# Data for: Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory This dataset contains the raw data to reproduce the paper: D. Schwalbe-Koda, S. Hamel, B. Sadigh, F. Zhou, V. Lordi. "Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory". arXiv:2404.12367 (2024). DOI: [10.48550/arXiv.2404.12367](https://doi.org/10.48550/arXiv.2404.12367) The raw data in `2025-quests-data.tar.gz` contains all the raw data to reproduce the paper.The tarfile is sorted by section of the paper (01 through 05) and supplementary information (A01 through A11). Its structure is the following: ``` data/ ├── 02-Aluminum ├── 02-GAP20 ├── 02-rMD17 ├── 04-TM23 ├── 05-Cu ├── 05-Ta ├── A08-Denoiser ├── A11-Cu ├── A11-QTB └── A11-Sn ``` The tarfile contains files of the following formats:- CSV files containing tables with the data for the analysis- JSON files containing structured data for the analysis- logfiles from LAMMPS simulations- Extended XYZ files containing the results of MD trajectories or materials structure data ### Citing If you use QUESTS or its data/examples in a publication, please cite the following paper: ```bibtex @article{schwalbekoda2024information, title = {Model-free quantification 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 = {2024}, journal = {arXiv:2404.12367}, url = {https://arxiv.org/abs/2404.12367}, doi = {10.48550/arXiv.2404.12367}, } ```