The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom.
Research in the Kulik group
Research in the Kulik group leverages computational modeling to aid the discovery of new materials and mechanisms. Our group uses first-principles modeling to unearth fundamental aspects of structure-property relationships in catalysts and materials. By taking a computational approach, we carry out studies that allow us to make connections across a wide range of catalytic systems from biological enzymes to emerging heterogeneous single-atom catalysts. We develop computational software and machine learning models that accelerate the discovery of new materials and design rules. This approach enables the prediction of new materials properties in seconds, the exploration of million-compound design spaces, and the identification of design rules and exceptions that go beyond intuition. To ensure the predictive power of our approach, our group develops new methods to increase the accuracy of density functional theory especially for materials with challenging electronic structure such as transition metal complexes and solids.
Read more about our group’s work in the recently published papers below!