| 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
from pathlib import Path
from urllib.request import urlretrieve
trajectory_path = Path("desorption_id_83_2409_9_111-4_neb1.0.traj")
if not trajectory_path.exists():
urlretrieve(
"https://dl.fbaipublicfiles.com/opencatalystproject/data/large_files/"
"desorption_id_83_2409_9_111-4_neb1.0.traj",
trajectory_path,
)
from ase.io import read
traj = read(trajectory_path, ":")
unrelaxed_frames = traj[0:10]
relaxed_frames = traj[-10:]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-1p2p1")
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)Warp 1.18.0 initialized:
CUDA Toolkit 13.4, Driver 13.1
Devices:
"cpu" : "x86_64"
"cuda:0" : "Tesla T4" (16 GiB, sm_75, mempool enabled)
Kernel cache:
/home/runner/.cache/warp/1.18.0
WARNING:root:device was not explicitly set, using device='cuda'.
/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/ase/mep/neb.py:329: UserWarning: The default method has changed from 'aseneb' to 'improvedtangent'. The 'aseneb' method is an unpublished, custom implementation that is not recommended as it frequently results in very poor bands. Please explicitly set method='improvedtangent' to silence this warning, or set method='aseneb' if you strictly require the old behavior (results may vary). See: https://gitlab.com/ase/ase/-/merge_requests/3952
warnings.warn(
WARNING:root:Model is being compiled this might take a while for the first time
W1007 02:51:22.310000 8421 site-packages/torch/_logging/_internal.py:1345] [0/0] Profiler record function <class 'torch.autograd.profiler.record_function'> will be ignored
W1007 02:52:10.623000 8421 site-packages/torch/_inductor/utils.py:1953] [6/0] Not enough SMs to use max_autotune_gemm mode
/home/runner/work/_tool/Python/3.12.15/x64/lib/python3.12/site-packages/torch/_inductor/lowering.py:2352: FutureWarning: `torch._prims_common.check` is deprecated and will be removed in the future. Please use `torch._check*` functions instead.
check(
Step Time Energy fmax
BFGS: 0 02:53:06 -305.668762 5.272434
BFGS: 1 02:53:08 -305.597497 11.665585
BFGS: 2 02:53:10 -305.823062 1.877240
BFGS: 3 02:53:17 -305.840877 2.466167
BFGS: 4 02:53:23 -305.935036 2.365342
BFGS: 5 02:53:26 -305.942107 7.320114
BFGS: 6 02:53:29 -306.228829 8.432062
BFGS: 7 02:53:31 -306.223755 2.478800
BFGS: 8 02:53:40 -306.273579 4.309765
BFGS: 9 02:53:51 -306.300463 0.770945
BFGS: 10 02:53:56 -306.314714 0.581313
BFGS: 11 02:54:02 -306.349713 1.184198
BFGS: 12 02:54:08 -306.408283 1.993177
BFGS: 13 02:54:13 -306.427396 0.445975
BFGS: 14 02:54:16 -306.423904 0.893946
BFGS: 15 02:54:22 -306.406772 2.152245
BFGS: 16 02:54:25 -306.344867 2.780494
BFGS: 17 02:54:29 -306.328264 1.021872
BFGS: 18 02:54:33 -306.227197 1.868081
BFGS: 19 02:54:39 -306.129470 3.274653
BFGS: 20 02:54:42 -306.147865 2.506686
BFGS: 21 02:54:51 -306.144218 0.835766
BFGS: 22 02:54:54 -306.177451 0.917536
BFGS: 23 02:55:01 -306.179271 0.407054
BFGS: 24 02:55:05 -306.226053 1.325884
BFGS: 25 02:55:12 -306.249059 1.250787
BFGS: 26 02:55:17 -306.235329 0.643100
BFGS: 27 02:55:21 -306.223339 0.474843
BFGS: 28 02:55:27 -306.226585 0.267351
BFGS: 29 02:55:31 -306.233086 0.346515
BFGS: 30 02:55:35 -306.247422 0.361674
BFGS: 31 02:55:43 -306.251811 0.448411
BFGS: 32 02:55:50 -306.248439 0.347654
BFGS: 33 02:56:02 -306.242115 0.310477
BFGS: 34 02:56:04 -306.235145 0.605209
BFGS: 35 02:56:05 -306.243418 0.524670
BFGS: 36 02:56:09 -306.248299 0.294079
BFGS: 37 02:56:14 -306.248348 0.312957
BFGS: 38 02:56:20 -306.256021 0.420672
BFGS: 39 02:56:23 -306.255649 0.270029
BFGS: 40 02:56:26 -306.249752 0.291312
BFGS: 41 02:56:29 -306.251714 0.320541
BFGS: 42 02:56:36 -306.261873 0.242245
BFGS: 43 02:56:41 -306.258520 0.171776
BFGS: 44 02:56:48 -306.253957 0.277481
BFGS: 45 02:56:55 -306.255328 0.167239
BFGS: 46 02:56:59 -306.258147 0.167616
BFGS: 47 02:57:03 -306.257514 0.101079
BFGS: 48 02:57:12 -306.257237 0.088189
BFGS: 49 02:57:17 -306.257030 0.118348
BFGS: 50 02:57:27 -306.257429 0.188631
BFGS: 51 02:57:39 -306.258925 0.219359
BFGS: 52 02:57:44 -306.260649 0.192540
BFGS: 53 02:57:50 -306.261685 0.166636
BFGS: 54 02:57:57 -306.262522 0.157853
BFGS: 55 02:58:00 -306.262680 0.215892
BFGS: 56 02:58:03 -306.263402 0.196768
BFGS: 57 02:58:07 -306.264585 0.198391
BFGS: 58 02:58:10 -306.265059 0.192521
BFGS: 59 02:58:19 -306.264356 0.176577
BFGS: 60 02:58:24 -306.264486 0.189131
BFGS: 61 02:58:29 -306.266404 0.212549
BFGS: 62 02:58:31 -306.268224 0.161119
BFGS: 63 02:58:35 -306.268848 0.109210
BFGS: 64 02:58:39 -306.268478 0.102071
BFGS: 65 02:58:42 -306.268150 0.081287
BFGS: 66 02:58:49 -306.268150 0.580691
BFGS: 67 02:58:54 -306.268150 0.102405
BFGS: 68 02:58:56 -306.268150 0.077076
BFGS: 69 02:59:04 -306.268150 0.072212
BFGS: 70 02:59:08 -306.268150 0.205508
BFGS: 71 02:59:12 -306.268150 0.102343
BFGS: 72 02:59:16 -306.268150 0.076812
BFGS: 73 02:59:19 -306.268150 0.074793
BFGS: 74 02:59:23 -306.268150 0.068981
BFGS: 75 02:59:28 -306.268150 0.052151
BFGS: 76 02:59:32 -306.268150 0.042765