Jan. 25, 2024 – Daniel King: Moving Beyond Density Functional Theory with Multiconfigurational Methods and Machine Learning

Density functional theory (DFT) lies at the heart of all practical applications of theoretical chemistry. However, it is well-known that DFT often provides unsatisfactory descriptions of many important systems: non-equilibrium geometries, such as transition states, and strongly correlated systems, such as transition metals and excited states. My research aims to move beyond these limitations through the use of high-accuracy multiconfigurational methods and machine-learned trends from experimental data. In particular, I have developed methods to overcome the difficult problem of active space selection in applying multiconfigurational methods to large-scale datasets, and have collaborated with experimentalists to improve MOF catalyst yields through the use of high-throughput data-driven experiments. These two directions of research will be presented and future ideas will be discussed.