Integrating First-Principles Modeling with Explainable Machine Learning for Non-Isothermal Chromatography

This dataset contains the complete simulation library and analysis code accompanying the article: Bilal, M.; Haq, S.; Asif, M.; Alhartomi, A. M. Integrating First-Principles Modeling with Explainable Machine Learning for Non-Isothermal Chromatography. ACS Omega 2026. DOI: 10.1021/acsomega.6c04740. Contents:- 1375 GRM simulations (31 dimensionless input parameters and 12 extracted performance indicators)- Machine learning and SHAP analysis code- Documentation notebook reproducing all reported metrics, SHAP attributions, permutation-importance cross-check, and parameter-space coverage analysis License: Creative Commons Attribution 4.0 International (CC BY 4.0)