报告地点：中国科学院物理研究所D楼206会议室

邀请人：Jure Dobnikar

报告摘要：

Ab initio molecular dynamics (AIMD) has been proven to be a powerful tool to study complex chemical systems such as electrochemical interfaces [1]. Insisting on rigorous treatment of electrochemical interfaces both quantum and statistical mechanically, not only has AIMD helped resolve the microscopic structures of electric double layers under bias potential [2], often in collaboration with in situ spectroscopic characterization, but also demonstrated that water adsorption on metal electrodes like Pt has significant impact on dielectric properties of the interfaces, leading to negative capacitive response and thus bell-shaped differential capacitances of Helmholtz layers [3].

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. The prerequisite is that the machine learning potential (MLP), often short-sighted in the common implementations, should be able to accurately capture long-range electrostatics, as well as both local and non-local dielectric responses of electrode-electrolyte interfaces. In this talk, I will present our recent effort in developing such an electrochemical MLP (ec-MLP) that utilizes a hybrid scheme combining Wannier localization and polarizable electrode method to account for polarization of the interface [4]. The accuracy of the ec-MLP has been validated against AIMD simulation of electrified Pt water interface, reproducing the bell-shaped differential capacitive curve.

报告人简介：

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 a Deputy Editor of The Journal of Chemical Physics, and has received the Alexander Kuznetsov Prize for Theoretical Electrochemistry of the International Society of Electrochemistry.