A critical assessment of bonding descriptors for predicting materials properties : ML models and postprocessing data

This Zenodo record contains the machine learning models and supporting results associated with our publication titled "A critical assessment of bonding descriptors for predicting materials properties" The archive includes trained machine learning models (RandomForest/MODNet/SISSO) used in the study, saved in their original serialized formats to support reproducibility. It also contains post-processing and model interpretation outputs, including feature attribution results derived from SHAP (SHapley Additive exPlanations) and permutation feature importance (PFI) analyses. In addition, the record provides correlation analysis results used to support the findings reported in the paper. Details on training procedures, data preprocessing, and evaluation protocols are described in detail in the publication. Codes to completely reproduce this data can be found on Github repository associated with this work here: DigiMatChem/paper-ml-with-lobster-descriptors