Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Abstract

The solid electrolyte interphase (SEI) is a complex passivation layer that forms in situ on many battery electrodes such as lithium-intercalated graphite or lithium metal anodes. Its essential function is to prevent the electrolyte from continuous electrochemical degradation, while simultaneously allowing ions to pass through, thus constituting an electronically insulating, but ionically conducting material. Its properties crucially affect the overall performance and aging of a battery cell. Despite decades of intense research, understanding the SEI's precise formation mechanism, structure, composition, and evolution remains a conundrum. State-of-the-art computational modeling techniques are powerful tools to gain additional insights, although confronted with a trade-off between accuracy and accessible time- and length scales. In this review, it is discussed how recent advances in data-driven models, especially the development of fast and accurate surrogate simulators and deep generative models, can work with physics-based and physics-informed approaches to enable the next generation of breakthroughs in this field. Machine learning-enhanced multiscale models can provide new pathways to inverse the design of interphases with desired properties.