Machine-Learning Accelerated Computational Methods Development for Electrochemical Reactions at Materials Interfaces by Asst Prof Xu Shenzhen

Interfacial electrochemistry is fundamental to the development of green energy technologies. Particularly, electrochemical reactions at materials’ interfaces determine the performance and life time of various energy-conversion/storage systems. Atomic-scale simulations act as indispensable tools to provide a microscopic understanding for these complicated processes. However, several challenges (including exact descriptions of electrochemical open systems, the rare event problem, etc.) limit the applicability and reliability of simulations. In this talk, I will introduce our group’s recent efforts on theoretical methods development for interfacial reaction simulations, further assisted by machine-learning force fields (MLFF). Application areas of electrocatalytic proton-coupled electron transfer (PCET) and battery systems will be the focus of this presentation. Specifically, an integrated framework based on the grand canonical path integral Monte Carlo algorithm is developed to exactly treat PCET considering the nuclear quantum effects under a constant potential condition. Regarding the simulation of a battery cell with counter electrodes, we also propose a novel methodology combining the MLFF with the charge equilibration method to apply bias potentials in a virtual battery. Our studies thus shed light on applications of new computational methods in broader scenarios of electrochemical systems with a good balance of efficiency and accuracy.