README.md

metadata

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, and OMAT24. 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/pbe and matpes/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.
• omat/train and omat/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.

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