Research in the Kulik group

Research in the Kulik group harnesses first-principles electronic structure in transition-metal chemistry and large-scale enzymology to understand fundamental phenomena in chemical bonding for catalyst and materials design. Our broader research goals are delineated by the following three research areas:

Automated molecular design and discovery. Computational discovery techniques have become integral in identifying new compounds from among many possible candidates in vast regions of chemical space. Transition metal complexes have many desirable characteristics in bonding and reactivity or electronic properties for functional materials, but strategies for their efficient design are much less well-developed in comparison to the discovery of simple organic molecules or ordered solid state materials. We develop new software tools that merge what works from therapeutic drug design and the machine learning communities with knowledge we’ve developed about transition metal chemistry to enable efficient and informed traversal of chemical space to discover new compounds. We’ve applied these ideas across materials science – from redesigning the building blocks of quantum dots to the discovery of new functionalizations for electrochemically active polymers for ion separation. Many of these advances hinge on our open-source code molSimplify, which enables automated discovery of transition metal complexes by interfacing with large chemical or user-built databases. More recently, we’ve introduce the first artificial neural network capable of predicting both spin-state and geometry of transition metal complexes to molSimplify, enabling prediction of transition metal complex properties at a fraction of the computational cost of typical DFT studies, enabling high-throughput screening.

Large-scale quantum phenomena in enzymes and the condensed phase. Knowledge of the mechanism by which enzymes accelerate reaction rates is the cornerstone of fundamental understanding in health and disease. Despite strides in understanding enzyme mechanism, guiding principles for the role of the greater protein environment in this rate enhancement remain under debate. Computational atomistic force field and first-principles simulation have emerged as crucial tools to augment experimentally-derived mechanistic understanding, as highlighted by the 2013 Nobel Prize for the development of mixed quantum-mechanical/molecular-mechanics (QM/MM) multiscale methods. First-principles, QM methods are essential to treat the charge transfer, polarization, and bond rearrangements in enzyme catalysis, albeit at much higher computational cost than MM methods. Our group has developed new quantitative methods to identify electronic contributions to enzyme properties. We’re now using these tools to reveal electronic contributions to rate enhancement, identify interactions that give rise to substrate specificity, and guide our understanding of where strong quantum mechanical interactions may reveal new structure-function relationships.

Electronic structure for transition metal chemistry and catalysis. Approximate density functional theory (DFT) is an essential tool for computational materials and catalysis discovery initiatives, owing to its balance of computational cost and accuracy, yet the accuracy of DFT is eroded proportionately to the novelty of the materials being studied (e.g., correlated electrons in transition metals). In order both for discovery efforts to become predictive and large-scale electronic structure in enzymes to provide meaningful results, these errors must be both understood and corrected. Presently available DFT functionals suffer from a mixture of errors, and standard corrections (e.g., DFT+U and range-separated hybrid functionals) improve one facet (e.g., delocalization error) while worsening another (e.g., static correlation error). Further, although energetic errors may be corrected, density errors may worsen or not improve sufficiently. We are currently developing novel correction schemes that simultaneously alleviate numerous errors by separately treating fractional spin concavity and fractional charge convexity. We also address uncertainty in DFT through an artificial neural network that can predict the sensitivity of key electronic properties to changes in functional choice.

Read more about recent papers and projects in the group below!

Conceptual DFT for systematic modeling

Multi-scale quantum-mechanical/molecular-mechanical (QM/MM) and large-scale QM simulation provide valuable insight into enzyme mechanism and structure-property relationships. Analysis of the electron density afforded through these methods can enhance our understanding of how the enzyme environment modulates reactivity at the enzyme active site. From this perspective, tools from conceptual density functional theory to interrogate electron densities can provide added insight into enzyme function.

Predicting electronic structure with ANNs

Direct density functional theory (DFT) simulation is the critical bottleneck in high-throughput computational screening for inorganic materials and molecular transition metal complexes. These calculations are computationally costly and the calculated properties are sensitive to the exchange–correlation functional employed. Screening efforts for molecular inorganic systems are complicated by the uncertain spin-state ordering of hypothetical complexes and difficulty in obtaining good initial guess geometries.

Discovering chemistry from large databases

Using our recently developed inorganic discovery toolkit, molSimplify code, candidate molecules were obtained from the ChEMBL-19 database (> 1M molecules) to address a critical outstanding challenge in materials science. These databases of bioactive organic molecules are typically employed for discovery of therapeutic drug-like molecules; we instead demonstrated their power as a tool to discover design rules for inorganic complexes while maintaining realism (i.e., stable, synthetically accessible substituents) and providing diversity in functional groups.

Inorganic discovery & machine learning

Virtual high throughput screening has emerged as a powerful tool for the discovery of new materials. Although the computational materials science community has benefited from open source tools for the rapid structure generation, calculation, and analysis of crystalline inorganic materials, software and strategies to address the unique challenges of inorganic complex discovery have not been as widely available. We present our recent developments in the open source molSimplify code for inorganic discovery.

Curvature in DFT (JCP Ed. Choice 2016)

Piecewise linearity of the energy with respect to fractional electron removal or addition is a requirement of an electronic structure method that necessitates the presence of a derivative discontinuity at integer electron occupation. Semi-local exchange-correlation (xc) approximations within density functional theory (DFT) fail to reproduce this behavior, giving rise to deviations from linearity with a convex global curvature that is evidence of many-electron, self-interaction error and electron delocalization.

Quantifying electronic effects in enzyme catalysis

We have developed two new methods that help to quantify when electronic effects matter in enzyme active sites to help guide a systematic approach to multi-scale modeling (i.e., QM/MM simulation). First, in the charge shift analysis (CSA) method, we probe the reorganization of electron density when core active site residues are removed completely, as determined by large-QM region QM/MM calculations.

Paradoxical meta-GGA behavior in TM complexes

Prediction of spin-state ordering is essential for understanding catalytic activity and designing functional materials. Semilocal DFT can suffer from self-interaction errors that give rise to systematic bias for low-spin states. We recently identified surprising behavior from incorporation of higher-order terms (i.e., in a meta-GGA).

Dynamics of depolymerization pathways

Lignocellulosic biomass is an abundant, rich source of aromatic compounds, but direct utilization of raw lignin has been hampered by both the high heterogeneity and variability of linking bonds in this biopolymer. Ab initio steered molecular dynamics (AISMD) has emerged both as a fruitful direct computational screening approach to identify products that occur through mechanical depolymerization (i.e., in sonication or ball-milling) and as a sampling approach.

DFT for molecule-surface interactions

First-principles simulation has played an ever-increasing role in the discovery and interpretation of the chemical properties of surface–adsorbate interactions. Nevertheless, key challenges remain for the computational chemist wishing to study surface chemistry: modelling the full extent of experimental conditions, managing computational cost, minimizing human effort in simulation set-up and maximizing accuracy. Our recent work introduces new tools for streamlining surface chemistry simulation set-up and reviews some of the challenges in first-principles, density functional theory (DFT) simulation of surface phenomena. 

Density delocalization in DFT

Approximate DFT is well-known to suffer from self-interaction error, which is expected to particularly plague the localized 3d and 4f electrons of transition metal complexes. In order to diagnose SIE, energetic delocalization error, i.e. deviation from piecewise linearity, is frequently used, but errors in the density are less well-understood.

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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: