Research in our laboratory focuses on innovations at the interface of computational and synthetic organic chemistry. We are currently interested in the development and application of machine learning algorithms for reaction and catalyst development.
Data-driven Reaction Optimization
Chemical syntheses are generally devised by practitioners with significant experience and yet, are often executed in a trial-and-error fashion. Thus, an overarching objective of our program is to apply data-driven workflows incorporating physical organic principles directly within the reaction development and discovery process.
Enantioselective Reaction Design
Our research aims to discover, develop and understand new reactions that can be performed to generate chiral molecules in high levels of enantiomeric excess. These studies typically focus on chiral Brønsted acids as a generally applicable asymmetric catalyst.
Analyzing Reaction Mechanism
The calculation of transition states involved in selective catalytic processes and attempting to understand the interactions that give rise to performance have become integral steps in the workflow for reaction development. Therefore, one of our key aims is to identify the important noncovalent interactions that guide enantioselectivity outcomes in modern organic reactions. Based on fundamental mechanistic insights, novel applications and catalysts can be designed.