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 30 first/co-first/corresponding authored papers (62 total) in leading journals like 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 over 2500 citations and an h-index of 25. Moreover, he has served as reviewer for 30+ prestigious peer-reviewed journals such as Phys. Rev. Lett., Nat. Commun., Adv. Theory Simul. and etc.