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!


Improving DFT descriptions of surface chemistry

Approximate, semi-local density functional theory (DFT) suffers from delocalization error that can lead to a paradoxical model of catalytic surfaces that both overbind adsorbates yet are also too stable. We investigate the effect of two widely applied approaches for delocalization error correction, i) affordable DFT+U (i.e., semi-local DFT augmented with a Hubbard U) and ii) hybrid functionals with an admixture of Hartree-Fock (HF) exchange, on surface and adsorbate energies across a range of rutile transition metal oxides widely studied for their promise as water splitting catalysts.

Machine learning spin-state-dependent catalysis

Metal–oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal–oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure–property relationships. To overcome these challenges, we train machine-learning (ML) models capable of predicting metal–oxo formation energies across a range of first-row metals, oxidation states, and spin states.

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.

WIRES Outlook on the future of ML

As machine learning has matured, it has opened a new frontier in theoretical and computational chemistry by offering the promise of simultaneous paradigm shifts in accuracy and efficiency. Nowhere is this advance more needed, but also more challenging to achieve, than in the discovery of open shell transition metal complexes where localized d or f electrons exhibit variable bonding that is challenging to capture even with the most computationally demanding methods.

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.

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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: