| Property | Value |
|---|---|
| Size | 932 NEB relaxation trajectories |
| Reaction Types | Desorptions, Dissociations, Transfers |
| Purpose | Transition state energy calculations |
| Paper | CatTSunami (arXiv) |
| License | CC-BY-4.0 |
Overview¶
This is a validation dataset which was used to assess model performance in CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks. It is comprised of 932 NEB relaxation trajectories. There are three different types of reactions represented: desorptions, dissociations, and transfers. NEB calculations allow us to find transition states. The rate of reaction is determined by the transition state energy, so access to transition states is very important for catalysis research. For more information, check out the paper.
File Structure and Contents¶
The tar file contains 3 subdirectories: dissociations, desorptions, and transfers. As the names imply, these directories contain the converged DFT trajectories for each of the reaction classes. Within these directories, the trajectories are named to identify the contents of the file. Here is an example and the anatomy of the name:
desorption_id_83_2409_9_111-4_neb1.0.traj
desorptionindicates the reaction type (dissociation and transfer are the other possibilities)ididentifies that the material belongs to the validation in domain split (ood - out of domain is th e other possibility)83is the task id. This does not provide relavent information2409is the bulk index of the bulk used in the ocdata bulk pickle file9is the reaction index. for each reaction type there is a reaction pickle file in the repository. In this case it is the 9th entry to that pickle file111-4the first 3 numbers are the miller indices (i.e. the (1,1,1) surface), and the last number cooresponds to the shift value. In this case the 4th shift enumerated was the one used.neb1.0the number here indicates the k value used. For the full dataset, 1.0 was used so this does not distiguish any of the trajectories from one another.
The content of these trajectory files is the repeating frame sets. Despite the initial and final frames not being optimized during the NEB, the initial and final frames are saved for every iteration in the trajectory. For the dataset, 10 frames were used - 8 which were optimized over the neb. So the length of the trajectory is the number of iterations (N) * 10. If you wanted to look at the frame set prior to optimization and the optimized frame set, you could get them like this:
from __future__ import annotations
!wget https://dl.fbaipublicfiles.com/opencatalystproject/data/large_files/desorption_id_83_2409_9_111-4_neb1.0.traj
from ase.io import read
traj = read("desorption_id_83_2409_9_111-4_neb1.0.traj", ":")
unrelaxed_frames = traj[0:10]
relaxed_frames = traj[-10:]--2026-01-30 18:57:12-- https://dl.fbaipublicfiles.com/opencatalystproject/data/large_files/desorption_id_83_2409_9_111-4_neb1.0.traj
Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 3.171.22.33, 3.171.22.68, 3.171.22.13, ...
Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|3.171.22.33|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 10074935 (9.6M) [binary/octet-stream]
Saving to: ‘desorption_id_83_2409_9_111-4_neb1.0.traj’
desorptio 0%[ ] 0 --.-KB/s desorption 7%[> ] 712.49K 3.39MB/s desorption_id_83_24 100%[===================>] 9.61M 26.0MB/s in 0.4s
2026-01-30 18:57:13 (26.0 MB/s) - ‘desorption_id_83_2409_9_111-4_neb1.0.traj’ saved [10074935/10074935]
Download¶
| Splits | Size of compressed version (in bytes) | Size of uncompressed version (in bytes) | MD5 checksum (download link) |
|---|---|---|---|
| ASE Trajectories | 1.5G | 6.3G | 52af34a93758c82fae951e52af445089 |
Use¶
One more note: We have not prepared an lmdb for this dataset. This is because it is NEB calculations are not supported directly in ocp. You must use the ase native OCP class along with ase infrastructure to run NEB calculations. Here is an example of a use:
import os
from ase.io import read
from ase.mep import DyNEB
from ase.optimize import BFGS
from fairchem.core import FAIRChemCalculator, pretrained_mlip
traj = read("desorption_id_83_2409_9_111-4_neb1.0.traj", ":")
images = traj[0:10]
predictor = pretrained_mlip.get_predict_unit("uma-s-1p1")
neb = DyNEB(images, k=1)
for image in images:
image.calc = FAIRChemCalculator(predictor, task_name="oc20")
optimizer = BFGS(
neb,
trajectory="neb.traj",
)
# Use a small number of steps here to keep the docs fast during CI, but otherwise do quite reasonable settings.
fast_docs = os.environ.get("FAST_DOCS", "false").lower() == "true"
if fast_docs:
optimization_steps = 20
else:
optimization_steps = 300
conv = optimizer.run(fmax=0.45, steps=optimization_steps)
if conv:
neb.climb = True
conv = optimizer.run(fmax=0.05, steps=optimization_steps)WARNING:root:device was not explicitly set, using device='cuda'.
Step Time Energy fmax
BFGS: 0 18:57:28 -305.763010 5.169707
BFGS: 1 18:57:33 -305.691696 11.366597
BFGS: 2 18:57:39 -305.916311 1.889962
BFGS: 3 18:57:43 -305.932501 2.616029
BFGS: 4 18:57:49 -306.010363 2.264339
BFGS: 5 18:57:53 -306.003696 6.891948
BFGS: 6 18:57:57 -306.254764 9.616733
BFGS: 7 18:58:01 -306.224735 3.371253
BFGS: 8 18:58:05 -306.290789 4.665265
BFGS: 9 18:58:09 -306.315130 0.727092
BFGS: 10 18:58:12 -306.329407 0.653804
BFGS: 11 18:58:16 -306.357723 1.619377
BFGS: 12 18:58:20 -306.412168 1.940356
BFGS: 13 18:58:23 -306.441252 0.604861
BFGS: 14 18:58:31 -306.470993 0.558965
BFGS: 15 18:58:36 -306.495172 2.156344
BFGS: 16 18:58:39 -306.497854 0.480806
BFGS: 17 18:58:45 -306.504589 0.518924
BFGS: 18 18:58:50 -306.511314 0.713229
BFGS: 19 18:58:57 -306.508516 0.831336
BFGS: 20 18:59:03 -306.477697 1.212268
BFGS: 21 18:59:09 -306.508837 0.552398
BFGS: 22 18:59:12 -306.509834 0.379673
BFGS: 23 18:59:19 -306.396742 2.995822
BFGS: 24 18:59:25 -306.425842 1.008549
BFGS: 25 18:59:29 -306.389904 0.993089
BFGS: 26 18:59:36 -306.185396 0.928763
BFGS: 27 18:59:39 -306.127857 0.638398
BFGS: 28 18:59:46 -306.158338 0.667671
BFGS: 29 18:59:52 -306.240490 0.422851
BFGS: 30 19:00:00 -306.258444 0.529225
BFGS: 31 19:00:07 -306.258255 0.609599
BFGS: 32 19:00:12 -306.250885 0.649585
BFGS: 33 19:00:15 -306.258137 0.537615
BFGS: 34 19:00:18 -306.274427 0.437579
BFGS: 35 19:00:22 -306.311596 0.513768
BFGS: 36 19:00:26 -306.361137 0.542922
BFGS: 37 19:00:28 -306.432720 0.513911
BFGS: 38 19:00:34 -306.503166 0.486000
BFGS: 39 19:00:38 -306.530468 0.799239
BFGS: 40 19:00:43 -306.459040 1.426954
BFGS: 41 19:00:47 -306.300882 1.015518
BFGS: 42 19:00:51 -306.235903 0.797395
BFGS: 43 19:00:56 -306.259470 0.390577
BFGS: 44 19:01:00 -306.288023 0.344454
BFGS: 45 19:01:04 -306.316154 0.402697
BFGS: 46 19:01:08 -306.327059 0.520626
BFGS: 47 19:01:12 -306.309139 0.559481
BFGS: 48 19:01:17 -306.284309 0.416815
BFGS: 49 19:01:24 -306.274357 0.484169
BFGS: 50 19:01:29 -306.273907 0.298622
BFGS: 51 19:01:32 -306.276178 0.365475
BFGS: 52 19:01:39 -306.291763 0.299509
BFGS: 53 19:01:42 -306.317566 0.354821
BFGS: 54 19:01:49 -306.317499 0.360801
BFGS: 55 19:01:54 -306.310024 0.306526
BFGS: 56 19:02:00 -306.314321 0.267716
BFGS: 57 19:02:04 -306.322050 0.279927
BFGS: 58 19:02:08 -306.325403 0.250961
BFGS: 59 19:02:12 -306.331470 0.286567
BFGS: 60 19:02:19 -306.336172 0.243742
BFGS: 61 19:02:24 -306.333271 0.273785
BFGS: 62 19:02:28 -306.335901 0.221006
BFGS: 63 19:02:35 -306.344732 0.201176
BFGS: 64 19:02:42 -306.352837 0.204118
BFGS: 65 19:02:46 -306.349916 0.257516
BFGS: 66 19:02:51 -306.346220 0.259248
BFGS: 67 19:02:58 -306.365108 0.218455
BFGS: 68 19:03:02 -306.370716 0.381275
BFGS: 69 19:03:07 -306.328445 0.419848
BFGS: 70 19:03:12 -306.342266 0.197944
BFGS: 71 19:03:16 -306.350796 0.242940
BFGS: 72 19:03:20 -306.352631 0.132509
BFGS: 73 19:03:26 -306.351557 0.114112
BFGS: 74 19:03:29 -306.352007 0.084409
BFGS: 75 19:03:33 -306.356285 0.248511
BFGS: 76 19:03:39 -306.353203 0.196804
BFGS: 77 19:03:44 -306.294933 0.500913
BFGS: 78 19:03:49 -306.346065 0.198650
BFGS: 79 19:03:54 -306.355627 0.154790
BFGS: 80 19:03:57 -306.357239 0.094310
BFGS: 81 19:04:01 -306.356544 0.249530
BFGS: 82 19:04:06 -306.335291 0.669413
BFGS: 83 19:04:12 -306.293110 0.648524
BFGS: 84 19:04:17 -306.236439 0.525035
BFGS: 85 19:04:22 -306.267626 0.480170
BFGS: 86 19:04:27 -306.342382 1.060074
BFGS: 87 19:04:32 -306.344854 0.766333
BFGS: 88 19:04:37 -306.361555 0.637087
BFGS: 89 19:04:42 -306.347484 0.296541
BFGS: 90 19:04:47 -306.341362 0.298310
BFGS: 91 19:04:52 -306.346008 0.224120
BFGS: 92 19:04:56 -306.352196 0.129369
BFGS: 93 19:05:00 -306.350225 0.177740
BFGS: 94 19:05:05 -306.342773 0.153202
BFGS: 95 19:05:11 -306.345606 0.597417
BFGS: 96 19:05:15 -306.361929 0.740547
BFGS: 97 19:05:21 -306.358423 0.243415
BFGS: 98 19:05:26 -306.357473 0.095052
BFGS: 99 19:05:33 -306.350967 0.278700
BFGS: 100 19:05:38 -306.306963 0.584749
BFGS: 101 19:05:44 -306.332355 0.368485
BFGS: 102 19:05:51 -306.356363 0.125553
BFGS: 103 19:05:54 -306.357454 0.116692
BFGS: 104 19:06:01 -306.356043 0.169835
BFGS: 105 19:06:06 -306.343582 0.508455
BFGS: 106 19:06:10 -306.328489 0.596810
BFGS: 107 19:06:14 -306.332031 0.350819
BFGS: 108 19:06:24 -306.360620 0.231158
BFGS: 109 19:06:29 -306.366381 0.234090
BFGS: 110 19:06:36 -306.373091 0.499393
BFGS: 111 19:06:40 -306.385425 0.845078
BFGS: 112 19:06:44 -306.404396 1.029021
BFGS: 113 19:06:50 -306.445827 0.966109
BFGS: 114 19:06:57 -306.412006 0.735945
BFGS: 115 19:07:02 -306.339586 0.396256
BFGS: 116 19:07:07 -306.335665 0.412524
BFGS: 117 19:07:12 -306.352674 0.434160
BFGS: 118 19:07:17 -306.349146 0.563454
BFGS: 119 19:07:22 -306.344322 0.368040
BFGS: 120 19:07:27 -306.398643 0.622056
BFGS: 121 19:07:32 -306.396582 0.653353
BFGS: 122 19:07:36 -306.351295 0.583026
BFGS: 123 19:07:41 -306.351694 0.221725
BFGS: 124 19:07:46 -306.357431 0.383030
BFGS: 125 19:07:52 -306.352901 0.323137
BFGS: 126 19:07:58 -306.356089 0.265452
BFGS: 127 19:08:05 -306.379503 0.526372
BFGS: 128 19:08:11 -306.371759 0.563880
BFGS: 129 19:08:18 -306.343223 0.387097
BFGS: 130 19:08:24 -306.356369 0.151208
BFGS: 131 19:08:28 -306.357864 0.323726
BFGS: 132 19:08:35 -306.362793 0.142574
BFGS: 133 19:08:41 -306.366125 0.294580
BFGS: 134 19:08:48 -306.361322 0.349417
BFGS: 135 19:08:53 -306.357614 0.371054
BFGS: 136 19:08:58 -306.361660 0.094932
BFGS: 137 19:09:04 -306.362442 0.105962
BFGS: 138 19:09:09 -306.362442 0.196555
BFGS: 139 19:09:12 -306.362442 0.091508
BFGS: 140 19:09:14 -306.362442 0.065564
BFGS: 141 19:09:17 -306.362442 0.108570
BFGS: 142 19:09:20 -306.362009 0.055138
BFGS: 143 19:09:22 -306.362009 0.040253