Machine learning force field molecular dynamics simulation of SEI formation on lithium metal

Lithium metal batteries represent a cornerstone technology for next-generation energy storage systems owing to their exceptional energy density. However, the extreme reactivity of lithium metal drives continuous parasitic reactions with electrolytes, causing interfacial instability and dendritic growth that severely hinder practical deployment. Formation of a protective solid electrolyte interphase (SEI) through electrolyte reduction is essential for stabilizing the lithium metal surface, yet the underlying mechanisms remain poorly understood due to the scarcity of experimental techniques capable of accurately probing highly reducing interfacial environments. Here we investigate the formation processes of SEI on lithium metal at the atomistic scale using molecular dynamics simulations based on a machine learning force field with first-principles accuracy, covering unprecedented spatial–temporal scales including chemical reactions as rare events. Furthermore, an automatic reaction identification program developed in this work enables systematic extraction of key kinetic information such as the identification, sequence, and probability of elementary reactions, as well as the formation rates of decomposition products. Typical experimental observations on the characteristic SEI structure, consisting of an inner oxide-rich layer and an outer fluorinated layer, were successfully reproduced. This simulation methodology provides an effective predictive framework for the initial stage of SEI formation and offers guiding principles for the rational design of stable lithium metal batteries.