We focus on the development and application of new electronic structure theories. Specifically, we are interested in multi-scale models, which allow for the study of large and extended systems. We are developing quantum-embedding theories, which treat different regions of the system at different levels of accuracy. This allows for a high chemical accuracy in a small region, such as an active site of a catalyst, and a less accurate, but more computationally efficient description of the remainder. These tools can then be used to perform first-principle studies on large, reactive, and condensed phase systems. We are interested in applying these methods to metalloenzymes, heterogeneous catalysts, and metal-organic frameworks.
Biography
Education
B.S. University of Illinois, Urbana-Champaign
Ph.D. California Institute of Technology
Post Doctorate Lawrence Berkeley National Laboratory, 2014 -2016
Publications & Awards
Honors and Awards
National Science Foundation CAREER award, 2020
Chemical Computing Group Excellence Award for Graduate Students, 2014
Data Science Research Interests
Theoretical Chemistry and Machine Learning in Quantum Chemistry, Neural Network Potentials, Physically Motivated Machine Learning Models