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!

Mandates for accelerated inorganic discovery

Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery.

Large scale QM in enzyme catalysis

Enzymes have evolved to facilitate challenging reactions at ambient conditions with specificity seldom matched by other catalysts. Computational modeling provides valuable insight into catalytic mechanism, and the large size of enzymes mandates multi-scale, quantum mechanical-molecular mechanical (QM/MM) simulations.

ML for orbital energies in inorganic chemistry

Machine learning the electronic structure of open shell transition metal complexes presents unique challenges, including robust and automated data set generation. Here, we introduce tools that simplify data acquisition from density functional theory (DFT) and validation of trained machine learning models using the molSimplify automatic design (mAD) workflow.

First-principles models of QD growth

Indium phosphide quantum dots (QDs) have emerged as a candidate to replace more toxic II-VI CdSe QDs, but production of high-quality III-V InP QDs with targeted properties requires a better understanding of their growth. We develop a first-principles-derived model that unifies InP QD formation from isolated precursor and early stage cluster reactions to 1.3-nm magic sized clusters, and we rationalize experimentally-observed properties of full sized > 3 nm QDs.

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.

How does solid state density localize? (Ed Choice)

Widely employed approximate density functional theory (DFT) suffers from delocalization errors. DFT+U and hybrid functionals are widely employed methods to correct energetic delocalization errors, but their effect on the density is less well known. Our recent work demonstrated that in transition metal complexes both methods localize density away from the metal and onto surrounding ligands, regardless of metal or ligand identity, in a similar fashion.

Scaling relations in single-site catalysis

Computational catalyst screening is limited primarily by the efficiency with which accurate predictions can be made. In bulk heterogeneous catalysis, linear free energy relationships (LFERs) accelerate screening by relating catalytic activity back to the adsorption energies of key intermediates, but their applicability to single-site catalysts remains unclear, in view of the directional, covalent metal-ligand bonds and the broader chemical space of accessible ligand scaffolds.

Recovering exact conditions in semilocal DFT

Widely employed semi-local DFT suffers from well-known errors that prevent its robust predictio, e.g. in materials and catalyst design. This failure in semi-local DFT can be traced to the violation of exchange-correlation approximations of key exact conditions. The flat-plane condition is the union of two exact constraints in electronic structure theory: (i) energetic piecewise linearity with fractional electron removal or addition and (ii) invariant energetics with change in electron spin in a half filled orbital.


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: