n",
      "8/8 - 1s - loss: 1.8698 - energy_loss: 0.3706 - force_loss: 0.0750 - 610ms/epoch - 76ms/step\n",
      "Epoch 1295/1500\n",
      "8/8 - 1s - loss: 1.8268 - energy_loss: 0.2774 - force_loss: 0.0775 - 645ms/epoch - 81ms/step\n",
      "Epoch 1296/1500\n",
      "8/8 - 1s - loss: 1.7237 - energy_loss: 0.3004 - force_loss: 0.0712 - 611ms/epoch - 76ms/step\n",
      "Epoch 1297/1500\n",
      "8/8 - 1s - loss: 1.7796 - energy_loss: 0.3744 - force_loss: 0.0703 - 603ms/epoch - 75ms/step\n",
      "Epoch 1298/1500\n",
      "8/8 - 1s - loss: 1.6922 - energy_loss: 0.2727 - force_loss: 0.0710 - 598ms/epoch - 75ms/step\n",
      "Epoch 1299/1500\n",
      "8/8 - 1s - loss: 1.7245 - energy_loss: 0.2860 - force_loss: 0.0719 - 614ms/epoch - 77ms/step\n",
      "Epoch 1300/1500\n",
      "8/8 - 1s - loss: 1.8538 - energy_loss: 0.2733 - force_loss: 0.0790 - 618ms/epoch - 77ms/step\n",
      "Epoch 1301/1500\n",
      "8/8 - 1s - loss: 1.9914 - energy_loss: 0.3206 - force_loss: 0.0835 - 652ms/epoch - 81ms/step\n",
      "Epoch 1302/1500\n",
      "8/8 - 1s - loss: 2.0053 - energy_loss: 0.3555 - force_loss: 0.0825 - 633ms/epoch - 79ms/step\n",
      "Epoch 1303/1500\n",
      "8/8 - 1s - loss: 1.9532 - energy_loss: 0.3366 - force_loss: 0.0808 - 626ms/epoch - 78ms/step\n",
      "Epoch 1304/1500\n",
      "8/8 - 1s - loss: 1.8114 - energy_loss: 0.3398 - force_loss: 0.0736 - 700ms/epoch - 88ms/step\n",
      "Epoch 1305/1500\n",
      "8/8 - 1s - loss: 1.7916 - energy_loss: 0.3008 - force_loss: 0.0745 - 622ms/epoch - 78ms/step\n",
      "Epoch 1306/1500\n",
      "8/8 - 1s - loss: 1.8668 - energy_loss: 0.3107 - force_loss: 0.0778 - 643ms/epoch - 80ms/step\n",
      "Epoch 1307/1500\n",
      "8/8 - 1s - loss: 1.8482 - energy_loss: 0.3210 - force_loss: 0.0764 - 656ms/epoch - 82ms/step\n",
      "Epoch 1308/1500\n",
      "8/8 - 1s - loss: 1.7902 - energy_loss: 0.3178 - force_loss: 0.0736 - 608ms/epoch - 76ms/step\n",
      "Epoch 1309/1500\n",
      "8/8 - 1s - loss: 1.8800 - energy_loss: 0.3030 - force_loss: 0.0788 - 608ms/epoch - 76ms/step\n",
      "Epoch 1310/1500\n",
      "8/8 - 1s - loss: 1.8607 - energy_loss: 0.3030 - force_loss: 0.0779 - 613ms/epoch - 77ms/step\n",
      "Epoch 1311/1500\n",
      "8/8 - 1s - loss: 1.8861 - energy_loss: 0.3625 - force_loss: 0.0762 - 688ms/epoch - 86ms/step\n",
      "Epoch 1312/1500\n",
      "8/8 - 1s - loss: 1.8314 - energy_loss: 0.3846 - force_loss: 0.0723 - 682ms/epoch - 85ms/step\n",
      "Epoch 1313/1500\n",
      "8/8 - 1s - loss: 1.7122 - energy_loss: 0.2850 - force_loss: 0.0714 - 624ms/epoch - 78ms/step\n",
      "Epoch 1314/1500\n",
      "8/8 - 1s - loss: 1.7456 - energy_loss: 0.3504 - force_loss: 0.0698 - 623ms/epoch - 78ms/step\n",
      "Epoch 1315/1500\n",
      "8/8 - 1s - loss: 1.7063 - energy_loss: 0.3121 - force_loss: 0.0697 - 622ms/epoch - 78ms/step\n",
      "Epoch 1316/1500\n",
      "8/8 - 1s - loss: 1.8857 - energy_loss: 0.5552 - force_loss: 0.0665 - 631ms/epoch - 79ms/step\n",
      "Epoch 1317/1500\n",
      "8/8 - 1s - loss: 1.6442 - energy_loss: 0.2896 - force_loss: 0.0677 - 593ms/epoch - 74ms/step\n",
      "Epoch 1318/1500\n",
      "8/8 - 1s - loss: 1.6358 - energy_loss: 0.2713 - force_loss: 0.0682 - 551ms/epoch - 69ms/step\n",
      "Epoch 1319/1500\n",
      "8/8 - 1s - loss: 1.9531 - energy_loss: 0.5791 - force_loss: 0.0687 - 551ms/epoch - 69ms/step\n",
      "Epoch 1320/1500\n",
      "8/8 - 1s - loss: 2.0096 - energy_loss: 0.5549 - force_loss: 0.0727 - 550ms/epoch - 69ms/step\n",
      "Epoch 1321/1500\n",
      "8/8 - 1s - loss: 1.6032 - energy_loss: 0.1511 - force_loss: 0.0726 - 543ms/epoch - 68ms/step\n",
      "Epoch 1322/1500\n",
      "8/8 - 1s - loss: 1.5218 - energy_loss: 0.1413 - force_loss: 0.0690 - 555ms/epoch - 69ms/step\n",
      "Epoch 1323/1500\n",
      "8/8 - 1s - loss: 1.4943 - energy_loss: 0.1413 - force_loss: 0.0676 - 545ms/epoch - 68ms/step\n",
      "Epoch 1324/1500\n",
      "8/8 - 1s - loss: 1.4555 - energy_loss: 0.1461 - force_loss: 0.0655 - 548ms/epoch - 69ms/step\n",
      "Epoch 1325/1500\n",
      "8/8 - 1s - loss: 1.4779 - energy_loss: 0.1428 - force_loss: 0.0668 - 545ms/epoch - 68ms/step\n",
      "Epoch 1326/1500\n",
      "8/8 - 1s - loss: 1.9965 - energy_loss: 0.6132 - force_loss: 0.0692 - 550ms/epoch - 69ms/step\n",
      "Epoch 1327/1500\n",
      "8/8 - 1s - loss: 1.9107 - energy_loss: 0.5445 - force_loss: 0.0683 - 553ms/epoch - 69ms/step\n",
      "Epoch 1328/1500\n",
      "8/8 - 1s - loss: 1.9064 - energy_loss: 0.4953 - force_loss: 0.0706 - 569ms/epoch - 71ms/step\n",
      "Epoch 1329/1500\n",
      "8/8 - 1s - loss: 2.0184 - energy_loss: 0.5366 - force_loss: 0.0741 - 555ms/epoch - 69ms/step\n",
      "Epoch 1330/1500\n",
      "8/8 - 1s - loss: 1.7325 - energy_loss: 0.2032 - force_loss: 0.0765 - 552ms/epoch - 69ms/step\n",
      "Epoch 1331/1500\n",
      "8/8 - 1s - loss: 1.8084 - energy_loss: 0.3060 - force_loss: 0.0751 - 561ms/epoch - 70ms/step\n",
      "Epoch 1332/1500\n",
      "8/8 - 1s - loss: 1.9649 - energy_loss: 0.4145 - force_loss: 0.0775 - 555ms/epoch - 69ms/step\n",
      "Epoch 1333/1500\n",
      "8/8 - 1s - loss: 1.7471 - energy_loss: 0.2308 - force_loss: 0.0758 - 564ms/epoch - 70ms/step\n",
      "Epoch 1334/1500\n",
      "8/8 - 1s - loss: 1.8575 - energy_loss: 0.2861 - force_loss: 0.0786 - 604ms/epoch - 76ms/step\n",
      "Epoch 1335/1500\n",
      "8/8 - 1s - loss: 1.5489 - energy_loss: 0.1508 - force_loss: 0.0699 - 592ms/epoch - 74ms/step\n",
      "Epoch 1336/1500\n",
      "8/8 - 1s - loss: 1.5171 - energy_loss: 0.2153 - force_loss: 0.0651 - 566ms/epoch - 71ms/step\n",
      "Epoch 1337/1500\n",
      "8/8 - 1s - loss: 1.9267 - energy_loss: 0.5114 - force_loss: 0.0708 - 555ms/epoch - 69ms/step\n",
      "Epoch 1338/1500\n",
      "8/8 - 1s - loss: 2.0011 - energy_loss: 0.5551 - force_loss: 0.0723 - 556ms/epoch - 69ms/step\n",
      "Epoch 1339/1500\n",
      "8/8 - 1s - loss: 1.9602 - energy_loss: 0.5046 - force_loss: 0.0728 - 550ms/epoch - 69ms/step\n",
      "Epoch 1340/1500\n",
      "8/8 - 1s - loss: 2.0389 - energy_loss: 0.4959 - force_loss: 0.0771 - 574ms/epoch - 72ms/step\n",
      "Epoch 1341/1500\n",
      "8/8 - 1s - loss: 1.7677 - energy_loss: 0.2068 - force_loss: 0.0780 - 548ms/epoch - 68ms/step\n",
      "Epoch 1342/1500\n",
      "8/8 - 1s - loss: 1.9272 - energy_loss: 0.3756 - force_loss: 0.0776 - 554ms/epoch - 69ms/step\n",
      "Epoch 1343/1500\n",
      "8/8 - 1s - loss: 1.8437 - energy_loss: 0.2634 - force_loss: 0.0790 - 551ms/epoch - 69ms/step\n",
      "Epoch 1344/1500\n",
      "8/8 - 1s - loss: 1.5960 - energy_loss: 0.1641 - force_loss: 0.0716 - 556ms/epoch - 70ms/step\n",
      "Epoch 1345/1500\n",
      "8/8 - 1s - loss: 1.5234 - energy_loss: 0.1488 - force_loss: 0.0687 - 552ms/epoch - 69ms/step\n",
      "Epoch 1346/1500\n",
      "8/8 - 1s - loss: 1.7121 - energy_loss: 0.2717 - force_loss: 0.0720 - 545ms/epoch - 68ms/step\n",
      "Epoch 1347/1500\n",
      "8/8 - 1s - loss: 1.5657 - energy_loss: 0.1385 - force_loss: 0.0714 - 552ms/epoch - 69ms/step\n",
      "Epoch 1348/1500\n",
      "8/8 - 1s - loss: 1.5822 - energy_loss: 0.1553 - force_loss: 0.0713 - 551ms/epoch - 69ms/step\n",
      "Epoch 1349/1500\n",
      "8/8 - 1s - loss: 1.6498 - energy_loss: 0.2428 - force_loss: 0.0704 - 559ms/epoch - 70ms/step\n",
      "Epoch 1350/1500\n",
      "8/8 - 1s - loss: 1.6609 - energy_loss: 0.1821 - force_loss: 0.0739 - 564ms/epoch - 70ms/step\n",
      "Epoch 1351/1500\n",
      "8/8 - 1s - loss: 1.5474 - energy_loss: 0.1568 - force_loss: 0.0695 - 566ms/epoch - 71ms/step\n",
      "Epoch 1352/1500\n",
      "8/8 - 1s - loss: 1.5891 - energy_loss: 0.2398 - force_loss: 0.0675 - 638ms/epoch - 80ms/step\n",
      "Epoch 1353/1500\n",
      "8/8 - 1s - loss: 1.5455 - energy_loss: 0.1689 - force_loss: 0.0688 - 723ms/epoch - 90ms/step\n",
      "Epoch 1354/1500\n",
      "8/8 - 1s - loss: 1.5816 - energy_loss: 0.2057 - force_loss: 0.0688 - 668ms/epoch - 83ms/step\n",
      "Epoch 1355/1500\n",
      "8/8 - 1s - loss: 1.7079 - energy_loss: 0.3165 - force_loss: 0.0696 - 595ms/epoch - 74ms/step\n",
      "Epoch 1356/1500\n",
      "8/8 - 1s - loss: 1.7344 - energy_loss: 0.3037 - force_loss: 0.0715 - 586ms/epoch - 73ms/step\n",
      "Epoch 1357/1500\n",
      "8/8 - 1s - loss: 1.7894 - energy_loss: 0.2999 - force_loss: 0.0745 - 586ms/epoch - 73ms/step\n",
      "Epoch 1358/1500\n",
      "8/8 - 1s - loss: 1.8854 - energy_loss: 0.3148 - force_loss: 0.0785 - 580ms/epoch - 72ms/step\n",
      "Epoch 1359/1500\n",
      "8/8 - 1s - loss: 2.1008 - energy_loss: 0.3338 - force_loss: 0.0884 - 576ms/epoch - 72ms/step\n",
      "Epoch 1360/1500\n",
      "8/8 - 1s - loss: 2.0157 - energy_loss: 0.3440 - force_loss: 0.0836 - 567ms/epoch - 71ms/step\n",
      "Epoch 1361/1500\n",
      "8/8 - 1s - loss: 1.9676 - energy_loss: 0.3260 - force_loss: 0.0821 - 575ms/epoch - 72ms/step\n",
      "Epoch 1362/1500\n",
      "8/8 - 1s - loss: 1.9627 - energy_loss: 0.3308 - force_loss: 0.0816 - 570ms/epoch - 71ms/step\n",
      "Epoch 1363/1500\n",
      "8/8 - 1s - loss: 1.8339 - energy_loss: 0.3027 - force_loss: 0.0766 - 581ms/epoch - 73ms/step\n",
      "Epoch 1364/1500\n",
      "8/8 - 1s - loss: 1.7370 - energy_loss: 0.2971 - force_loss: 0.0720 - 581ms/epoch - 73ms/step\n",
      "Epoch 1365/1500\n",
      "8/8 - 1s - loss: 1.7098 - energy_loss: 0.3056 - force_loss: 0.0702 - 579ms/epoch - 72ms/step\n",
      "Epoch 1366/1500\n",
      "8/8 - 1s - loss: 1.7968 - energy_loss: 0.3052 - force_loss: 0.0746 - 578ms/epoch - 72ms/step\n",
      "Epoch 1367/1500\n",
      "8/8 - 1s - loss: 1.8571 - energy_loss: 0.3102 - force_loss: 0.0773 - 582ms/epoch - 73ms/step\n",
      "Epoch 1368/1500\n",
      "8/8 - 1s - loss: 1.7911 - energy_loss: 0.3017 - force_loss: 0.0745 - 587ms/epoch - 73ms/step\n",
      "Epoch 1369/1500\n",
      "8/8 - 1s - loss: 1.7614 - energy_loss: 0.3122 - force_loss: 0.0725 - 579ms/epoch - 72ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1370/1500\n",
      "8/8 - 1s - loss: 1.7885 - energy_loss: 0.3029 - force_loss: 0.0743 - 584ms/epoch - 73ms/step\n",
      "Epoch 1371/1500\n",
      "8/8 - 1s - loss: 1.8967 - energy_loss: 0.3072 - force_loss: 0.0795 - 579ms/epoch - 72ms/step\n",
      "Epoch 1372/1500\n",
      "8/8 - 1s - loss: 1.7590 - energy_loss: 0.3019 - force_loss: 0.0729 - 668ms/epoch - 83ms/step\n",
      "Epoch 1373/1500\n",
      "8/8 - 1s - loss: 1.7160 - energy_loss: 0.3050 - force_loss: 0.0706 - 657ms/epoch - 82ms/step\n",
      "Epoch 1374/1500\n",
      "8/8 - 1s - loss: 1.7361 - energy_loss: 0.2934 - force_loss: 0.0721 - 631ms/epoch - 79ms/step\n",
      "Epoch 1375/1500\n",
      "8/8 - 1s - loss: 1.7108 - energy_loss: 0.3144 - force_loss: 0.0698 - 656ms/epoch - 82ms/step\n",
      "Epoch 1376/1500\n",
      "8/8 - 1s - loss: 1.7204 - energy_loss: 0.2977 - force_loss: 0.0711 - 671ms/epoch - 84ms/step\n",
      "Epoch 1377/1500\n",
      "8/8 - 1s - loss: 1.7563 - energy_loss: 0.2909 - force_loss: 0.0733 - 668ms/epoch - 84ms/step\n",
      "Epoch 1378/1500\n",
      "8/8 - 1s - loss: 1.6346 - energy_loss: 0.2272 - force_loss: 0.0704 - 677ms/epoch - 85ms/step\n",
      "Epoch 1379/1500\n",
      "8/8 - 1s - loss: 1.4787 - energy_loss: 0.1299 - force_loss: 0.0674 - 650ms/epoch - 81ms/step\n",
      "Epoch 1380/1500\n",
      "8/8 - 1s - loss: 1.4705 - energy_loss: 0.1207 - force_loss: 0.0675 - 650ms/epoch - 81ms/step\n",
      "Epoch 1381/1500\n",
      "8/8 - 1s - loss: 1.4323 - energy_loss: 0.1204 - force_loss: 0.0656 - 636ms/epoch - 80ms/step\n",
      "Epoch 1382/1500\n",
      "8/8 - 1s - loss: 1.4460 - energy_loss: 0.1421 - force_loss: 0.0652 - 693ms/epoch - 87ms/step\n",
      "Epoch 1383/1500\n",
      "8/8 - 1s - loss: 1.4287 - energy_loss: 0.1408 - force_loss: 0.0644 - 699ms/epoch - 87ms/step\n",
      "Epoch 1384/1500\n",
      "8/8 - 1s - loss: 1.4174 - energy_loss: 0.1463 - force_loss: 0.0636 - 668ms/epoch - 84ms/step\n",
      "Epoch 1385/1500\n",
      "8/8 - 1s - loss: 1.6143 - energy_loss: 0.3707 - force_loss: 0.0622 - 641ms/epoch - 80ms/step\n",
      "Epoch 1386/1500\n",
      "8/8 - 1s - loss: 1.5389 - energy_loss: 0.2857 - force_loss: 0.0627 - 635ms/epoch - 79ms/step\n",
      "Epoch 1387/1500\n",
      "8/8 - 1s - loss: 1.6808 - energy_loss: 0.3181 - force_loss: 0.0681 - 687ms/epoch - 86ms/step\n",
      "Epoch 1388/1500\n",
      "8/8 - 1s - loss: 1.7815 - energy_loss: 0.3039 - force_loss: 0.0739 - 678ms/epoch - 85ms/step\n",
      "Epoch 1389/1500\n",
      "8/8 - 1s - loss: 1.7883 - energy_loss: 0.2923 - force_loss: 0.0748 - 623ms/epoch - 78ms/step\n",
      "Epoch 1390/1500\n",
      "8/8 - 1s - loss: 1.9108 - energy_loss: 0.2961 - force_loss: 0.0807 - 667ms/epoch - 83ms/step\n",
      "Epoch 1391/1500\n",
      "8/8 - 1s - loss: 1.8718 - energy_loss: 0.3324 - force_loss: 0.0770 - 680ms/epoch - 85ms/step\n",
      "Epoch 1392/1500\n",
      "8/8 - 1s - loss: 1.8189 - energy_loss: 0.3300 - force_loss: 0.0744 - 644ms/epoch - 80ms/step\n",
      "Epoch 1393/1500\n",
      "8/8 - 1s - loss: 1.6770 - energy_loss: 0.2717 - force_loss: 0.0703 - 669ms/epoch - 84ms/step\n",
      "Epoch 1394/1500\n",
      "8/8 - 1s - loss: 1.4869 - energy_loss: 0.1313 - force_loss: 0.0678 - 664ms/epoch - 83ms/step\n",
      "Epoch 1395/1500\n",
      "8/8 - 1s - loss: 1.5617 - energy_loss: 0.2485 - force_loss: 0.0657 - 640ms/epoch - 80ms/step\n",
      "Epoch 1396/1500\n",
      "8/8 - 1s - loss: 1.5966 - energy_loss: 0.2666 - force_loss: 0.0665 - 657ms/epoch - 82ms/step\n",
      "Epoch 1397/1500\n",
      "8/8 - 1s - loss: 1.6588 - energy_loss: 0.3377 - force_loss: 0.0661 - 658ms/epoch - 82ms/step\n",
      "Epoch 1398/1500\n",
      "8/8 - 1s - loss: 1.5932 - energy_loss: 0.2837 - force_loss: 0.0655 - 649ms/epoch - 81ms/step\n",
      "Epoch 1399/1500\n",
      "8/8 - 1s - loss: 1.5111 - energy_loss: 0.1437 - force_loss: 0.0684 - 628ms/epoch - 79ms/step\n",
      "Epoch 1400/1500\n",
      "8/8 - 1s - loss: 1.6707 - energy_loss: 0.2957 - force_loss: 0.0687 - 647ms/epoch - 81ms/step\n",
      "Epoch 1401/1500\n",
      "8/8 - 1s - loss: 1.8243 - energy_loss: 0.3690 - force_loss: 0.0728 - 649ms/epoch - 81ms/step\n",
      "Epoch 1402/1500\n",
      "8/8 - 1s - loss: 1.6282 - energy_loss: 0.2592 - force_loss: 0.0685 - 639ms/epoch - 80ms/step\n",
      "Epoch 1403/1500\n",
      "8/8 - 1s - loss: 1.6128 - energy_loss: 0.2803 - force_loss: 0.0666 - 646ms/epoch - 81ms/step\n",
      "Epoch 1404/1500\n",
      "8/8 - 1s - loss: 1.6836 - energy_loss: 0.3221 - force_loss: 0.0681 - 648ms/epoch - 81ms/step\n",
      "Epoch 1405/1500\n",
      "8/8 - 1s - loss: 1.7359 - energy_loss: 0.2449 - force_loss: 0.0746 - 655ms/epoch - 82ms/step\n",
      "Epoch 1406/1500\n",
      "8/8 - 1s - loss: 1.7485 - energy_loss: 0.2895 - force_loss: 0.0729 - 673ms/epoch - 84ms/step\n",
      "Epoch 1407/1500\n",
      "8/8 - 1s - loss: 1.9484 - energy_loss: 0.3296 - force_loss: 0.0809 - 696ms/epoch - 87ms/step\n",
      "Epoch 1408/1500\n",
      "8/8 - 1s - loss: 1.8973 - energy_loss: 0.2833 - force_loss: 0.0807 - 658ms/epoch - 82ms/step\n",
      "Epoch 1409/1500\n",
      "8/8 - 1s - loss: 1.8089 - energy_loss: 0.2803 - force_loss: 0.0764 - 655ms/epoch - 82ms/step\n",
      "Epoch 1410/1500\n",
      "8/8 - 1s - loss: 1.7477 - energy_loss: 0.3284 - force_loss: 0.0710 - 638ms/epoch - 80ms/step\n",
      "Epoch 1411/1500\n",
      "8/8 - 1s - loss: 1.7153 - energy_loss: 0.2890 - force_loss: 0.0713 - 670ms/epoch - 84ms/step\n",
      "Epoch 1412/1500\n",
      "8/8 - 1s - loss: 1.6956 - energy_loss: 0.3010 - force_loss: 0.0697 - 686ms/epoch - 86ms/step\n",
      "Epoch 1413/1500\n",
      "8/8 - 1s - loss: 1.7019 - energy_loss: 0.3191 - force_loss: 0.0691 - 685ms/epoch - 86ms/step\n",
      "Epoch 1414/1500\n",
      "8/8 - 1s - loss: 1.8382 - energy_loss: 0.4760 - force_loss: 0.0681 - 671ms/epoch - 84ms/step\n",
      "Epoch 1415/1500\n",
      "8/8 - 1s - loss: 1.7993 - energy_loss: 0.4470 - force_loss: 0.0676 - 644ms/epoch - 80ms/step\n",
      "Epoch 1416/1500\n",
      "8/8 - 1s - loss: 1.7758 - energy_loss: 0.4756 - force_loss: 0.0650 - 658ms/epoch - 82ms/step\n",
      "Epoch 1417/1500\n",
      "8/8 - 1s - loss: 1.6865 - energy_loss: 0.2799 - force_loss: 0.0703 - 717ms/epoch - 90ms/step\n",
      "Epoch 1418/1500\n",
      "8/8 - 1s - loss: 1.6962 - energy_loss: 0.2431 - force_loss: 0.0727 - 648ms/epoch - 81ms/step\n",
      "Epoch 1419/1500\n",
      "8/8 - 1s - loss: 1.4673 - energy_loss: 0.1168 - force_loss: 0.0675 - 668ms/epoch - 83ms/step\n",
      "Epoch 1420/1500\n",
      "8/8 - 1s - loss: 1.5534 - energy_loss: 0.2731 - force_loss: 0.0640 - 656ms/epoch - 82ms/step\n",
      "Epoch 1421/1500\n",
      "8/8 - 1s - loss: 1.7662 - energy_loss: 0.4910 - force_loss: 0.0638 - 633ms/epoch - 79ms/step\n",
      "Epoch 1422/1500\n",
      "8/8 - 1s - loss: 1.8449 - energy_loss: 0.5511 - force_loss: 0.0647 - 648ms/epoch - 81ms/step\n",
      "Epoch 1423/1500\n",
      "8/8 - 1s - loss: 1.8635 - energy_loss: 0.5468 - force_loss: 0.0658 - 659ms/epoch - 82ms/step\n",
      "Epoch 1424/1500\n",
      "8/8 - 1s - loss: 1.8491 - energy_loss: 0.4840 - force_loss: 0.0683 - 675ms/epoch - 84ms/step\n",
      "Epoch 1425/1500\n",
      "8/8 - 1s - loss: 1.7180 - energy_loss: 0.2966 - force_loss: 0.0711 - 689ms/epoch - 86ms/step\n",
      "Epoch 1426/1500\n",
      "8/8 - 1s - loss: 1.5557 - energy_loss: 0.1430 - force_loss: 0.0706 - 692ms/epoch - 86ms/step\n",
      "Epoch 1427/1500\n",
      "8/8 - 1s - loss: 1.4310 - energy_loss: 0.1353 - force_loss: 0.0648 - 681ms/epoch - 85ms/step\n",
      "Epoch 1428/1500\n",
      "8/8 - 1s - loss: 1.4400 - energy_loss: 0.1277 - force_loss: 0.0656 - 687ms/epoch - 86ms/step\n",
      "Epoch 1429/1500\n",
      "8/8 - 1s - loss: 1.3928 - energy_loss: 0.1321 - force_loss: 0.0630 - 669ms/epoch - 84ms/step\n",
      "Epoch 1430/1500\n",
      "8/8 - 1s - loss: 1.5857 - energy_loss: 0.2777 - force_loss: 0.0654 - 642ms/epoch - 80ms/step\n",
      "Epoch 1431/1500\n",
      "8/8 - 1s - loss: 1.6403 - energy_loss: 0.2757 - force_loss: 0.0682 - 650ms/epoch - 81ms/step\n",
      "Epoch 1432/1500\n",
      "8/8 - 1s - loss: 1.7409 - energy_loss: 0.2782 - force_loss: 0.0731 - 645ms/epoch - 81ms/step\n",
      "Epoch 1433/1500\n",
      "8/8 - 1s - loss: 1.6728 - energy_loss: 0.3154 - force_loss: 0.0679 - 638ms/epoch - 80ms/step\n",
      "Epoch 1434/1500\n",
      "8/8 - 1s - loss: 1.5408 - energy_loss: 0.2457 - force_loss: 0.0648 - 634ms/epoch - 79ms/step\n",
      "Epoch 1435/1500\n",
      "8/8 - 1s - loss: 1.6620 - energy_loss: 0.2496 - force_loss: 0.0706 - 625ms/epoch - 78ms/step\n",
      "Epoch 1436/1500\n",
      "8/8 - 1s - loss: 1.7414 - energy_loss: 0.3205 - force_loss: 0.0710 - 652ms/epoch - 81ms/step\n",
      "Epoch 1437/1500\n",
      "8/8 - 1s - loss: 1.6350 - energy_loss: 0.1977 - force_loss: 0.0719 - 651ms/epoch - 81ms/step\n",
      "Epoch 1438/1500\n",
      "8/8 - 1s - loss: 1.5441 - energy_loss: 0.1384 - force_loss: 0.0703 - 670ms/epoch - 84ms/step\n",
      "Epoch 1439/1500\n",
      "8/8 - 1s - loss: 1.7597 - energy_loss: 0.3626 - force_loss: 0.0699 - 680ms/epoch - 85ms/step\n",
      "Epoch 1440/1500\n",
      "8/8 - 1s - loss: 1.8546 - energy_loss: 0.2919 - force_loss: 0.0781 - 678ms/epoch - 85ms/step\n",
      "Epoch 1441/1500\n",
      "8/8 - 1s - loss: 2.0568 - energy_loss: 0.5646 - force_loss: 0.0746 - 663ms/epoch - 83ms/step\n",
      "Epoch 1442/1500\n",
      "8/8 - 1s - loss: 1.7122 - energy_loss: 0.2826 - force_loss: 0.0715 - 788ms/epoch - 99ms/step\n",
      "Epoch 1443/1500\n",
      "8/8 - 1s - loss: 1.7196 - energy_loss: 0.2454 - force_loss: 0.0737 - 718ms/epoch - 90ms/step\n",
      "Epoch 1444/1500\n",
      "8/8 - 1s - loss: 2.0667 - energy_loss: 0.5886 - force_loss: 0.0739 - 683ms/epoch - 85ms/step\n",
      "Epoch 1445/1500\n",
      "8/8 - 1s - loss: 1.7022 - energy_loss: 0.3008 - force_loss: 0.0701 - 666ms/epoch - 83ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1446/1500\n",
      "8/8 - 1s - loss: 1.7595 - energy_loss: 0.3143 - force_loss: 0.0723 - 668ms/epoch - 83ms/step\n",
      "Epoch 1447/1500\n",
      "8/8 - 1s - loss: 1.7839 - energy_loss: 0.2968 - force_loss: 0.0744 - 637ms/epoch - 80ms/step\n",
      "Epoch 1448/1500\n",
      "8/8 - 1s - loss: 1.8501 - energy_loss: 0.3171 - force_loss: 0.0766 - 607ms/epoch - 76ms/step\n",
      "Epoch 1449/1500\n",
      "8/8 - 1s - loss: 1.8423 - energy_loss: 0.3111 - force_loss: 0.0766 - 599ms/epoch - 75ms/step\n",
      "Epoch 1450/1500\n",
      "8/8 - 1s - loss: 1.7719 - energy_loss: 0.3149 - force_loss: 0.0729 - 590ms/epoch - 74ms/step\n",
      "Epoch 1451/1500\n",
      "8/8 - 1s - loss: 1.6898 - energy_loss: 0.3050 - force_loss: 0.0692 - 605ms/epoch - 76ms/step\n",
      "Epoch 1452/1500\n",
      "8/8 - 1s - loss: 1.7285 - energy_loss: 0.3019 - force_loss: 0.0713 - 587ms/epoch - 73ms/step\n",
      "Epoch 1453/1500\n",
      "8/8 - 1s - loss: 1.7353 - energy_loss: 0.2921 - force_loss: 0.0722 - 631ms/epoch - 79ms/step\n",
      "Epoch 1454/1500\n",
      "8/8 - 1s - loss: 1.7460 - energy_loss: 0.2918 - force_loss: 0.0727 - 610ms/epoch - 76ms/step\n",
      "Epoch 1455/1500\n",
      "8/8 - 1s - loss: 1.7187 - energy_loss: 0.2989 - force_loss: 0.0710 - 634ms/epoch - 79ms/step\n",
      "Epoch 1456/1500\n",
      "8/8 - 1s - loss: 1.7916 - energy_loss: 0.2919 - force_loss: 0.0750 - 652ms/epoch - 81ms/step\n",
      "Epoch 1457/1500\n",
      "8/8 - 1s - loss: 1.7417 - energy_loss: 0.3003 - force_loss: 0.0721 - 631ms/epoch - 79ms/step\n",
      "Epoch 1458/1500\n",
      "8/8 - 1s - loss: 1.7099 - energy_loss: 0.2765 - force_loss: 0.0717 - 648ms/epoch - 81ms/step\n",
      "Epoch 1459/1500\n",
      "8/8 - 1s - loss: 1.7110 - energy_loss: 0.2656 - force_loss: 0.0723 - 606ms/epoch - 76ms/step\n",
      "Epoch 1460/1500\n",
      "8/8 - 1s - loss: 1.9387 - energy_loss: 0.5766 - force_loss: 0.0681 - 591ms/epoch - 74ms/step\n",
      "Epoch 1461/1500\n",
      "8/8 - 1s - loss: 1.8903 - energy_loss: 0.5047 - force_loss: 0.0693 - 583ms/epoch - 73ms/step\n",
      "Epoch 1462/1500\n",
      "8/8 - 1s - loss: 1.8582 - energy_loss: 0.5004 - force_loss: 0.0679 - 585ms/epoch - 73ms/step\n",
      "Epoch 1463/1500\n",
      "8/8 - 1s - loss: 1.8011 - energy_loss: 0.4982 - force_loss: 0.0651 - 583ms/epoch - 73ms/step\n",
      "Epoch 1464/1500\n",
      "8/8 - 1s - loss: 1.7966 - energy_loss: 0.4657 - force_loss: 0.0665 - 596ms/epoch - 75ms/step\n",
      "Epoch 1465/1500\n",
      "8/8 - 1s - loss: 1.8388 - energy_loss: 0.5297 - force_loss: 0.0655 - 597ms/epoch - 75ms/step\n",
      "Epoch 1466/1500\n",
      "8/8 - 1s - loss: 1.5447 - energy_loss: 0.1717 - force_loss: 0.0686 - 584ms/epoch - 73ms/step\n",
      "Epoch 1467/1500\n",
      "8/8 - 1s - loss: 1.3845 - energy_loss: 0.1101 - force_loss: 0.0637 - 605ms/epoch - 76ms/step\n",
      "Epoch 1468/1500\n",
      "8/8 - 1s - loss: 1.3904 - energy_loss: 0.1377 - force_loss: 0.0626 - 582ms/epoch - 73ms/step\n",
      "Epoch 1469/1500\n",
      "8/8 - 1s - loss: 1.3685 - energy_loss: 0.1459 - force_loss: 0.0611 - 584ms/epoch - 73ms/step\n",
      "Epoch 1470/1500\n",
      "8/8 - 1s - loss: 1.4077 - energy_loss: 0.1618 - force_loss: 0.0623 - 580ms/epoch - 72ms/step\n",
      "Epoch 1471/1500\n",
      "8/8 - 1s - loss: 1.3751 - energy_loss: 0.1512 - force_loss: 0.0612 - 589ms/epoch - 74ms/step\n",
      "Epoch 1472/1500\n",
      "8/8 - 1s - loss: 1.3620 - energy_loss: 0.1332 - force_loss: 0.0614 - 586ms/epoch - 73ms/step\n",
      "Epoch 1473/1500\n",
      "8/8 - 1s - loss: 1.3658 - energy_loss: 0.1460 - force_loss: 0.0610 - 583ms/epoch - 73ms/step\n",
      "Epoch 1474/1500\n",
      "8/8 - 1s - loss: 1.3466 - energy_loss: 0.1399 - force_loss: 0.0603 - 648ms/epoch - 81ms/step\n",
      "Epoch 1475/1500\n",
      "8/8 - 1s - loss: 1.6958 - energy_loss: 0.3982 - force_loss: 0.0649 - 599ms/epoch - 75ms/step\n",
      "Epoch 1476/1500\n",
      "8/8 - 1s - loss: 1.8292 - energy_loss: 0.5429 - force_loss: 0.0643 - 614ms/epoch - 77ms/step\n",
      "Epoch 1477/1500\n",
      "8/8 - 1s - loss: 1.6539 - energy_loss: 0.2435 - force_loss: 0.0705 - 602ms/epoch - 75ms/step\n",
      "Epoch 1478/1500\n",
      "8/8 - 1s - loss: 2.2165 - energy_loss: 0.5061 - force_loss: 0.0855 - 588ms/epoch - 73ms/step\n",
      "Epoch 1479/1500\n",
      "8/8 - 1s - loss: 2.2316 - energy_loss: 0.5006 - force_loss: 0.0865 - 594ms/epoch - 74ms/step\n",
      "Epoch 1480/1500\n",
      "8/8 - 1s - loss: 1.6919 - energy_loss: 0.1395 - force_loss: 0.0776 - 642ms/epoch - 80ms/step\n",
      "Epoch 1481/1500\n",
      "8/8 - 1s - loss: 1.5496 - energy_loss: 0.1370 - force_loss: 0.0706 - 590ms/epoch - 74ms/step\n",
      "Epoch 1482/1500\n",
      "8/8 - 1s - loss: 1.6186 - energy_loss: 0.2404 - force_loss: 0.0689 - 634ms/epoch - 79ms/step\n",
      "Epoch 1483/1500\n",
      "8/8 - 1s - loss: 1.6250 - energy_loss: 0.1576 - force_loss: 0.0734 - 589ms/epoch - 74ms/step\n",
      "Epoch 1484/1500\n",
      "8/8 - 1s - loss: 1.4499 - energy_loss: 0.1349 - force_loss: 0.0658 - 586ms/epoch - 73ms/step\n",
      "Epoch 1485/1500\n",
      "8/8 - 1s - loss: 1.4434 - energy_loss: 0.1549 - force_loss: 0.0644 - 615ms/epoch - 77ms/step\n",
      "Epoch 1486/1500\n",
      "8/8 - 1s - loss: 1.3931 - energy_loss: 0.1358 - force_loss: 0.0629 - 588ms/epoch - 74ms/step\n",
      "Epoch 1487/1500\n",
      "8/8 - 1s - loss: 1.5054 - energy_loss: 0.2412 - force_loss: 0.0632 - 584ms/epoch - 73ms/step\n",
      "Epoch 1488/1500\n",
      "8/8 - 1s - loss: 1.4749 - energy_loss: 0.1472 - force_loss: 0.0664 - 597ms/epoch - 75ms/step\n",
      "Epoch 1489/1500\n",
      "8/8 - 1s - loss: 1.4261 - energy_loss: 0.1522 - force_loss: 0.0637 - 592ms/epoch - 74ms/step\n",
      "Epoch 1490/1500\n",
      "8/8 - 1s - loss: 1.4459 - energy_loss: 0.1570 - force_loss: 0.0644 - 598ms/epoch - 75ms/step\n",
      "Epoch 1491/1500\n",
      "8/8 - 1s - loss: 1.3956 - energy_loss: 0.1552 - force_loss: 0.0620 - 591ms/epoch - 74ms/step\n",
      "Epoch 1492/1500\n",
      "8/8 - 1s - loss: 1.3533 - energy_loss: 0.1365 - force_loss: 0.0608 - 609ms/epoch - 76ms/step\n",
      "Epoch 1493/1500\n",
      "8/8 - 1s - loss: 1.3799 - energy_loss: 0.1502 - force_loss: 0.0615 - 592ms/epoch - 74ms/step\n",
      "Epoch 1494/1500\n",
      "8/8 - 1s - loss: 1.4150 - energy_loss: 0.1398 - force_loss: 0.0638 - 584ms/epoch - 73ms/step\n",
      "Epoch 1495/1500\n",
      "8/8 - 1s - loss: 1.5756 - energy_loss: 0.2479 - force_loss: 0.0664 - 577ms/epoch - 72ms/step\n",
      "Epoch 1496/1500\n",
      "8/8 - 1s - loss: 1.4159 - energy_loss: 0.1364 - force_loss: 0.0640 - 562ms/epoch - 70ms/step\n",
      "Epoch 1497/1500\n",
      "8/8 - 1s - loss: 1.4557 - energy_loss: 0.1473 - force_loss: 0.0654 - 571ms/epoch - 71ms/step\n",
      "Epoch 1498/1500\n",
      "8/8 - 1s - loss: 1.4696 - energy_loss: 0.1643 - force_loss: 0.0653 - 570ms/epoch - 71ms/step\n",
      "Epoch 1499/1500\n",
      "8/8 - 1s - loss: 1.7963 - energy_loss: 0.3854 - force_loss: 0.0705 - 568ms/epoch - 71ms/step\n",
      "Epoch 1500/1500\n",
      "8/8 - 1s - loss: 1.4900 - energy_loss: 0.1466 - force_loss: 0.0672 - 567ms/epoch - 71ms/step\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(\n",
    "    x_tensor, {\"energy\": eng, \"force\": forces},\n",
    "    shuffle=True,\n",
    "    batch_size=64,\n",
    "    epochs=1500,\n",
    "    verbose=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e312e998",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kgcnn.utils.plots import plot_train_test_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1370f095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_test_loss([hist]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d0a0440",
   "metadata": {},
   "source": [
    "## 3. `MolDynamicsModelPredictor` wrapping pre- and postprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "957e8025",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kgcnn.molecule.dynamics.base import MolDynamicsModelPredictor\n",
    "from kgcnn.graph.postprocessor import ExtensiveEnergyForceScalerPostprocessor\n",
    "from kgcnn.graph.preprocessor import SetRange, CountNodesAndEdges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a239c889",
   "metadata": {},
   "outputs": [],
   "source": [
    "dyn_model = MolDynamicsModelPredictor(\n",
    "    model=model, \n",
    "    model_inputs=model_config[\"model_energy\"][\"config\"][\"inputs\"], \n",
    "    model_outputs={\"energy\":\"energy\", \"forces\": \"force\"},\n",
    "    graph_preprocessors=[\n",
    "        SetRange(node_coordinates=\"node_coordinates\", overwrite=False), \n",
    "        CountNodesAndEdges(total_edges=\"total_ranges\", count_edges=\"range_indices\", count_nodes=\"atomic_number\", total_nodes=\"total_nodes\")\n",
    "    ],  # To use \n",
    "    graph_postprocessors=[\n",
    "        ExtensiveEnergyForceScalerPostprocessor(\n",
    "            scaler, energy=\"energy\", force=\"forces\", atomic_number=\"atomic_number\")],\n",
    "    store_last_input=True,\n",
    "    update_from_last_input=[\"range_indices\"],  # and \"range_images\" if periodic.\n",
    "    update_from_last_input_skip=3,\n",
    "    use_predict=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9f62e23f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'energy': array([-17469.83828173]),\n",
       " 'forces': array([[-2.670555  ,  2.6772392 , -0.9047204 ],\n",
       "        [-0.99556243, -0.8928257 ,  2.115237  ],\n",
       "        [ 3.0201063 , -2.1920564 , -0.10269466],\n",
       "        [-0.3934617 ,  1.6692059 , -0.10940111],\n",
       "        [-0.0665977 ,  1.8290963 ,  0.9220997 ],\n",
       "        [-0.6138771 ,  0.35973954, -0.12089539],\n",
       "        [ 0.07644939,  0.7710326 , -2.137013  ],\n",
       "        [-0.3606711 , -1.1926634 , -0.03478914],\n",
       "        [ 0.6117526 ,  3.666739  ,  1.8770413 ],\n",
       "        [-0.78135276,  1.9039568 ,  0.8194084 ],\n",
       "        [ 1.1729598 , -1.1987687 ,  0.53568983],\n",
       "        [ 0.52312624, -3.6745806 , -1.8835176 ],\n",
       "        [ 2.1295605 , -1.0608826 , -0.69878817],\n",
       "        [-0.2534858 , -0.50828743,  0.338701  ],\n",
       "        [ 1.2685527 , -1.9717028 ,  1.4492956 ],\n",
       "        [ 0.08581114,  0.35703838, -0.92711794],\n",
       "        [-1.0106573 ,  0.34707612, -0.18868059],\n",
       "        [-0.41618347,  0.49094236, -0.32614136],\n",
       "        [-0.28099245, -1.0225637 , -0.6689911 ],\n",
       "        [-0.503161  ,  0.32071835, -0.55113465],\n",
       "        [-0.54176235, -0.6784541 ,  0.59641206]], dtype=float32)}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dyn_model(dataset[0:510])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecd94305",
   "metadata": {},
   "source": [
    "## 4. Use ASE compatible `KgcnnSingleCalculator`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c78027e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ase import Atoms\n",
    "from kgcnn.molecule.dynamics.ase_calc import KgcnnSingleCalculator, AtomsToGraphConverter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ff3a525d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Atoms(symbols='C7O3C2OH8', pbc=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atoms = Atoms(dataset[0][\"atomic_number\"], positions=dataset[0][\"node_coordinates\"])\n",
    "atoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b86c71fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MemoryGraphList [{'atomic_number': array([6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 6, 6, 8, 1, 1, 1, 1, 1, 1, 1, 1]), 'node_coordinates': array([[ 2.15275078, -0.93790121, -0.05378575],\n",
       "       [ 0.99956719,  1.13262738, -1.67300307],\n",
       "       [ 2.73218273, -0.44859684, -1.19275553],\n",
       "       [ 2.14794307,  0.41880283, -2.08405233],\n",
       "       [-3.15705489,  1.42240939,  0.33067654],\n",
       "       [ 0.91168856, -0.33727827,  0.29772754],\n",
       "       [ 0.36105629,  0.72618343, -0.42339745],\n",
       "       [-0.40166094, -0.12259909,  2.26219435],\n",
       "       [-2.13128849, -0.48888369, -0.80224462],\n",
       "       [ 0.29867456, -2.24990948,  1.44246368],\n",
       "       [ 0.1239993 , -0.83296539,  1.42230211],\n",
       "       [-2.04596577,  0.64616435, -0.22107209],\n",
       "       [-0.88096468,  1.36265193, -0.06099633],\n",
       "       [-0.02472582, -2.46371902,  2.33402192],\n",
       "       [ 2.47956412, -1.70739289,  0.55101985],\n",
       "       [ 0.49126967,  1.99943374, -2.09234064],\n",
       "       [ 3.77819263, -0.85891833, -1.40897491],\n",
       "       [ 2.75656656,  0.58901616, -2.98423316],\n",
       "       [-2.82095412,  2.33964098,  0.93973904],\n",
       "       [-3.76033286,  1.76801371, -0.50670433],\n",
       "       [-3.80678333,  0.80577181,  0.93093152]])} ...]>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv=AtomsToGraphConverter({\"atomic_number\": \"get_atomic_numbers\", \"node_coordinates\": \"get_positions\"})\n",
    "conv(atoms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f886b267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<kgcnn.molecule.dynamics.ase_calc.KgcnnSingleCalculator at 0x202e46eb220>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc = KgcnnSingleCalculator(model_predictor=dyn_model, atoms_converter=conv)\n",
    "calc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5e323e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ase.md.velocitydistribution import MaxwellBoltzmannDistribution\n",
    "from ase.md.verlet import VelocityVerlet\n",
    "from ase import units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e0d59a45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'energy': array(-17469.83828191), 'forces': array([[-2.6705542 ,  2.6772387 , -0.9047214 ],\n",
      "       [-0.9955634 , -0.8928251 ,  2.1152375 ],\n",
      "       [ 3.0201075 , -2.192057  , -0.10269532],\n",
      "       [-0.39346123,  1.6692051 , -0.10939991],\n",
      "       [-0.06659603,  1.8290954 ,  0.92209923],\n",
      "       [-0.6138769 ,  0.35974014, -0.12089533],\n",
      "       [ 0.07644919,  0.77103245, -2.1370127 ],\n",
      "       [-0.36067158, -1.1926638 , -0.03478879],\n",
      "       [ 0.61175245,  3.6667366 ,  1.8770401 ],\n",
      "       [-0.7813537 ,  1.9039577 ,  0.8194096 ],\n",
      "       [ 1.1729604 , -1.1987686 ,  0.5356884 ],\n",
      "       [ 0.5231255 , -3.6745777 , -1.8835161 ],\n",
      "       [ 2.12956   , -1.0608817 , -0.69878805],\n",
      "       [-0.25348562, -0.50828815,  0.338701  ],\n",
      "       [ 1.2685529 , -1.9717036 ,  1.4492959 ],\n",
      "       [ 0.08581102,  0.35703826, -0.9271178 ],\n",
      "       [-1.0106578 ,  0.34707642, -0.1886805 ],\n",
      "       [-0.41618264,  0.49094242, -0.3261423 ],\n",
      "       [-0.28099155, -1.0225631 , -0.6689911 ],\n",
      "       [-0.5031617 ,  0.32071868, -0.55113506],\n",
      "       [-0.5417628 , -0.6784543 ,  0.5964118 ]], dtype=float32)}\n"
     ]
    }
   ],
   "source": [
    "atoms.calc = calc\n",
    "calc.calculate(atoms)\n",
    "print(calc.results)\n",
    "\n",
    "# Set the momenta corresponding to T=300K\n",
    "MaxwellBoltzmannDistribution(atoms, temperature_K=300)\n",
    "\n",
    "# We want to run MD with constant energy using the VelocityVerlet algorithm.\n",
    "dyn = VelocityVerlet(atoms, 1 * units.fs)  # 5 fs time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e9f42dc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy per atom: Epot = -831.897eV  Ekin = 0.052eV (T=399K)  Etot = -831.846eV\n",
      "Energy per atom: Epot = -831.892eV  Ekin = 0.051eV (T=392K)  Etot = -831.841eV\n",
      "Energy per atom: Epot = -831.918eV  Ekin = 0.074eV (T=569K)  Etot = -831.844eV\n",
      "Energy per atom: Epot = -831.913eV  Ekin = 0.068eV (T=524K)  Etot = -831.845eV\n",
      "Energy per atom: Epot = -831.896eV  Ekin = 0.056eV (T=437K)  Etot = -831.839eV\n",
      "Energy per atom: Epot = -831.908eV  Ekin = 0.072eV (T=560K)  Etot = -831.836eV\n",
      "Energy per atom: Epot = -831.908eV  Ekin = 0.068eV (T=525K)  Etot = -831.840eV\n",
      "Energy per atom: Epot = -831.914eV  Ekin = 0.070eV (T=541K)  Etot = -831.844eV\n",
      "Energy per atom: Epot = -831.904eV  Ekin = 0.067eV (T=518K)  Etot = -831.837eV\n",
      "Energy per atom: Epot = -831.900eV  Ekin = 0.063eV (T=491K)  Etot = -831.837eV\n",
      "Energy per atom: Epot = -831.901eV  Ekin = 0.057eV (T=442K)  Etot = -831.844eV\n",
      "Energy per atom: Epot = -831.906eV  Ekin = 0.063eV (T=489K)  Etot = -831.843eV\n",
      "Energy per atom: Epot = -831.903eV  Ekin = 0.065eV (T=505K)  Etot = -831.838eV\n",
      "Energy per atom: Epot = -831.903eV  Ekin = 0.067eV (T=516K)  Etot = -831.836eV\n",
      "Energy per atom: Epot = -831.893eV  Ekin = 0.057eV (T=443K)  Etot = -831.836eV\n",
      "Energy per atom: Epot = -831.903eV  Ekin = 0.065eV (T=499K)  Etot = -831.838eV\n",
      "Energy per atom: Epot = -831.911eV  Ekin = 0.068eV (T=525K)  Etot = -831.843eV\n",
      "Energy per atom: Epot = -831.903eV  Ekin = 0.057eV (T=442K)  Etot = -831.846eV\n",
      "Energy per atom: Epot = -831.908eV  Ekin = 0.064eV (T=492K)  Etot = -831.844eV\n",
      "Energy per atom: Epot = -831.911eV  Ekin = 0.069eV (T=532K)  Etot = -831.842eV\n",
      "Energy per atom: Epot = -831.903eV  Ekin = 0.068eV (T=527K)  Etot = -831.835eV\n"
     ]
    }
   ],
   "source": [
    "def printenergy(a):\n",
    "    \"\"\"Function to print the potential, kinetic and total energy\"\"\"\n",
    "    epot = a.get_potential_energy() / len(a)\n",
    "    ekin = a.get_kinetic_energy() / len(a)\n",
    "    print('Energy per atom: Epot = %.3feV  Ekin = %.3feV (T=%3.0fK)  '\n",
    "          'Etot = %.3feV' % (epot, ekin, ekin / (1.5 * units.kB), epot + ekin))\n",
    "\n",
    "\n",
    "# Now run the dynamics\n",
    "printenergy(atoms)\n",
    "for i in range(20):\n",
    "    dyn.run(10)\n",
    "    printenergy(atoms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "be5ef139",
   "metadata": {},
   "outputs": [],
   "source": [
    "calc.model_predictor._last_input;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aead5d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.018125"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dyn_model._test_timing(conv(atoms))"
   ]
  }
 ],
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