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!

Hubbard U for correcting minimal basis sets

We recently introduced an approach to get more mileage out of minimal basis set calculations for geometry optimization on graphical processing units. In this approach, we take the Hubbard U +U correction normally applied for correcting self-interaction errrors in DFT to correct basis set incompleteness error in formally-SIE free Hartree-Fock theory.

Fullerene allotropes throughout the periodic table

Thirty years after the discovery of Buckminsterfullerene (C60), the excellent properties and potential applications of this unusual carbon allotrope continue to drive considerable scientific inquiry.

Watching QDs form with AIMD

Colloidal quantum dots (QDs), such as Indium Phosphide QDs, exhibit highly desirable size- and shape-dependent properties for applications from electronic devices to imaging. Production of InP QDs with the desired properties has lagged behind other QD materials due to a poor understanding of how to tune the growth process. Using high-temperature ab initio molecular dynamics (AIMD) simulations, we report the first direct observation of early-stage intermediates and subsequent formation of an InP cluster from indium and phosphorus precursors. In our simulations, indium agglomeration precedes formation of In–P bonds. We observe a predominantly intercomplex pathway in which In–P bonds form between one set of precursor copies, and the carboxylate ligand of a second indium precursor in the agglomerated indium abstracts a ligand from the phosphorus precursor. This process produces an indium-rich cluster with structural properties comparable to those in bulk zinc-blende InP crystals.

New paradigms for enzyme catalysis

We use a combination of long-time molecular dynamics and ab initio electronic structure theory to probe the underlying characteristic motions and interactions that facilitate catalysis of reactions in the environment of an enzyme. Recent work on a ubiquitous methyltransferase has been published in collaboration with an experimental group in the Proceedings of the National Academy of Sciences.

Amorphous nanostructures from AIMD

Semiconducting quantum dots (QDs) have a broad number of applications due to their unique size- and shape- dependent electronic and optical properties. When people use first-principles simulations to study structure-property relationships in QDs, the experimental bulk crystal structure is the most commonly used model. However, experiments show QDs may possess distinct, amorphous structures.

Exploring the role of exchange in transition metal complex properties

Spin crossover complexes are transition metal complexes that undergo a transition in spin or magnetic moment when a stimulus such as increasing temperature, pressure, or light is applied. Many catalysts of interest also possess multiple closely spaced spin states. In all of these cases, robust prediction of ground state spin is a profound challenge for almost all electronic structure methods, especially density functional theory. One challenge for DFT is self-interaction error, in which electrons are repelled by their own image.

Studying depolymerization dynamics of lignin from first-principles

Lignin is a highly heterogeneous biopolymer that makes up about 35% of biomass by weight. While lignin monomers are relatively homogeneous aromatic compounds, e.g. coniferyl alcohol, they link together to form at least 8 different kinds of linkages. In order to depolymerize lignin into useful products, it is necessary to understand how it can be broken down in order to turn it into valuable products.

Understanding nanoparticle growth

Indium phosphide (InP) quantum dots (QDs) have a wide range of applications due to their unique size- and shape-dependent electronic and optical properties. In this project, we aim to understand InP QDs core structure and surface ligand morphology through ab initio simulations. We investigate the interaction between indium phosphide nanoparticle surfaces and precursors using techniques such as ab initio molecular dynamics.

Catalyst design and discovery

Efficient design and discovery of catalysts is central to solving modern challenges in energy and resource utilization. Effective catalyst design, however, is a multi-step process that includes the generation of candidate catalytic materials followed by evaluation and modification of their properties with the ultimate goal of maximizing the catalytic activity for a specific reaction or reaction network. Often these improvements are carried out in an ad hoc manner. Instead, we are developing systematic workflows that streamline this process by combining first principles calculations, chemical intuition, cheminformatics and evaluation of catalytic properties. Here, we target single-site catalysts that provide exquisite control and selectivity but are supported on hetereogeneous substrates, allowing us to overcome the challenges associated with separation and recovery of molecular catalysts.

Recent work: Quantum chemistry for proteins

Proteins are large biological macromolecules that play a pivotal role in the function of all living things.  Because of the large size of proteins (most are at least several hundred to thousands of atoms in size), study of their structure and function has been largely limited to empirical force fields.  While these force fields can reproduce many basic structural properties of proteins as observed experimentally by NMR or X-ray crystallography, typical force fields cannot accurately describe bond-rearrangement, polarization, and charge transfer, all of which are key for understanding protein function.  We recently investigated whether GPU-accelerated quantum chemistry approaches could provide additional insight into protein structure-function relationships by examining a vast test set of over 55 proteins with a variety of DFT, HF, and force field methods.  


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: