Atomic-scale tracking of sodium metal-electrolyte reactions via adaptive machine learning force fields
摘要
High-capacity and cost-effective sodium (Na) metal anode receives increasing attention for constructing high-energy-density metal batteries. However, the unstable solid electrolyte interphase (SEI) that forms on Na metal anodes drives detrimental dendrite growth and capacity fade, and its formation mechanisms remain poorly understood. Herein, an accelerated on-the-fly learning (AOFL) approach is introduced to uncover the mechanistic underpinnings of SEI formation. By combining conventional on-the-fly learning with similarity structure screening, AOFL achieves 71% faster simulations than ab initio molecular dynamics while maintaining comparable accuracy. The ClO4− decomposition forms Na2O during the interfacial reaction simulation, while proton ion from 1,2-dimethoxyethane (DME) by reactive oxygen leads to NaOH formation, both of which are identified as critical inorganic SEI components. These insights afford theoretical guidance for elucidating SEI formation mechanisms and for the rational design of advanced electrolytes.