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 three 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, and the Center for the Exascale Simulation of Material Interfaces in Extreme Environments (CESMIX) PSAAP-III.
Interested to learn more about our group's research? Check out these recent presentations on YouTube:
- Molecular design blueprints from machine learning for catalysis and materials (ML4Science presentation)
- DFT+U and beyond: electronic structure methods for correlated systems (EAIFR presentation)
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.
Keep up to date by:
- Following us on twitter, where we are @KulikGroup.
- Check out our recent News on the news page.
- The group's publications may be found on Google Scholar or on our publications page.
- Email us.
