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--- |
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language: |
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- en |
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tags: |
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- physics |
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- materials |
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- science |
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- chemistry |
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pretty_name: LMX Dataset |
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size_categories: |
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- 10K<n<500K |
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--- |
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# LMX_Dataset |
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**Pretty name:** LMX Dataset |
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**Tags:** materials-science, atomistic-simulations, extxyz, ase, mliap |
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**License:** |
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**Dataset type:** splits (train/val/test) |
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## Summary |
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This dataset provides three `.extxyz` splits (train/val/test) for training and benchmarking machine-learning interatomic potentials (MLIAPs). |
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- **Format:** ASE/EXTXYZ with keys: `REF_energy`, `REF_forces`, `REF_stress` |
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- **Units:** energy (eV), forces (eV/Å), stress (eV/ų) |
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- **Systems:** Halide-based Solid electrolytes |
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- **Provenance:** DFT/MD details: functional, code, cutoffs, k-points, thermostat/barostat, etc. |
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## Files |
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- `data/dataset_aliovalent_antisite.extxyz` — <1> frames |
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- `data/dataset_aliovalent.extxyz` — <1> frames |
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- `data/dataset_isovalent.extxyz` — <1> frames |
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- `data/train.extxyz` — <1> frames |
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- `data/val.extxyz` — <1> frames |
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- `data/test.extxyz` — <1> frames |
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- `index.json` — split counts, checksums, and schema |
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### Schema (per-frame) |
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- Energies stored in `info["REF_energy"]` (float, eV) |
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- Forces stored in `arrays["REF_forces"]` (shape: (N,3), eV/Å) |
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- Stress stored in `info["REF_stress"]` (Voigt 6 or 3x3; eV/ų). Specify exact convention below. |
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**Stress convention:** <Voigt order and sign convention; e.g., [σ_xx, σ_yy, σ_zz, σ_yz, σ_xz, σ_xy]> |
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## Quickstart |
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### Python: Download entire repository |
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```python |
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from huggingface_hub import snapshot_download |
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local_dir = snapshot_download( |
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repo_id="cparidaAI/LMX_dataset", |
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repo_type="dataset", |
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local_dir="dataset", |
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local_dir_use_symlinks=False |
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) |
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``` |
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### Python: Load a split with ASE |
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```python |
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from ase.io import read |
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train = read('hf://datasets/<your-username>/my-mliap-dataset/data/train.extxyz', ':') |
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``` |
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### Python: Fetch a single file |
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```python |
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from huggingface_hub import hf_hub_download |
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path = hf_hub_download( |
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repo_id="cparidaAI/LMX_dataset", |
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repo_type="dataset", |
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filename="data/train.extxyz" |
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) |
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from ase.io import read |
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train = read(path, ':') |
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``` |
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## Benchmarks / Intended use |
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- Primary task: supervised learning of energies/forces/stresses for MLIAPs. |
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- Suggested splits: train/val/test as provided. |
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- Recommended metrics: energy MAE (meV/atom), force MAE (meV/Å), virial/stress MAE (GPa or eV/ų). |
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## Provenance & generation |
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- **Code:** VASP/PBE |
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- **Settings:** ENCUT, K-mesh, smearing, thresholds, etc. |
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- **MD Sampling:** NVT/NPT, T/P ranges, timesteps |
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- **Post-processing:** relaxations, filters, deduplication, unit conversions |
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## License |
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This dataset is released under **will be here**. See `LICENSE`. |
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## How to cite |
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Please cite: |
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1. <Your article / preprint / DOI> |
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2. This dataset (see `CITATION.cff`) |
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## Changelog |
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- v1.0.0 — initial release. |
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--- |
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**Contact:** Chiku Parida, DTU Energy, |
