Public Research Seminar by Advanced Materials Thrust, Function Hub - Theory- and Data-driven materials design and discovery
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Catalyst design will be one key element to storing and utilizing renewable energy. Catalyst active sites are elementally and structurally diverse; and data sets spanning this large chemical space are sparse. Open scientific questions remain on how best to model catalyst activity and how fast to screen catalysts in a large chemical space.
In this talk, Dr. Ou will discuss the concepts of theory- and data-driven catalysts design and discovery. Specifically, two applications will be presented. First, research on developing sophisticated models to predict the catalyst performance and identify the reaction mechanism more accurately, including the applications related to CO2/CO reduction, nitrogen reduction reaction, and oxygen evolution reaction, among others. Next, research on methodological development in AI/ML towards fast and accurate predict and rational design of catalysts, involving the graph theory and Bayesian optimization.
Dr. Ou received his Ph.D. in Materials Engineering at McGill University in 2020 (M.Eng. 2015 Central South University, B.Eng. 2012 Central South University), with an emphasis on computational chemistry, artificial intelligence/machine learning, and heterogeneous catalysis. He was a postdoctoral fellow in the Department of Electrical and Computer Engineering funded by the Climate Positive Energy at the University of Toronto. He joined the Northwestern University as a Research Associate in November 2022, and he currently is the lead computationalist in the Prof. Edward Sargent group in the Department of Chemistry.
He has published 73 papers in peer-reviewed journals with citations of 1994 and a h-index of 22 (Google Scholar by November 2023), including 34 papers as first/corresponding author in prestigious journals: 5 Nat. Catal., 1 Nat. Energy, 2 Nat. Commun., 2 J. Am. Chem. Soc., 1 Sci. Adv., 2 Proc. Natl. Acad. Sci., and 1 ACS Catal., among others.