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!

Protein-substrate interactions influence catalysis

Biosynthetic enzyme complexes selectively catalyze challenging chemical transformations, including alkane functionalization (e.g., halogenation of threonine, Thr, by the non-heme iron halogenase SyrB2). However, the role of complex formation in enabling reactivity and guiding selectivity is poorly understood, owing to the challenges associated with obtaining detailed structural information of the dynamically associating protein complexes.

Non-empirical corrections to DFT

Density functional theory (DFT) is widely applied to both molecules and materials, but well known energetic delocalization and static correlation errors in practical exchange-correlation approximations limit quantitative accuracy. Common methods that correct energetic delocalization error, such as the Hubbard U correction in DFT+U or Hartree-Fock exchange in global hybrids, do so at the cost of worsening static correlation error.

Mining unexpected interactions in proteins

We have investigated unexpectedly short non-covalent distances (< 85% of the sum of van der Waals radii) in X-ray crystal structures of proteins. We curated over 11,000 high quality protein crystal structures and an ultra-high resolution (1.2 Å or better) subset containing > 900 structures. Although our non-covalent distance criterion excludes standard hydrogen bonds known to be essential in protein stability, we observed over 75,000 close contacts in the curated protein structures.

Predicting simulation outcomes with ML

High-throughput computational screening for chemical discovery mandates the automated and unsupervised simulation of thousands of new molecules and materials. In challenging materials spaces, such as open shell transition metal chemistry, characterization requires time-consuming first-principles simulation that often necessitates human intervention. These calculations can frequently lead to a null result, e.g., the calculation does not converge or the molecule does not stay intact during a geometry optimization.

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.


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: