language:
- en
pretty_name: LeMaterial/Atompack
tags:
- atomistic-machine-learning
- materials-science
- chemistry
- atompack
task_categories:
- other
size_categories:
- 100M<n<1B
configs:
- config_name: default
data_files:
- split: train
path: '**'
LeMaterial/Atompack
Atompack is the Hugging Face Hub repository for public atomistic ML datasets distributed in the Atompack format.
This repository is part of the broader LeMaterial effort. Its role is
distribution and serving: it exposes packaged dataset paths that can be opened directly with the
atompack Python package.
The data hosted here comes from upstream public datasets such as LeMat-Bulk, MatPES, MP-ALOE, MPtrj, OMAT24, and OC20. This repository is not the original source of those datasets.
Install
pip install atompack-db
Import the package as atompack after installation.
Open a Dataset
import atompack
db = atompack.hub.open(
repo_id="LeMaterial/Atompack",
path_in_repo="omat/train",
)
print(len(db))
print(db[0].energy)
db.close()
You can also download a local copy first:
import atompack
local_path = atompack.hub.download(
repo_id="LeMaterial/Atompack",
path_in_repo="omat/train",
)
db = atompack.hub.open_path(local_path)
print(len(db))
db.close()
Dataset Paths and Sources
The main public dataset paths currently exposed in this repository are:
lematbulk/pbe, from LeMat-Bulk. If you use this path, cite LeMat-Bulk.matpes/pbeandmatpes/r2scan, from MatPES. If you use these paths, cite MatPES.mp_aloe, from MP-ALOE. If you use this path, cite MP-ALOE.mptrj, from MPtrj. If you use this path, cite MPtrj.oc20/s2ef_train_all, from Open Catalyst 2020 (OC20). This is the full S2EF training split exported as 10 AtomPack shards.oc20/s2ef_val_id,oc20/s2ef_val_ood_ads,oc20/s2ef_val_ood_cat, andoc20/s2ef_val_ood_both, from Open Catalyst 2020 (OC20). These are the standard S2EF validation splits packaged as single-shard AtomPack datasets.omat/trainandomat/val, from OMAT24. If you use these paths, cite OMAT24.
These paths may be stored as one .atp file or as a shard directory.
atompack.hub.open(...) handles both through the same read-only API.
For the OC20 exports in this repository, positions and forces are stored as float32, while
energies and cells are stored as float64.
Why Atompack
Atompack is designed for the point where atomistic datasets stop behaving like small scientific databases and start behaving like training corpora: repeated random reads, multiprocessing workers, large immutable snapshots, and regular export and publish steps.
It provides:
- read-only mmap-backed access for static datasets
- direct indexed reads of full molecule records
- support for local files and shard directories
- direct open/download helpers for Hugging Face Hub paths
More
- Project repository: https://github.com/LeMaterial/atompack
- LeMaterial: https://lematerial.org