Research and Values of the Kulik group

We are an inclusive, creative, and collaborative group of people who work at the interface of computational chemistry, chemical engineering, and materials science for a wide range of applications from fundamental biochemistry to data-driven discovery of new molecules and materials. We prioritize learning - from each other and from the world around us - in our goal to advance the field and our own fundamental knowledge, while staying mindful of the impact we have on each other and society. The Kulik lab prioritizes the mental and physical health of its members as much as our quest to develop into world class scientists. Finally, we embrace very #random (but probably fairly uncool) jokes in our Slack channels. We are computational researchers after all!
 
Our home is in the Chemical Engineering department at MIT, and we are also members of the Center for Computational Science and Engineering and Computational Systems Biology (CSBi) program. Our group is excited to be a part of five multidisciplinary center efforts that bring together researchers across disciplines and universities: the Inorganometallic Catalyst Design Center (ICDC) EFRC, the Center for Enhanced Nanofluidic Transport (CENT) EFRC, the Center for the Exascale Simulation of Material Interfaces in Extreme Environments (CESMIX) PSAAP-III, the NSF CCI Center for the Chemistry of Molecularly Optimized Networks (MONET), and a SciDAC-5 Center (PI: Martin Head-Gordon, UC Berkeley/LBL).  
 
Interested to learn more about our group's research? Check out these recent presentations on YouTube:
 
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.

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Using literature data to engineer MOF stability

Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in practice. To overcome this limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application: the ability to withstand high temperatures without degrading and the capacity to be activated by removal of solvent molecules.

Universal design rules from 23 DFT functionals

Virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient DFT-based workflows are carried out with a single density functional approximation (DFA). Nevertheless, properties evaluated with different DFAs can be expected to disagree for cases with challenging electronic structure (e.g., open-shell transition-metal complexes, TMCs) for which rapid screening is most needed and accurate benchmarks are often unavailable.

The importance of substrate positioning

Nonheme iron halogenases, such as SyrB2, WelO5, and BesD, halogenate unactivated carbon atoms of diverse substrates at ambient conditions with exquisite selectivity seldom matched by nonbiological catalysts. Using experimentally guided molecular dynamics (MD) simulations augmented with multiscale (i.e., quantum mechanics/molecular mechanics) simulations of substrate-bound complexes of BesD and WelO5, we investigate substrate/active-site dynamics that enable selective halogenation.

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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: