Reaction and Ion Transport at Solid-State Battery Electrode-Electrolyte Interface 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 difficulty of experimental characterization techniques for buried interfaces and the insufficient speed and accuracy in previously available 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 first-principles machine-learning force field (MLFF) to perform large-scale long-time explicit reactive simulation of a complete symmetric battery cell. We observe prominent fast reactions and interdiffusion at the interface and characterize the dominant reaction products along with their evolution time scales, using unsupervised learning techniques based on atomic geometry descriptors. Our simulation reveals the kinetics and the passivation involved in the chemical reaction responsible for the SEI formation. Remarkably, we observe formation of phases different from those predicted by thermodynamics, illustrating the importance of explicit modeling of kinetics. The methods in this study are promising for accelerated analysis of atomistic mechanisms in complex heterogeneous scenarios, such as solid state synthesis and stability of heterostructure, such as electrochemical systems.