锂金属负极固态电解质界面膜形成和生长机理的理论研究进展

Progress of theoretical studies on the formation and growth mechanisms of solid electrolyte interphase at lithium metal anodes

锂金属负极因其极高的理论比容量在锂离子电池领域引起了极大的关注,但其高反应活性会引发电解液组分发生一系列复杂的降解反应,并在电极表面生成固态电解质界面膜(SEI)。SEI钝化层一方面能抑制电解液持续损耗,另一方面也会显著影响电池的循环性能。因此,从原子/分子层面阐明SEI形成和生长机理成为了近些年的研究重点和热点。本文综述了不同理论模拟方法在SEI结构、组分和生长过程的最新研究进展,介绍了经典分子动力学、反应力场分子动力学、第一性原理分子动力学、机器学习力场分子动力学以及动力学蒙特卡罗等模拟方法在SEI研究中的成功案例。讨论了现有理论计算方法在模拟SEI形成和生长机理方面的局限性,提出可结合机器学习和动力学蒙特卡罗方法来实现长时域SEI形成和生长过程模拟的技术方案展望。

Lithium metal anodes in lithium-ion batteries(LIBs)have garnered significant research interest due to their exceptionally high theoretical specific capacity.However,their high reactivity can trigger a series of complex degradation reactions of electrolyte components,ultimately forming a solid electrolyte interface(SEI)on the electrode surfaces.This SEI passivation layer suppresses the continuous loss of electrolytes but can significantly affect the cycling performance of the LIBs.Consequently,understanding the formation and growth mechanisms of SEI at the atomic/molecular level has become a major research focus in recent years.This study summarizes the latest research progress on the structure,composition,and growth process of SEI using various theoretical approaches.Particularly,it introduces some successful examples of classical molecular dynamics,reactive force field molecular dynamics,first-principles molecular dynamics,machine learning force field molecular dynamics,and kinetic Monte Carlo methods in SEI modeling.In addition,this study discusses the limitations of current theoretical approaches in simulating the formation and growth mechanisms of SEI.Finally,this study proposes that combining machine learning methods and kinetic Monte Carlo approaches to enhance the simulations of the formation and growth processes of SEI over long-time domains.