File size: 20,998 Bytes
b78a213 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SchNet S2EF training example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The purpose of this notebook is to demonstrate some of the basics of the Open Catalyst Project's (OCP) codebase and data. In this example, we will train a schnet model for predicting the energy and forces of a given structure (S2EF task). First, ensure you have installed the OCP ocp repo and all the dependencies according to the [README](https://github.com/Open-Catalyst-Project/ocp/blob/master/README.md)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Disclaimer: This notebook is for tutorial purposes, it is unlikely it will be practical to train baseline models on our larger datasets using this format. As a next step, we recommend trying the command line examples. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import os\n",
"from ocpmodels.trainers import ForcesTrainer\n",
"from ocpmodels import models\n",
"from ocpmodels.common import logger\n",
"from ocpmodels.common.utils import setup_logging\n",
"setup_logging()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"# a simple sanity check that a GPU is available\n",
"if torch.cuda.is_available():\n",
" print(\"True\")\n",
"else:\n",
" print(\"False\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The essential steps for training an OCP model\n",
"\n",
"1) Download data\n",
"\n",
"2) Preprocess data (if necessary)\n",
"\n",
"3) Define or load a configuration (config), which includes the following\n",
" \n",
" - task\n",
" - model\n",
" - optimizer\n",
" - dataset\n",
" - trainer\n",
"\n",
"4) Train\n",
"\n",
"5) Depending on the model/task there might be intermediate relaxation step\n",
"\n",
"6) Predict"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This examples uses the LMDB generated from the following [tutorial](http://laikapack.cheme.cmu.edu/notebook/open-catalyst-project/mshuaibi/notebooks/projects/ocp/docs/source/tutorials/lmdb_dataset_creation.ipynb). Please run that notebook before moving on. Alternatively, if you have other LMDBs available you may specify that instead."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# set the path to your local lmdb directory\n",
"train_src = \"s2ef\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define config"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this example, we will explicitly define the config; however, a set of default config files exists in the config folder of this repository. Default config yaml files can easily be loaded with the `build_config` util (found in `ocp/ocpmodels/common/utils.py`). Loading a yaml config is preferrable when launching jobs from the command line. We have included our best models' config files [here](https://github.com/Open-Catalyst-Project/ocp/tree/master/configs/s2ef)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Task** "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"task = {\n",
" 'dataset': 'trajectory_lmdb', # dataset used for the S2EF task\n",
" 'description': 'Regressing to energies and forces for DFT trajectories from OCP',\n",
" 'type': 'regression',\n",
" 'metric': 'mae',\n",
" 'labels': ['potential energy'],\n",
" 'grad_input': 'atomic forces',\n",
" 'train_on_free_atoms': True,\n",
" 'eval_on_free_atoms': True\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Model** - SchNet for this example"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"model = {\n",
" 'name': 'schnet',\n",
" 'hidden_channels': 1024, # if training is too slow for example purposes reduce the number of hidden channels\n",
" 'num_filters': 256,\n",
" 'num_interactions': 3,\n",
" 'num_gaussians': 200,\n",
" 'cutoff': 6.0\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Optimizer**"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"optimizer = {\n",
" 'batch_size': 16, # if hitting GPU memory issues, lower this\n",
" 'eval_batch_size': 8,\n",
" 'num_workers': 8,\n",
" 'lr_initial': 0.0001,\n",
" 'scheduler': \"ReduceLROnPlateau\",\n",
" 'mode': \"min\",\n",
" 'factor': 0.8,\n",
" 'patience': 3,\n",
" 'max_epochs': 80,\n",
" 'max_epochs': 1, # used for demonstration purposes\n",
" 'force_coefficient': 100,\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Dataset**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For simplicity, `train_src` is used for all the train/val/test sets. Feel free to update with the actual S2EF val and test sets, but it does require additional downloads and preprocessing. If you desire to normalize your targets, `normalize_labels` must be set to `True` and corresponding `mean` and `stds` need to be specified. These values have been precomputed for you and can be found in any of the [`base.yml`](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/s2ef/20M/base.yml#L5-L9) config files."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"dataset = [\n",
"{'src': train_src, 'normalize_labels': False}, # train set \n",
"{'src': train_src}, # val set (optional)\n",
"{'src': train_src} # test set (optional - writes predictions to disk)\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Trainer**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the `ForcesTrainer` for the S2EF and IS2RS tasks, and the `EnergyTrainer` for the IS2RE task "
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"amp: false\n",
"cmd:\n",
" checkpoint_dir: ./checkpoints/2021-09-04-08-51-28-SchNet-example\n",
" commit: 98a06d8\n",
" identifier: SchNet-example\n",
" logs_dir: ./logs/tensorboard/2021-09-04-08-51-28-SchNet-example\n",
" print_every: 5\n",
" results_dir: ./results/2021-09-04-08-51-28-SchNet-example\n",
" seed: 0\n",
" timestamp_id: 2021-09-04-08-51-28-SchNet-example\n",
"dataset:\n",
" normalize_labels: false\n",
" src: s2ef\n",
"gpus: 1\n",
"logger: tensorboard\n",
"model: schnet\n",
"model_attributes:\n",
" cutoff: 6.0\n",
" hidden_channels: 1024\n",
" num_filters: 256\n",
" num_gaussians: 200\n",
" num_interactions: 3\n",
"optim:\n",
" batch_size: 16\n",
" eval_batch_size: 8\n",
" factor: 0.8\n",
" force_coefficient: 100\n",
" lr_initial: 0.0001\n",
" max_epochs: 1\n",
" mode: min\n",
" num_workers: 8\n",
" patience: 3\n",
" scheduler: ReduceLROnPlateau\n",
"slurm: {}\n",
"task:\n",
" dataset: trajectory_lmdb\n",
" description: Regressing to energies and forces for DFT trajectories from OCP\n",
" eval_on_free_atoms: true\n",
" grad_input: atomic forces\n",
" labels:\n",
" - potential energy\n",
" metric: mae\n",
" train_on_free_atoms: true\n",
" type: regression\n",
"test_dataset:\n",
" src: s2ef\n",
"val_dataset:\n",
" src: s2ef\n",
"\n",
"2021-09-04 08:51:37 (INFO): Loading dataset: trajectory_lmdb\n",
"2021-09-04 08:51:37 (INFO): Loading model: schnet\n",
"2021-09-04 08:51:37 (INFO): Loaded SchNet with 5704193 parameters.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-09-04 08:51:37 (WARNING): Model gradient logging to tensorboard not yet supported.\n"
]
}
],
"source": [
"trainer = ForcesTrainer(\n",
" task=task,\n",
" model=model,\n",
" dataset=dataset,\n",
" optimizer=optimizer,\n",
" identifier=\"SchNet-example\",\n",
" run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n",
" is_debug=False, # if True, do not save checkpoint, logs, or results\n",
" is_vis=False,\n",
" print_every=5,\n",
" seed=0, # random seed to use\n",
" logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n",
" local_rank=0,\n",
" amp=False, # use PyTorch Automatic Mixed Precision (faster training and less memory usage)\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check the model"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OCPDataParallel(\n",
" (module): SchNet(hidden_channels=1024, num_filters=256, num_interactions=3, num_gaussians=200, cutoff=6.0)\n",
")\n"
]
}
],
"source": [
"print(trainer.model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-09-04 08:51:43 (INFO): forcesx_mae: 6.12e-01, forcesy_mae: 7.54e-01, forcesz_mae: 7.98e-01, forces_mae: 7.21e-01, forces_cos: -8.32e-03, forces_magnitude: 1.34e+00, energy_mae: 3.14e+01, energy_force_within_threshold: 0.00e+00, loss: 1.04e+02, lr: 1.00e-04, epoch: 1.25e-01, step: 5.00e+00\n",
"2021-09-04 08:51:43 (INFO): forcesx_mae: 4.95e-01, forcesy_mae: 5.85e-01, forcesz_mae: 6.06e-01, forces_mae: 5.62e-01, forces_cos: -1.64e-03, forces_magnitude: 9.97e-01, energy_mae: 2.38e+01, energy_force_within_threshold: 0.00e+00, loss: 8.02e+01, lr: 1.00e-04, epoch: 2.50e-01, step: 1.00e+01\n",
"2021-09-04 08:51:44 (INFO): forcesx_mae: 4.35e-01, forcesy_mae: 5.44e-01, forcesz_mae: 5.30e-01, forces_mae: 5.03e-01, forces_cos: 2.57e-02, forces_magnitude: 9.14e-01, energy_mae: 2.09e+01, energy_force_within_threshold: 0.00e+00, loss: 7.11e+01, lr: 1.00e-04, epoch: 3.75e-01, step: 1.50e+01\n",
"2021-09-04 08:51:44 (INFO): forcesx_mae: 3.70e-01, forcesy_mae: 4.50e-01, forcesz_mae: 4.22e-01, forces_mae: 4.14e-01, forces_cos: 3.03e-03, forces_magnitude: 7.05e-01, energy_mae: 1.66e+01, energy_force_within_threshold: 0.00e+00, loss: 5.83e+01, lr: 1.00e-04, epoch: 5.00e-01, step: 2.00e+01\n",
"2021-09-04 08:51:45 (INFO): forcesx_mae: 3.61e-01, forcesy_mae: 4.58e-01, forcesz_mae: 4.42e-01, forces_mae: 4.20e-01, forces_cos: 3.09e-02, forces_magnitude: 7.07e-01, energy_mae: 1.40e+01, energy_force_within_threshold: 0.00e+00, loss: 5.58e+01, lr: 1.00e-04, epoch: 6.25e-01, step: 2.50e+01\n",
"2021-09-04 08:51:45 (INFO): forcesx_mae: 3.51e-01, forcesy_mae: 3.96e-01, forcesz_mae: 3.91e-01, forces_mae: 3.79e-01, forces_cos: 2.94e-02, forces_magnitude: 6.65e-01, energy_mae: 1.39e+01, energy_force_within_threshold: 0.00e+00, loss: 5.19e+01, lr: 1.00e-04, epoch: 7.50e-01, step: 3.00e+01\n",
"2021-09-04 08:51:46 (INFO): forcesx_mae: 3.13e-01, forcesy_mae: 3.46e-01, forcesz_mae: 3.38e-01, forces_mae: 3.32e-01, forces_cos: 2.50e-02, forces_magnitude: 5.61e-01, energy_mae: 9.40e+00, energy_force_within_threshold: 0.00e+00, loss: 4.23e+01, lr: 1.00e-04, epoch: 8.75e-01, step: 3.50e+01\n",
"2021-09-04 08:51:46 (INFO): forcesx_mae: 3.06e-01, forcesy_mae: 3.59e-01, forcesz_mae: 3.59e-01, forces_mae: 3.41e-01, forces_cos: 1.31e-02, forces_magnitude: 5.62e-01, energy_mae: 1.02e+01, energy_force_within_threshold: 0.00e+00, loss: 4.91e+01, lr: 1.00e-04, epoch: 1.00e+00, step: 4.00e+01\n",
"2021-09-04 08:51:46 (INFO): Evaluating on val.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"device 0: 100%|██████████| 79/79 [00:01<00:00, 39.87it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-09-04 08:51:48 (INFO): forcesx_mae: 0.2778, forcesy_mae: 0.3467, forcesz_mae: 0.3606, forces_mae: 0.3284, forces_cos: 0.0278, forces_magnitude: 0.5615, energy_mae: 12.4560, energy_force_within_threshold: 0.0000, loss: 44.8795, epoch: 1.0000\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-09-04 08:51:49 (INFO): Predicting on test.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"device 0: 100%|██████████| 79/79 [00:01<00:00, 41.47it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-09-04 08:51:51 (INFO): Writing results to ./results/2021-09-04-08-51-28-SchNet-example/s2ef_predictions.npz\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"trainer.train()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Checkpoint\n",
"Once training has completed a `Trainer` class, by default, is loaded with the best checkpoint as determined by training or validation (if available) metrics. To load a `Trainer` class directly with a pretrained model, specify the `checkpoint_path` as defined by your previously trained model (`checkpoint_dir`):"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'./checkpoints/2021-09-04-08-51-28-SchNet-example/checkpoint.pt'"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"checkpoint_path = os.path.join(trainer.config[\"cmd\"][\"checkpoint_dir\"], \"checkpoint.pt\")\n",
"checkpoint_path"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"amp: false\n",
"cmd:\n",
" checkpoint_dir: ./checkpoints/2021-09-04-08-51-28-SchNet-example\n",
" commit: 98a06d8\n",
" identifier: SchNet-example\n",
" logs_dir: ./logs/tensorboard/2021-09-04-08-51-28-SchNet-example\n",
" print_every: 10\n",
" results_dir: ./results/2021-09-04-08-51-28-SchNet-example\n",
" seed: 0\n",
" timestamp_id: 2021-09-04-08-51-28-SchNet-example\n",
"dataset:\n",
" normalize_labels: false\n",
" src: s2ef\n",
"gpus: 1\n",
"logger: tensorboard\n",
"model: schnet\n",
"model_attributes:\n",
" cutoff: 6.0\n",
" hidden_channels: 1024\n",
" num_filters: 256\n",
" num_gaussians: 200\n",
" num_interactions: 3\n",
"optim:\n",
" batch_size: 16\n",
" eval_batch_size: 8\n",
" factor: 0.8\n",
" force_coefficient: 100\n",
" lr_initial: 0.0001\n",
" max_epochs: 1\n",
" mode: min\n",
" num_workers: 8\n",
" patience: 3\n",
" scheduler: ReduceLROnPlateau\n",
"slurm: {}\n",
"task:\n",
" dataset: trajectory_lmdb\n",
" description: Regressing to energies and forces for DFT trajectories from OCP\n",
" eval_on_free_atoms: true\n",
" grad_input: atomic forces\n",
" labels:\n",
" - potential energy\n",
" metric: mae\n",
" train_on_free_atoms: true\n",
" type: regression\n",
"test_dataset:\n",
" src: s2ef\n",
"val_dataset:\n",
" src: s2ef\n",
"\n",
"2021-09-04 08:51:51 (INFO): Loading dataset: trajectory_lmdb\n",
"2021-09-04 08:51:51 (INFO): Loading model: schnet\n",
"2021-09-04 08:51:51 (INFO): Loaded SchNet with 5704193 parameters.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-09-04 08:51:51 (WARNING): Model gradient logging to tensorboard not yet supported.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-09-04 08:51:51 (INFO): Loading checkpoint from: ./checkpoints/2021-09-04-08-51-28-SchNet-example/checkpoint.pt\n"
]
}
],
"source": [
"model = {\n",
" 'name': 'schnet',\n",
" 'hidden_channels': 1024, # if training is too slow for example purposes reduce the number of hidden channels\n",
" 'num_filters': 256,\n",
" 'num_interactions': 3,\n",
" 'num_gaussians': 200,\n",
" 'cutoff': 6.0\n",
"}\n",
"\n",
"pretrained_trainer = ForcesTrainer(\n",
" task=task,\n",
" model=model,\n",
" dataset=dataset,\n",
" optimizer=optimizer,\n",
" identifier=\"SchNet-example\",\n",
" run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n",
" is_debug=False, # if True, do not save checkpoint, logs, or results\n",
" is_vis=False,\n",
" print_every=10,\n",
" seed=0, # random seed to use\n",
" logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n",
" local_rank=0,\n",
" amp=False, # use PyTorch Automatic Mixed Precision (faster training and less memory usage)\n",
")\n",
"\n",
"pretrained_trainer.load_checkpoint(checkpoint_path=checkpoint_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predict"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If a test has been provided in your config, predictions are generated and written to disk automatically upon training completion. Otherwise, to make predictions on unseen data a `torch.utils.data` DataLoader object must be constructed. Here we reference our test set to make predictions on. Predictions are saved in `{results_file}.npz` in your `results_dir`."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-09-04 08:51:51 (INFO): Predicting on test.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"device 0: 100%|██████████| 79/79 [00:01<00:00, 44.68it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-09-04 08:51:53 (INFO): Writing results to ./results/2021-09-04-08-51-28-SchNet-example/s2ef_s2ef_results.npz\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# make predictions on the existing test_loader\n",
"predictions = pretrained_trainer.predict(pretrained_trainer.test_loader, results_file=\"s2ef_results\", disable_tqdm=False)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"energies = predictions[\"energy\"]\n",
"forces = predictions[\"forces\"]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "ocp-models",
"language": "python",
"name": "ocp-models"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|