Predicting electronic properties of molecules: a stacking ensemble model for HOMO and LUMO energy estimation
Abstract
for LUMO), outperforming individual baseline models. Feature selection using the SelectKBest algorithm with mutual information regression identified the most influential descriptors. To ensure these descriptors did not trivially encode HOMO or LUMO energies, we performed correlation analysis between each descriptor and the target properties. SHAP Tree Explainer analysis further revealed the feature contribution of each feature to model predictions. In addition, analysis of molecular topology and functional groups highlighted trends in aromaticity and ring structures, and their impact on electronic behavior. Finally, HOMO-LUMO gap analysis demonstrated how molecular structure and functionalization affect electronic properties.
Fields of Study & Subjects
Machine Learning in Materials Science
Advanced Physical and Chemical Molecular Interactions
Advanced Chemical Physics Studies
Publication details
- Journal / source
- RSC Advances
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 16
- Issue
- 14
- Pages
- 12444–12457
- Publication date
- 1 January 2026
- Language
- EN