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!

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.

Large-scale QM/MM in enzymology: COMT

Hybrid quantum mechanical–molecular mechanical (QM/MM) simulations are widely used in studies of enzymatic catalysis. Until recently, it has been cost prohibitive to determine the asymptotic limit of key energetic and structural properties with respect to increasingly large QM regions.

Hydrogen bond design for ion separation

Selective ion separation is a major challenge with far-ranging impact from wastewater treatment to product separation in catalysis. The Hatton group here at MIT has recently pioneered the synthesis of Ferrocenium (Fc+)/ferrocene (Fc) polymeric electrode materials for catalysis and ion separation. In earlier collaborative work (Xiao Su et al Adv. Funct.

Substrate positioning in COMT reactivity

Catechol O-methyltransferase (COMT) is a SAM- and Mg2+-dependent methyltransferase that regulates neurotransmitters through methylation. Simulations and experiments have identified divergent catecholamine substrate orientations in the COMT active site: molecular dynamics simulations have favored a monodentate coordination of catecholate substrates to the active site Mg2+, and crystal structures instead preserve bidentate coordination along with short (2.65 Å) methyl donor-acceptor distances. We carry out longer dynamics (up to 350 ns) to quantify interconversion between bidentate and monodentate binding poses.

The molSimplify code automates discovery

We recently developed a code to automate the discovery and generation of inorganic complex structures. The code is available both as a user-friendly GUI and through commandline or input file interface on OS X and Linux. You can download the latest version of the code, the user guide and compiled executables by visiting


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: