multi-fidelity-training-mlff
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# 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)
