Data for: "Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory"

Data for: "Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory" This dataset contains the raw data to reproduce the paper: Benjamin Yu, Vincenzo Lordi, Daniel Schwalbe-Koda. "Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory" (2025). The raw data in `2025-quests-compression-data.tar.gz` contains all the raw data to reproduce the paper. Structure of the raw data The tarfile is sorted by dataset, with the following structure: ```data/├── GAP20│ ├── Fullerenes│ │ ├── dH│ │ ├── SevenNet│ │ ├── test│ │ ├── train│ │ └── val│ ├── Nanotubes│ │ └── ...│ ├── Graphene│ │ └── ...├── TM23│ ├── Ag│ │ ├── data_cold│ │ ├── data_warm│ │ ├── dH_cold│ │ ├── dH_warm│ │ ├── SevenNet│ │ └── test│ ├── Au│ │ └── ...│ ├── Cd│ │ └── ...│ ├── Co│ │ └── ...│ ├── Ir│ │ └── ...│ ├── Pd│ │ └── ...│ ├── Ti│ │ └── ... data_csv/``` The tarfile contains files of the following formats: Training, testing and validation XYZ files generated for model training. Training logs of every model trained Delta entropy calculations of each compressed dataset with respect to the full dataset. CSV files containing the post-processed results from the analysis, which are used for plotting all figures in the manuscript. Datasets The GAP-20 and TM23 datasets used for training/testing ML potentials were obtained from the original sources at: GAP-20: https://doi.org/10.17863/CAM.54529 GAP-20 paper: Rowe et al. J. Phys. Chem. 153, 034702 (2020). https://doi.org/10.1063/5.0005084TM23: https://doi.org/10.24435/materialscloud:6c-b3 TM23 paper: Owen et al. npj Comp Mater 10, 92 (2024). https://doi.org/10.1038/s41524-024-01264-z Code The code for QUESTS is available on GitHub at the link https://github.com/dskoda/quests. The version of the code used in this work (v.2025.09.29) is deposited on Zenodo for persistent storage under the DOI: 10.5281/zenodo.17229448. The SevenNet models used in this work used the code (v. 0.2.0) from Park et al. available at https://github.com/MDIL-SNU/SevenNet. SevenNet paper: Park et al. J. Chem. Theory. Comput. 20 (11), 4857 (2024). https://pubs.acs.org/doi/10.1021/acs.jctc.4c00190 Citing If you use QUESTS or its data/examples in a publication, please cite the following paper: @article{schwalbekoda2025information, title = {Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory}, author = {Schwalbe-Koda, Daniel and Hamel, Sebastien and Sadigh, Babak and Zhou, Fei and Lordi, Vincenzo}, year = {2025}, journal = {Nature Communications}, url = {https://doi.org/10.1038/s41467-025-59232-0}, doi = {10.1038/s41467-025-59232-0}, volume={16}, pages={4014}, }