Data-Driven Knowledge Discovery Reveals Quantitative Electrolyte Design Rules for Anode-Free Sodium Metal Batteries

Area of Science:

• Electrochemistry
• Materials Science
• Data Science

Background:

• Sodium-based batteries offer a low-cost, abundant alternative for large-scale energy storage.
• Anode-free designs boost energy density but demand electrolytes for efficient sodium plating and stripping.
• Achieving high Coulombic efficiency (CE) is critical for practical sodium battery performance.

Purpose of the Study:

• To establish quantifiable design rules for high-CE electrolytes using a machine learning framework.
• To identify key electrolyte properties influencing sodium plating/stripping reversibility.
• To develop and validate a novel electrolyte for high-performance anode-free sodium batteries.

Main Methods:

• An interpretable machine learning-driven knowledge discovery framework was employed.
• Analysis focused on solvent properties (oxygen content, nonpolar surface area) and their effect on solvation structure.
• An optimal electrolyte was designed based on derived principles and tested in electrochemical cells.

Main Results:

• The study identified that reduced solvent oxygen and increased nonpolar surface area promote anion-dominated solvation.
• This leads to a NaF-rich solid electrolyte interphase (SEI) and suppresses inactive sodium formation.
• The developed electrolyte achieved 99.9% average CE over 800 cycles and enabled a pouch cell with >230 Wh kg-1 energy density.

Conclusions:

• Machine learning can guide the rational design of electrolytes for sodium batteries.
• Electrolyte design principles were established linking solvation, SEI chemistry, and electrochemical performance.
• This work paves the way for high-performance, sustainable sodium energy storage solutions.