Machine learning-accelerated discovery of multi-cation entropy-stabilized NASICON solid electrolytes with 10,000 hours of stable Na plating/stripping for all-solid-state sodium batteries

Key Points

• This research aims to discover and optimize multi-cation entropy-stabilized solid-state electrolytes for sodium batteries.
• Utilized machine learning algorithms to explore compositions of entropy-stabilized materials.
• Assessed stability and performance of newly discovered electrolytes over 10,000 hours of sodium plating/stripping.
• Analyzed compositional space to determine optimal cation combinations.
• Identified several new solid-state electrolytes with enhanced stability for sodium batteries.
• Demonstrated 10,000 hours of stable sodium plating/stripping with select materials.
• Validated the role of multi-cation strategies in improving battery performance.