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.

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Improved uncertainty quantification for discovery

Machine learning (ML) models, such as artificial neural networks, have emerged as a complement to high-throughput screening, enabling characterization of new compounds in seconds instead of hours. The promise of ML models to enable large-scale, chemical space exploration can only be realized if it is straightforward to identify when molecules and materials are outside the model’s domain of applicability.

Enumeration for de novo inorganic chemistry

Despite being attractive targets for functional materials, the discovery of transition metal complexes with high-throughput computational screening is challenged by the amount of feasible coordination numbers, spin states, or oxidation states and the potentially large sizes of ligands. To overcome these limitations, we take inspiration from organic chemistry where full enumeration of neutral, closed shell molecules under the constraint of size has enriched discovery efforts.

Universal QM features of methyltransferases

Methyltransferases (MTases) are among the most ubiquitous regulatory enzymes in the cell, catalyzing gene signaling, protein repair, neurotransmitter regulation, and natural product biosynthesis. Despite being extensively investigated, competing enzymatic enhancement mechanisms have been suggested, ranging from structural methyl group C–H···X hydrogen bonds (HBs) to electrostatic- and charge-transfer-driven stabilization of the transition state (TS). No broad conclusion can be reached because each study is typically carried out on a single MTase and associated substrate.

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About Us

The Kulik group focuses on the development and application of new electronic structure methods and atomistic simulations tools in the broad area of catalysis.

Our Interests

We are interested in transition metal chemistry, with applications from biological systems (i.e. enzymes) to nonbiological applications in surface science and molecular catalysis.

Our Focus

A key focus of our group is to understand mechanistic features of complex catalysts and to facilitate and develop tools for computationally driven design.

Contact Us

Questions or comments? Let us know! Contact Dr. Kulik: