README.md

---
license: mit
tags:
- mlip
- mlff
- multi-fidelity
size_categories:
- 1M<n<10M
---

# Multi-Fidelity Training of Machine-Learned Force Fields — Dataset

Dataset accompanying the paper
[*Understanding Multi-Fidelity Training of Machine-Learned Force-Fields*](https://arxiv.org/abs/2506.14963).


## File Structure

```
data/
├── data.lmdb                      # LMDB database with atomic structures and labels
├── metadata.parquet               # Lightweight metadata index
├── schema.json                    # Schema for the metadata
├── {method}_train_{a,b,c,d}.json  # Train/validation/test split definitions (12 files)
```

- **`data.lmdb`** — The main database containing atomic positions, atomic numbers, energies, and forces for each structure.
- **`metadata.parquet`** — A metadata index with columns: `formula`, `conformation_idx`, `method`, `n_atoms`, `energy`, `forces_present`, `energy_unit`, `forces_unit`, `idx`.
- **`{method}_train_{a,b,c,d}.json`** — Split files defining train, validation, and test indices for each method (`dft`, `xtb`, `cc`) and training group (`a`–`d`). Indices reference entries in the LMDB database.
- **`schema.json`** — Schema definition for the metadata fields.

## Code

The code to reproduce the experiments in the paper is available at
[github.com/microsoft/multi-fidelity-training-mlff](https://github.com/microsoft/multi-fidelity-training-mlff).

## Citation

```bibtex
@online{Gardner2025Understanding,
  title = {Understanding Multi-Fidelity Training of Machine-Learned Force-Fields},
  author = {Gardner, John L. A. and Schulz, Hannes and Helie, Jean and Sun, Lixin and Simm, Gregor N. C.},
  date = {2025-06-17},
  eprint = {2506.14963},
  eprinttype = {arXiv},
  eprintclass = {physics},
  doi = {10.48550/arXiv.2506.14963},
  url = {http://arxiv.org/abs/2506.14963}
}
```

## License

This dataset is released under the [MIT License](https://opensource.org/licenses/MIT).

## Contact

- John Gardner — [johngardner@microsoft.com](mailto:johngardner@microsoft.com)
- Gregor Simm — [gregorsimm@microsoft.com](mailto:gregorsimm@microsoft.com)