Unraveling Structure-Property
Relationships at Dynamic Interfaces
for Rational Materials Design

with Guoxiang (Emma) Hu

Assistant Professor
School of Materials Science and Engineering
Georgia Tech

Abstract: Efficient and sustainable energy harvesting and utilization is one of the prime scientific challenges of today. A key objective lies in finding high-performance and low-cost materials for these applications. However, new computational methods are needed to enable rational materials design for these complex dynamic physical and chemical processes. My group at Georgia Tech seeks to uncover the atomic-level mechanisms governing energy harvesting, storage, and conversion, and to elucidate structure-property relationships at dynamic interfaces for knowledge- and data-driven materials discovery. We develop integrated computational frameworks that combine ab initio calculations, grand canonical modeling, machine learning potentials, and data-driven screening to capture the fundamental physical phenomena underlying materials’ functional properties. In this colloquium, I will talk about two representative directions of our ongoing research: (1) Revealing electrochemical interfaces in operando for energy storage and conversion, and (2) Understanding complex surface chemistry of quantum-confined nanocrystals for energy harvesting. Through close collaborations with experimental groups, our goal is to bridge fundamental understanding and predictive modeling to drive rational materials design and address pressing energy-related challenges.

Biography: Dr. Guoxiang (Emma) Hu is an Assistant Professor in the School of Materials Science and Engineering at the Georgia Institute of Technology. Prior to joining Georgia Tech, she served as an Assistant Professor at the City University of New York (2020-2023) and was a Postdoctoral Scholar at the Center for Nanophase Materials Sciences, Oak Ridge National Laboratory (2018-2020). She earned her Ph.D. in Physical Chemistry from the University of California, Riverside in 2018, and her B.S. in Chemistry from the University of Science and Technology of China in 2013. Dr. Hu’s research group develops computational frameworks that integrate quantum mechanical modeling with machine learning to enable accurate and efficient studies of complex interfaces and quantum materials. Her research has been supported by the National Science Foundation (NSF), the U.S. Department of Energy, and the American Chemical Society Petroleum Research Fund. She is the recipient of the 2025 NSF CAREER Award.

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