Reaction and Ionic Migration at the Electrode-Electrolyte Interface in Solid State Batteries from Machine Learning Molecular Dynamics

Atomistic-level understanding of the chemical reactions forming the solid-electrolyte interphase (SEI) in solid-state lithium batteries has remained challenging, primarily due to the limited resolution in experimental techniques and the insufficient accuracy in large-scale simulations. In this work, we combine on-the-fly active learning based on Gaussian Process regression (FLARE) with local equivariant neural network interatomic potentials (Allegro) to construct a machine-learning force field (MLFF) to perform large-scale long-time explicit reactive simulation of a complete symmetric battery with ab initio accuracy. The MLFF is validated with experimental values of mechanical properties of bulk lithium and diffusion coefficient of solid electrolyte. For the symmetric battery, atomic descriptors are used to identify the reaction products and estimate the evolution of the SEI. We observe prominent fast reactions at the interface and characterize the dominant reaction products along with their evolution time scales. The methods in this study exhibit great potential in revealing atomistic mechanisms in complicated systems and provide insights for the development of solid-state lithium batteries.