Machine Learning for Materials Design

Electronic structure methods and machine learning


We develop advanced electronic structure methods such as density functional theory (DFT) for modeling both static and dynamic material properties. This includes creating novel exchange-correlation approximations for ground-state DFT, and applying time-dependent DFT for dynamic phenomena. We also integrate artificial intelligence techniques to enhance the accuracy and efficiency of electronic structure methods.