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metadata 
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.

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.

Citation 

@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.

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