Mohamed Katish

Computational Approaches for the Discovery of New Phase-Change Materials Using Two Method Machine Learning and First-Principles Density Functional Theory

This work evaluates two approaches for solid-solid PCM discovery: data-driven machine-learning screening and first-principles density functional theory (DFT) modelling, highlighting the complementary roles of ML for rapid candidate identification via screening of known PCMs and DFT for mechanistic characterisation.