09/06/2023
By Fanglin Che

Guest: Hongliang Xin, Ph.D., Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA
Thursday, Sept. 14, 4-5 p.m., Shah Hall 310

Title: Machine Learning for Accelerating Catalytic Materials Discovery

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.

Biography:

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 in 2011, all in Chemical Engineering. After completing postdoctoral research at Michigan and Stanford/SLAC, he joined the Department of Chemical Engineering at Virginia Tech in 2014 and was promoted to Associate Professor in 2020. His research group focuses on the development of an interpretable machine learning framework for advancing catalysis theory 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 at the Xin research group.