Dr. Hongliang Xin

Virgina Tech

Host: Dr. Wenyu Huang

Physcial and Theoretical Chemistry

"From Alchemy to AI: Harnessing the Power of Data for Catalytic Materials Design"

Abstract:

Finding catalytic materials with optimal properties for sustainable chemical and energy transformations is one of the pressing challenges faced by our society today. Traditionally, the discovery of catalysts or the philosopher’s stone of alchemists relies on a trial-and-error approach with physicochemical intuition. Decades-long advances in science and engineering, particularly in quantum chemistry and computing infrastructures, popularize a paradigm of computational science for materials discovery. However, the brute-force search through a vast chemical space is hampered by its formidable cost. In recent years, machine learning (ML) has emerged as a promising approach to streamline the design of active sites by learning from data. In this talk, we present an interpretable ML framework for accelerating catalytic materials design, particularly in driving sustainable carbon, nitrogen, and oxygen cycles. We will discuss existing challenges and opportunities of ML in predicting catalytic materials, and more importantly, on advancing catalysis theory beyond conventional wisdom. We envision future directions in developing highly accurate, easily explainable, and trustworthy ML strategies, facilitating the maturation of the data science paradigm for sustainability through catalysis.

Bio:

Hongliang Xin received his B.S. degree from Tianjin University in 2002, M.S. degree from Tsinghua University in 2005, and Ph.D. degree from the University of Michigan at Ann Arbor in 2011, all in Chemical Engineering. After postdoc at Stanford/SLAC, he joined the Department of Chemical Engineering in 2014 at Virginia Tech, where he is currently an associate professor. His research focus is the development of an interpretable machine learning framework for advancing theory of heterogeneous catalysis and catalytic materials discovery. He received the recognition from the Journal of Materials Chemistry A as one of the 2017 Emerging Investigators. He received the Dean’s award for Outstanding New Assistant Professor in 2018 and Engineering Faculty Fellow in 2019. He is one of the 2019 Class Influential Researchers from ACS Industrial & Engineering Chemistry Research. He is the recipient of the prestigious NSF CAREER Award (2019). You can find details of the Xin research group at https://xingroup.org/.