Research in the Kulik group

The Kulik group develops advanced simulation methodology ranging from data-driven to computationally-demanding but very accurate quantum mechanical simulation for the advancement of understanding and design of materials, inorganic catalysts, and biological enzymes. Simulations provide researchers in the Kulik group unique insights into relationships between how structure (i.e., where the atoms and electrons are) informs function (i.e., what do we see at the macroscopic scale). These tools all underlie our advancement of new methods to design molecules atom-by-atom. We also advance and use novel computing architectures to make normally impossible to solve calculations tractable, providing some key insights into how electronic structure teaches us new design rules in large, complex systems such as enzymes. Keep up to date by:

Large-scale quantum effects in enzyme dynamics

We recently carried out the first large-scale electronic structure studies of enzyme dynamics - over 1 ns in total and up to 544 atoms treated fully quantum mechanically. These simulations reveal long range charge transfer to be an essential component of enzyme action that is often excluded from conventional modeling. Hybrid quantum mechanical-molecular mechanical (QM/MM) simulations are the method of choice in enzyme modeling because they provide key insights into enzyme structure–function relationships.

Ligand-only descriptors for catalyst design

We present a detailed study of nearly 70 Zn molecular catalysts for CO2 hydration from four diverse ligand classes ranging from well-studied carbonic anhydrase mimics (e.g., cyclen) to new structures we obtain by leveraging diverse hits from large organic libraries. Using microkinetic analysis and establishing linear free energy relationships, we confirm that turnover is sensitive to the relative thermodynamic stability of reactive hydroxyl and bound bicarbonate moieties.

Discovering inorganic complexes with an ANN

Machine learning (ML) has emerged as a powerful complement to simulation for materials discovery by reducing time for evaluation of energies and properties at accuracy competitive with first-principles methods. In this work, we take our recently developed artificial neural network (ANN) that can predict spin-state ordering to within 3 kcal/mol of DFT training data and use it for chemical exploration.

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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: