(Invited) Multiscale Modeling and Machine Learning Investigation of Battery Interphase Dynamics

Abstract

In recent years, the solid electrolyte interphase (SEI) has emerged as a critical component in the development of efficient and durable electrochemical storage systems. Understanding the dynamics and properties of SEI is paramount for the enhancement of battery performance and longevity. This study presents a novel multi-scale simulation approach, incorporating Hybrid Ab Initio Reactive Molecular Dynamics (HAIR), to elucidate the intricate mechanisms governing SEI formation and evolution. Our methodology involves simulation under operando conditions, closely mimicking real-world scenarios, to provide insights into the molecular interactions and structural changes within the SEI layer. By leveraging HAIR, we bridge the gap between quantum mechanical precision and the expansive temporal-spatial scales of molecular dynamics, facilitating a more comprehensive understanding of SEI behavior [1,2]. Furthermore, we integrate machine learning (ML) techniques to refine our predictive models. These ML algorithms are trained on a rich dataset derived from HAIR simulations, enabling the prediction of key electrochemical properties, including chemical composition and electronic structure. The application of machine learning significantly enhances our ability to forecast SEI evolution under various operational conditions. A critical aspect of our research is the prediction of X-ray photoelectron spectroscopy (XPS) and Coulombic Efficiency (CE) outcomes [3]. By simulating these properties, we provide valuable predictions that align closely with experimental data, thereby validating our simulation framework. In summary, our study offers a groundbreaking perspective on SEI analysis, merging the fidelity of HAIR with the predictive power of machine learning. This approach not only deepens our fundamental understanding of SEI properties but also paves the way for the development of more efficient and durable electrochemical storage systems. Our findings hold significant implications for the future of battery technology and underscore the importance of advanced simulation techniques in electrochemical research. Reference 1. Liu Y; Yu PP; Sun QT; Wu Y; Xie M; Yang H; Cheng T*; Goddard WA; ACS Energy Lett. 2021, 6, 2320.2. Liu Y; Sun QT; Yue BT; Zhang YY; Cheng T*; J. Mater. Chem. A 2023, 11, 14640.3. Sun QT; Xiang Y; Liu Y; Xu L; Leng TL; Ye YF; Fortunelli A*; Goddard WA*; Cheng T*; J. Phys. Chem. Lett. 2022, 13, 8047. Figure 1