Computational Studies of Selective C-H Functionalization Reactions

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

In this project, funded by the Chemical Structure, Dynamics & Mechanisms B Program and the Chemical Catalysis program, Professor Peng Liu of the Department of Chemistry at the University of Pittsburgh is applying computational tools to study catalytic carbon hydrogen bond functionalization reactions. The goal of this research is to perform computations using density functional theory (DFT) and other computational methods to model catalytic reaction pathways and investigate factors that control reactivity and selectivity. These computational studies will provide mechanistic understanding of various carbon hydrogen bond functionalization reactions catalyzed by transition metal complexes and bioengineered enzymes. In addition, new computational approaches to investigate catalyst–substrate interactions and their impacts on reaction outcomes, including data-driven approaches to rationally design problem-specific descriptors in regression models for reactivity and selectivity predictions, will be explored. This project's educational and outreach plan aims to maximize the power of modern computer technology, such as virtual reality, to enhance learning of organic chemistry concepts and to facilitate synthetic organic chemistry research. Computational tools, in particular, density functional theory (DFT) calculations, have emerged as a potent force to investigate the mechanisms, reactivity, and selectivity of catalytic carbon hydrogen bond functionalization reactions. This project aims to explore three types of new computational strategies to provide robust and meaningful insights into the origin of catalyst effects on reactivity and selectivity. Professor Peng Liu and his research team will (1) apply energy decomposition analysis calculations to analyze non-covalent interactions between catalyst and substrate and use ring strain energies to analyze transition state distortion; (2) use mechanistic insights to select and design problem-specific descriptors for predictive regression models; and (3) perform hybrid quantum mechanics/molecular mechanics and ab initio molecular dynamics simulations to study stereoselective reactions catalyzed by bioengineered enzymes. They expect that these new computational strategies can increase the capability of DFT-based computational studies of mechanically complex catalytic systems by offering chemically meaningful, practical insights to guide experimental reaction and catalyst developments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.