Machine Learning for Materials Design

Density functional methodologies


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.