Hugging Face's logo

Datasets:
microsoft
/
multi-fidelity-training-mlff

Modalities:
Text
Size:
< 1K
ArXiv:
Tags:
mlip
mlff
multi-fidelity
License:
Dataset card Data Studio Files
xet
 Community
Dataset Viewer
The dataset viewer is not available for this dataset.
The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

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.

Contact

Downloads last month
59
Total file size:
43.4 GB

Paper for microsoft/multi-fidelity-training-mlff