D. Pundhir

Predicting dielectric constants of crystalline materials using explainable machine learning and composition-aware feature engineering

An explainable machine-learning framework was developed for dielectric constant prediction using 52,168 crystalline materials extracted from the Joint Automated Repository for Various Integrated Simulations (JARVIS-DFT) database, demonstrating the complementary roles of electronic structure and elemental chemistry.