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ev-tlt/MACE_finetuning_supplementary

MACE Fine-Tuning Supplementary Supplementary data and scripts for: Tompa, T. L.; Varga-Umbrich, E.; Batatia, I.; Elena, A. M.; Bernstein, N.; Csányi, G. Fine-tuning MLIP foundation models: strategies for accuracy and transferability (2026). arXiv:2606.12704. The repository contains training datasets, mace_run_train launch scripts, Slurm logs, fine-tuned model checkpoints (.model), evaluation scripts, and processed results for the paper. Benchmark systems: lithium argyrodite… See the full description on the dataset page: https://huggingface.co/datasets/ev-tlt/MACE_finetuning_supplementary.

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MACE Fine-Tuning Supplementary

Supplementary data and scripts for:

Tompa, T. L.; Varga-Umbrich, E.; Batatia, I.; Elena, A. M.; Bernstein, N.; Csányi, G. Fine-tuning MLIP foundation models: strategies for accuracy and transferability (2026). arXiv:2606.12704.

The repository contains training datasets, mace_run_train launch scripts, Slurm logs, fine-tuned model checkpoints (.model), evaluation scripts, and processed results for the paper. Benchmark systems: lithium argyrodite electrolytes (li), aqueous NaCl (nacl), ice polymorphs (water), SN₂ reactions (sn2), and MACE-OFF biomolecules (spice). The E0s_MH1_model/ tree holds the MACE-MH1 isolated-atom E0 reestimation study on UiO-66.

Repository layout

MACE_finetuning_supplementary/
├── foundation-utils/          # Foundation checkpoints and OMat24 replay pools
├── E0s_MH1_model/             # MACE-MH1 E0 reestimation on UiO-66
├── li/                        # Lithium electrolyte (LPSC) 
├── nacl/                      # Aqueous NaCl 
├── water/                     # Ice polymorph 
├── sn2/                       # SN₂ reaction 
├── spice/                     # SPICE biomolecules
├── rss-eval/                  # Random structure search (short-range repulsion) evals
└── ablations/                 # Hyperparameter and design ablations

Licensing

Scripts, logs, checkpoints, and results produced for this study are under LICENSE (CC BY 4.0, Tompa et al.). Dataset structure files retain upstream licenses in THIRD_PARTY_LICENSES.md.

Data pathLicenseSource
li/data/CC BY 4.0Zenodo 15686940 (Kim et al., reEWC)
spice/data/MIT10.17863/CAM.107498 (MACE-OFF)
nacl/data/CC BY-SA 4.0niamhon/nacl-water
water/data/CC BY 4.0fine-tuning-MLPs-ice-polymorphs
sn2/data/CC BY 4.0organic_neb_mace
foundation-utils/omat24_filtered_*.extxyzCC BY 4.0OMat24 (subsamples)
li/data/mp_traj*.xyzCC BY 4.0Materials Project / MPtrj (subsamples)
E0s_MH1_model/data/*uio66*CC BY 4.0Elena et al., mace-mof-0 (UiO-66 structures)
E0s_MH1_model/data/omat*-replay*CC BY 4.0OMat24 (replay subsamples)

Citation

bibtex
@misc{tompa2026mlipfinetuning,
  title         = {Fine-tuning {MLIP} foundation models: strategies for accuracy and transferability},
  author        = {Tompa, Tam{\'a}s Lajos and Varga-Umbrich, Eszter and Batatia, Ilyes and Elena, Alin M. and Bernstein, Noam and Cs{\'a}nyi, G{\'a}bor},
  year          = {2026},
  eprint        = {2606.12704},
  archivePrefix = {arXiv},
  primaryClass  = {physics.chem-ph},
  url           = {https://arxiv.org/abs/2606.12704},
  note          = {Journal: TBD}
}

See THIRD_PARTY_LICENSES.md for upstream data citations.

Authors

Tamás Lajos Tompa, Eszter Varga-Umbrich, Ilyes Batatia, Alin M. Elena, Noam Bernstein, Gábor Csányi

ev-tlt/MACE_finetuning_supplementary · Team Ai