Research Interest

My research focuses on leveraging state-of-the-art computational methods, including density functional theory (DFT), classical and ab initio molecular dynamics (MD) simulations, chemoinformatics, and modern data-driven approaches such as machine learning algorithms, to gain mechanistic insights into homogeneous catalysis and enzymatic reactions. By employing energy decomposition analysis (EDA) and kinetic modeling, I quantitatively elucidate reaction mechanisms and the origins of reactivity and selectivity. These insights help rationalize experimental findings, predict the effects of catalysts and reagents, and guide the design of highly efficient catalysts and reactive enzymes.

By combining computational chemistry with advanced data analysis techniques, my research aims to provide predictive models and fundamental insights that drive innovation in catalysis and chemical biology. My work is highly interdisciplinary and closely integrated with experimental collaborations to address challenges in chemistry and biochemistry. Current research directions include:

• Workflow development using Bayesian optimization for chemical reaction optimization and reaction condition recommendations.

• Computational catalyst design, integrating grid-based dispersion/electrostatic interaction fields with machine learning to predict reaction selectivity and accelerate catalyst development.

• Multiscale molecular simulations to explore enzymatic reaction mechanisms.

• Theoretical investigations of reactivity and selectivity rules in organic and organometallic reactions.

• Molecular dynamics simulations for protein dynamics, as well as protein-ligand and protein-protein binding free energy calculations.

TEACHING

• Computational Chemistry.

• Advanced - Physical Chemistry.

• Organic Structure and Selectivity.

Award and prize

• 2018 - 2019: Carl Tryggers Fellowship (CTS 17:198).

• 2016: Catalysis Science & Technology Poster Prize.