Our unique approach has been to develop and apply computational tools to both enzymatic and catalytic conversion processes, with the ultimate aim of developing computational predictions as part of an iterative ‘model-validate-predict’ design process for de novo catalysts. We aim to establish a leading research program in an interdisciplinary environment where computational predictions coupled with experimental design work hand-in-hand to discover new catalytic materials that can serve as a building block for a new bio-energy infrastructure. The ultimate goal of our research is to create a direct impact on everyone’s life in a sustainable and renewable environment.

Our research group’s central focus is the development of catalytic strategies to convert biomass (for example, corns, woods) into high-valued platform renewable chemicals, polymers, plastics, and biofuels using computational modeling. Three scientific challenges we are pursuing: Mechanism-Driven Discovery of Biopolymer Upgrading (quantum/molecular mechanics (DFT, MD)), Material Design for Catalytic Upgrading of Biomass (DFT, MD and Data Science), and Bottom-up Predictions/Design of (Bio)Fuel & Engine Performance (Cantera, DFT Chemical Kinetics).

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