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<p>Accurately understanding the impact of solid electrolyte interphase (SEI) on lithium deposition is critical for high-energy lithium metal batteries. Yet traditional strategies, focusing solely on isolated components, fail to capture multi-constituent synergies and underlying mechanisms. To address this challenge, we introduce the concept of SEI omics and establish a dataset of cryogenic transmission electron microscopy images combined with co-localized component information. By integrating interpretable machine learning and physics-based feature selection, we decoupled the roles of SEI constituents, revealing that higher N/S/P/F content and reduced O in the SEI improve lithium deposition. Combined density functional theory and electrochemical phase-field modeling uncovered multi-scale effects of SEI components on Li growth. Results confirm that designing an inner SEI layer with high surface energy and migration ability significantly refines deposition morphology. Guided by machine learning-optimized composition, a highly disordered SEI was engineered, achieving high average Coulombic efficiency of 99.35% over 800 cycles for Li||Cu cell at 1 mA cm<sup>−2</sup> and 1 mAh cm<sup>−2</sup>. This work establishes a universal framework for understanding SEI-coupled effects on lithium growth, offering transformative strategies for electrolyte and interface design.</p>

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Decoding single-crystal lithium growth through solid electrolyte interphase omics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月22日

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  • Gongxun Lu,
  • Zhiyuan Han,
  • Lei Shi,
  • Zhilong Wang,
  • Mengtian Zhang,
  • Xinru Wu,
  • Zhihong Piao,
  • Xiao Xiao,
  • Shengyu Tao,
  • Jianwei Nai,
  • Zhijin Ju,
  • Xuan Zhang,
  • Yanqiang Han,
  • Karl Luigi Loza Vidaurre,
  • Hongyan Fu,
  • Jinjin Li,
  • Xinyong Tao,
  • Guangmin Zhou

摘要

Accurately understanding the impact of solid electrolyte interphase (SEI) on lithium deposition is critical for high-energy lithium metal batteries. Yet traditional strategies, focusing solely on isolated components, fail to capture multi-constituent synergies and underlying mechanisms. To address this challenge, we introduce the concept of SEI omics and establish a dataset of cryogenic transmission electron microscopy images combined with co-localized component information. By integrating interpretable machine learning and physics-based feature selection, we decoupled the roles of SEI constituents, revealing that higher N/S/P/F content and reduced O in the SEI improve lithium deposition. Combined density functional theory and electrochemical phase-field modeling uncovered multi-scale effects of SEI components on Li growth. Results confirm that designing an inner SEI layer with high surface energy and migration ability significantly refines deposition morphology. Guided by machine learning-optimized composition, a highly disordered SEI was engineered, achieving high average Coulombic efficiency of 99.35% over 800 cycles for Li||Cu cell at 1 mA cm−2 and 1 mAh cm−2. This work establishes a universal framework for understanding SEI-coupled effects on lithium growth, offering transformative strategies for electrolyte and interface design.