EXTENDED ABSTRACT: Elucidating the atomic structures of electrochemical interfaces and interphases is pivotal in advancing our understanding of various electrochemical processes. Our research introduces advanced multiscale simulation methods and a hybrid algorithm for accurately simulating the electrolyte/electrode interface and electrochemical reactions under operando conditions[1]. These approa che s have enabl ed us to elucidate the reaction mechanism of CO2 reduction on copper surfaces and to detail the atomic structure of the solid electrolyte interphase (SEI) in Lithium/Sodium metal batteries at the nanoscale[2,3]. Incorporating machine learning (ML) with physic models, we have signiffcantly enhanced the efffciency and accuracy of surface spectroscopy calculations[4], offering new insights that accelerate material design and the development of next-generation electrochemical systems. Multiscale simulations combined with machine learning have signiffcantly accelerated the development of material design and next-generation electrochemical systems[5].

Keywords: Multiscale Simulation; Electrochemical Interface; Machine Learning

REFERENCES:

[1] Cheng, T.; Goddard WA* et al., PNAS, 2019, 116, 18193 [2] Cheng, T.; Fortunelli A; Goddard WA*, PNAS, 2019, 116, 7718 [3] Liu, Y.; Cheng, T.* et al., ACS Energy Lett. 2021, 6, 2320 [4] Sun, Q.T.; Cheng, T.* et al., J. Phys. Chem. Lett. 2022, 13, 8047 [5] Jie, Y.L.; Wang, S.Y.; Weng, S.T., Liu, Y.; Cheng, T.*; Wang, X.F.*; Jiao, S.H.*; Xu, D.S., Nat. Energy, 2024, DOI: 10.1038/s41560-024-01565-z