It is known that electrode materials undergo dynamic structural changes at in-situ/in-operando conditions. Yet, the majority of computational studies only consider the static structures of electrode materials. When the materials are submerged in liquid solution, dynamic solvation effects are often completely ignored, or treated with dielectric continuum models, often lacking validation. The situations are about to change. Thanks to the latest development of in-situ experimental techniques and state-of-the-art computational methods, dynamics of electrode materials has recently drawn more and more attentions in many research areas. In this talk, I will present our recent progress on modeling dynamic catalysis and electrochemistry using ab initio molecular dynamics (AIMD). The high computational cost of AIMD however limits its application to small model systems consisting of hundreds of atoms at timescale of tens of ps. While, the latest development of AI accelerated AIMD (AI2MD) significantly increases the size and timescale, showing great promise for in situ modeling of realistic electrochemical systems.
Jun Cheng received his PhD in theoretical chemistry from the Queen’s University Belfast in 2008. He spent five years at University of Cambridge as a postdoc in theory sector in chemistry, and a junior research fellow at Emmanuel College. He is currently a Professor at College of Chemistry and Chemical Engineering, Xiamen University. His research interests are computational electrochemistry and theoretical catalysis. In particular, his group focuses on developing computational methods combining electronic structure theory, machine learning potential and molecular dynamics to simulate electrochemical interfaces and catalyst dynamics. He has been awarded the National Science Fund for Distinguished Young Scholars. He is an Associate Editor of The Journal of Chemical Physics, and has received the Alexander Kuznetsov Prize for Theoretical Electrochemistry of the International Society of Electrochemistry.
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