Title: AIEmpowered Innovation of Renewable Energy Conversion and Storage Materials

Speaker: Prof. Zhen Zhou

Invited by: Prof. Zhiming Cui

Time: October 30, 2026 (Friday), 10:00 AM

Venue: Room 445, Building B8, University Town Campus

Organizer: School of Chemistry and Chemical Engineering, Guangdong Provincial Key Laboratory of Fuel Cell Technology

Biography

 Zhen Zhou received his B.Sc. degree from Nankai University in 1994 and his Doctor of Science degree from Nankai University in 1999, after which he joined the faculty of Nankai University. He was promoted to Professor at the end of 2010 and became a doctoral supervisor in 2011. He served as Director of the Molecular Science Computation Center of Nankai University starting in 2012 and Director of the Institute of New Energy Material Chemistry of Nankai University from 2014. He was selected as a Changjiang Distinguished Professor under the Ministry of Education Changjiang Scholar Program in 2019. He moved to Zhengzhou University in 2021 and served as Dean of the School of Chemical Engineering from 2022 to 2025. He was honored as a Zhongyuan Scholar in 2025. Currently, he is the Zhishan Distinguished Professor at the School of New Energy and Intelligent Manufacturing, Henan University.

 He has published more than 400 papers in domestic and international journals. His publications have been cited over 56 000 times with an hindex of 132. He has been listed in Elsevier’s China Most Cited Researchers for 12 consecutive years (20142025) and in Clarivate’s Highly Cited Researchers for 8 consecutive years (20182025). He was elected Fellow of the Royal Society of Chemistry (FRSC) in 2020. He presently serves as Associate Editor for Journal of Materials Chemistry A and Green Energy & Environment, Editorial Board Member for Journal of Power Sources, Batteries & Supercaps, and Journal of Rare Earths.

Abstract

 This presentation focuses on cuttingedge research concerning the application of Artificial Intelligence (AI) in renewableenergyconversion and energystorage materials. Combining database resources with machinelearning techniques, efficient screening and rational design of electrocatalysts are realized. A multiagent collaborative framework built upon largelanguage models is adopted for intelligent recommendation of potential electrocatalysts for electrochemical ammonia synthesis. AIdriven multiscale simulation approaches, exemplified by PVDFbased polymer electrolytes, are employed to thoroughly uncover lithiumion transport mechanisms, delivering theoretical foundations for optimized material design and performance improvement of solidstate batteries.

Announced by School of Chemistry and Chemical Engineering.