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Unveiling Mechanisms of SEI Formation and Sodium Loss in Sodium Batteries via Interface Reactor Sampling

  • Zhoulin Liu
  • , Ziliang Wang*
  • , Zherui Chen
  • , Jianchun Sha
  • , Fengzijun Pan
  • , Pingyang Zhang
  • , Yinghe Zhang*
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Shandong University
  • Harbin Institute of Technology Shenzhen
  • Shenzhen University
  • Northeastern University China
  • School of Transportation Science and Engineering, Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

The solid electrolyte interphase (SEI) critically dictates the cyclability and Coulombic efficiency of sodium-metal batteries, yet its dynamic formation mechanisms and atomic-scale evolution during electrochemical cycling remain elusive due to the spatiotemporal limitations of existing techniques. Here, an “Interface Reactor” sampling strategy is proposed to construct a charge-aware neuroevolution potential (qNEP). This approach overcomes the instability bottlenecks of conventional machine learning potentials, enabling stable, first-principles-accurate molecular dynamics simulations of complex electrode–electrolyte interfaces on the hundred-nanosecond scale. Fundamentally distinct SEI formation mechanisms are revealed during the early stage: carbonate-based electrolytes form heterogeneous organic-inorganic matrices via “mixed co-formation,” whereas ether-based electrolytes generate dense, self-limiting inorganic barriers through “surface-energy-controlled” NaF crystallization. Metadynamics simulations further elucidate that these compositional disparities govern sodium-ion storage dynamics: NaF-rich SEIs facilitate efficient metallic deposition, while carbonate-dominated interphases induce irreversible sodium trapping and continuous electrolyte decomposition. These findings establish a comprehensive atomic-scale framework linking solvation structure, interfacial reaction networks, and electrochemical performance, providing mechanistic guidelines for rational SEI engineering in next-generation alkali-metal batteries. Crucially, a general and robust computational framework is established for simulating complex interfacial reactions in electrochemical systems.

Original languageEnglish
JournalAdvanced Science
DOIs
StateAccepted/In press - 2026
Externally publishedYes

Keywords

  • interfacial reaction
  • machine learning
  • sodium batteries
  • solid electrolyte interphase

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