Machine Learning for Materials Design

Density functional methodologies

Research

We develop advanced density functional theory methods to calculate both static and dynamic material properties. This includes the development of novel exchange-correlation approximations for ground state density functional theory (DFT) and the application of time-dependent density functional theory (TD-DFT) to describe dynamic phenomena. In addition, we use artificial intelligence techniques to improve the accuracy and efficiency of our DFT approaches.