CBE Colloquia - AI for Electroplating: Data-Driven Control of Interfaces and Functional Surfaces
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Electroplating is a powerful method for constructing functional surfaces, yet its advancement depends on understanding and controlling complex interfacial processes. In this talk, I will present our efforts to transform electroplating into a data-driven science through systematic studies of additive–interface interactions, coupled with machine learning and closed-loop discovery. These insights enable predictive control of surface morphology and open pathways to functional electroplated materials for electrocatalysis. I will further highlight our development of robotic platforms guided by large language models (LLMs), which translate natural language into executable experiments and accelerate the exploration of inorganic synthesis. Together, these approaches illustrate how electroplating, when integrated with data science and automation, can evolve into a modern strategy for designing advanced functional surfaces.
Dr. Wang graduated from the College of Chemistry and Molecular Engineering at Peking University in 2009 and received his Ph.D. from the University of North Carolina at Chapel Hill in 2013. From 2013 to 2015, he conducted postdoctoral research at the University of Chicago. Since 2015, he has been a professor at Xiamen University. He is a recipient of the National Science Fund for Distinguished Young Scholars and serves as a Minjiang Distinguished Professor. His main research focuses on Metal–Organic Layers (MOLs) and the application of artificial intelligence technologies in catalytic chemistry. He has published more than 120 papers with over 12,000 citations. Professor Cheng Wang has received multiple honors, including the ACS Inorganic Chemistry Young Investigator Award (2013), the Chinese Chemical Society Youth Chemistry Award (2020), and the Chinese Chemical Society Nano Research Young Investigator Award (2021).