Biography
Dr. Xijun Wang is a Special Appointment Professor at University of Science and Technology of China. His group develops data-driven and physics-grounded methods—combining machine-learning interatomic potentials, molecular dynamics/Monte Carlo, and quantum chemistry—to understand and design porous materials and heterogeneous catalysts. Current interests include CO2 capture and conversion, methane activation, and photo/electro/thermo-catalysis, as well as generative AI-enabled inverse design and autonomous discovery workflows.
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Dr. Wang earned his Ph.D. from the University of Science and Technology of China (with Prof. Jun Jiang), was an exchange scholar at UC Santa Barbara (with Prof. Baron G. Peters), held a postdoctoral appointment at North Carolina State University (with Prof. Fanxing Li), and later worked at Northwestern University co-mentored by Prof. Randall Q. Snurr and Prof. Edward H. Sargent. He has published 100+ papers (>5,400 citations; H-index 35) and received the 2024 Outstanding Researcher Leader award at Northwestern University.
Research Overview
Dr. Wang's research interests mainly focus on developing and exploiting multiscale modeling methods such as density functional theory (DFT), combined with data-driven techniques like machine learning (ML) to help understand, design, and discover next-generation catalysts and functional materials for a broad range of pressing scientific issues related to sustainable chemical production and energy conversion and storage. He has published 70+ papers on leading journals like Science, J. Am. Chem. Soc., Energy Environ. Sci., Nat. Commun., Angew. Chem. Int. Ed., Adv. Mater., etc., covering a wide variety of interdisciplinary topics ranging from photocatalysis to chemical looping. These publications have attracted widespread attention, bringing him 3300+ citations and an h-index of 29. Moreover, he has served as reviewer for 40+ prestigious peer-reviewed journals such as Phys. Rev. Lett., Nat. Commun., Adv. Theory Simul., etc.
