Hongliang Xin (Virginia Tech): Schmidt AI in Science Speaker Series
Organized by the University of Chicago’s Eric and Wendy Schmidt AI in Science Fellowship Program.
Agenda
4:00pm – 4:45pm: Presentation
4:45pm – 5:00pm: Q&A
5:00pm – 5:30pm: Reception
Meeting location
William Eckhardt Research Center. Room 401
5640 S Ellis Avenue, Chicago, IL 60637
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Abstract: This seminar will highlight recent efforts to build explainable and agentic artificial intelligence (AI) systems for catalysis science and catalytic materials discovery. Prof. Hongliang Xin will discuss how agentic AI and machine learning workflows can accelerate the discovery of catalytic nanostructures by integrating domain knowledge, data-driven modeling, and iterative decision-making. The talk will emphasize how such approaches can improve both efficiency and scientific insight, and how they may open new opportunities toward sustainable chemistry.
Bio: Hongliang Xin is Professor of Chemical Engineering at Virginia Tech, where he has been a faculty member since 2014. His research focuses on developing an explainable artificial intelligence (AI) platform for catalysis science. Xin received his Ph.D. in Chemical Engineering from the University of Michigan and completed postdoctoral research at Stanford/SLAC. He is a recipient of the 2019 NSF CAREER Award and has been recognized as an Emerging Investigator by Journal of Materials Chemistry A and as an Influential Researcher by ACS Industrial & Engineering Chemistry Research. He serves on the Editorial Board of Chem Catalysis and has contributed to the field through editorial leadership, including guest-editing special issues for the Journal of Catalysis and the Journal of Chemical Physics on data science for catalysis. He is Communications Director of the North American Catalysis Society (NACS). He initiated and co-chaired the 2024 AI for Multidisciplinary Exploration and Discovery (AIMED) Workshop on Heterogeneous Catalysis. He also served as Scientific Co-Chair of the 2025 North American Catalysis Society Meeting (NAM29) in Atlanta and is Co-Chair of the inaugural 2026 Gordon Research Conference on AI for Materials, Energy, and Chemical Sciences (AIMECS).
