A research team has developed a computational framework that combines machine learning force field molecular dynamics (MLFF-MD) simulations with automated reaction analysis to reveal the initial formation of the solid electrolyte interphase (SEI) on lithium metal at the atomic scale. The SEI plays a critical role in the performance, lifetime, and safety of lithium metal batteries, yet its formation process has remained difficult to observe directly.
Using a large-scale interfacial model containing approximately 4,000 atoms, the team simulated electrolyte decomposition and SEI formation beyond the size and time scales accessible to conventional first-principles MD. Automated reaction analysis precisely tracked bond formation and dissociation, reaction pathways, and decomposition products throughout the simulations.
The simulations reproduced experimentally reported SEI characteristics, including the preferential formation of Li2O near the lithium metal surface and LiF in the outer SEI region for concentrated LiFSI/EC electrolytes. This computational framework is expected to advance the atomic-scale understanding of electrode–electrolyte interfacial reactions and support the rational design of next-generation lithium metal batteries.
Fig. 1: Visualization of solid electrolyte interphase (SEI) formation on lithium metal using machine learning force field molecular dynamics (MLFF-MD) simulations and automated reaction analysis.
Papers
Journal: npj Computational Materials
Title: Machine learning force field molecular dynamics simulation of SEI formation on lithium metal
Authors: Norio Takenaka, Taiga Iwata, Théophane Bernhard, Atsuo Yamada