Prof. Heather J Kulik

Kulik leverages computational modeling to aid the discovery of new materials and mechanisms. Her group advances data-driven machine learning models to enable rapid design of open shell transition metal complexes. She advances fundamental theories to enable low-cost, accurate modeling of quantum mechanical properties of transition metal complexes and software for high-throughput screening to reveal design principles and develop data-driven machine learning models for the rapid design of open shell transition metal complexes. Her group uses these tools to bridge the gap from heterogeneous to homogeneous and enzyme catalysis. The methods she develops enable 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.

The Kulik group is focused on developing and applying accurate and efficient quantum mechanical methods to understand and design heterogeneous, molecular, and biological catalysts. A firm understanding of the fundamentals of catalysis is critical for tackling human health challenges and managing disease as well as addressing modern challenges in energy and efficient use of raw feedstocks. Through studying a wide range of catalysts - from enzymes to surface science -- we aim to elucidate unifying principles that govern catalysis and provide a blueprint for catalyst design.