| --- |
| 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](https://lematerial.org) 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 |
|
|
| ```bash |
| pip install atompack-db |
| ``` |
|
|
| Import the package as `atompack` after installation. |
|
|
| ## Open a Dataset |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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](https://huggingface.co/datasets/LeMaterial/LeMat-Bulk). If you use this path, cite LeMat-Bulk. |
| - `matpes/pbe` and `matpes/r2scan`, from [MatPES](https://docs.materialsproject.org/collaborations/matpes). If you use these paths, cite MatPES. |
| - `mp_aloe`, from [MP-ALOE](https://www.nature.com/articles/s41524-025-01834-9). If you use this path, cite MP-ALOE. |
| - `mptrj`, from [MPtrj](https://docs.materialsproject.org/services/ml-and-ai-applications/mptrj). If you use this path, cite MPtrj. |
| - `omat/train` and `omat/val`, from [OMAT24](https://huggingface.co/datasets/facebook/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> |
|
|