Hong-Zhou Ye
Contact Info
Office: CHM 1144
Phone: 617-909-6658
https://sites.google.com/view/yegroup
Hong-Zhou Ye
Assistant Professor

Education

  • B.Sc. in Chemistry, Peking University, 2015
  • Ph.D. in Chemistry, Massachusetts Institute of Technology, 2020

Professional Experience

  • Assistant Professor, University of Maryland, College Park, starting 2024
  • Postdoctoral Fellow with Prof. Timothy C. Berkelbach, Department of Chemistry, Columbia University, 2020 – 2024

Research Interests

We develop and apply quantum chemistry-based computational tools to study functional molecules and materials relevant to energy, storage, and pharmaceutical applications. We are particularly interested in developing and extending theories and algorithms that exploit locality, sparsity, and low-rank structures in correlated wavefunctions to enable accurate and scalable electronic structure calculations. This research thrust naturally intersects with emerging symmetry-aware machine learning techniques such as equivariant neural networks for tensorial learning, and also informs the development of more approximate methods such as density functional theory and semi-empirical methods. We apply our newly developed tools to tackle cutting-edge problems in chemistry and materials science. Current areas of interest include molecular solids, catalyzing surfaces, and porous materials such as covalent organic frameworks.

Major Recognitions and Honors

  • American Chemical Society Young Investigator Award in Physical Chemistry for Theory, 2023
  • American Chemical Society Wiley Computers in Chemistry Outstanding Postdoc Award, 2022

We develop and apply quantum chemistry-based computational tools to study functional molecules and materials relevant to energy, storage, and pharmaceutical applications. We are particularly interested in developing and extending theories and algorithms that exploit locality, sparsity, and low-rank structures in correlated wavefunctions to enable accurate and scalable electronic structure calculations. This research thrust naturally intersects with emerging symmetry-aware machine learning techniques such as equivariant neural networks for tensorial learning, and also informs the development of more approximate methods such as density functional theory and semi-empirical methods. We apply our newly developed tools to tackle cutting-edge problems in chemistry and materials science. Current areas of interest include molecular solids, catalyzing surfaces, and porous materials such as covalent organic frameworks.