add info to README

README.md

@@ -6,4 +6,54 @@ tags:
 - 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)