Decoding single-crystal lithium growth through solid electrolyte interphase omics.

Area of Science:

• Materials Science
• Electrochemistry
• Battery Technology

Background:

• Solid electrolyte interphase (SEI) formation critically impacts lithium metal battery performance.
• Traditional studies often overlook multi-constituent SEI synergies, limiting understanding of lithium deposition mechanisms.

Purpose of the Study:

• To develop a comprehensive understanding of SEI's role in lithium deposition using an 'SEI omics' approach.
• To identify key SEI components and their influence on lithium growth dynamics.

Main Methods:

• Established a dataset combining cryogenic transmission electron microscopy (cryo-TEM) with co-localized component information.
• Integrated interpretable machine learning and physics-based feature selection to decouple SEI constituent roles.
• Employed density functional theory (DFT) and electrochemical phase-field modeling to investigate multi-scale SEI effects.

Main Results:

• Decoupled SEI roles, identifying higher N/S/P/F content and reduced O as beneficial for lithium deposition.
• Uncovered multi-scale effects of SEI components on lithium growth.
• Demonstrated that an inner SEI layer with high surface energy and migration ability refines deposition morphology.

Conclusions:

• Engineered a highly disordered SEI, guided by machine learning, achieving 99.35% average Coulombic efficiency over 800 cycles.
• Established a universal framework for understanding SEI-coupled effects on lithium growth.
• Provided transformative strategies for electrolyte and interface design in high-energy lithium metal batteries.