Machine-Learning-Guided Chemical Metathesis for In Situ Construction of High-Entropy Alloy Interphases in Li-Metal...

Area of Science:

• Materials Science
• Electrochemistry
• Computational Materials Science

Background:

• Stabilizing lithium metal anodes is crucial for next-generation batteries.
• High-entropy alloys offer potential for improved anode stability.
• Scalable fabrication methods are needed for practical applications.

Purpose of the Study:

• To design and fabricate a high-entropy alloy for stabilizing lithium metal anodes.
• To optimize alloy composition using machine learning and DFT.
• To develop a scalable method for in situ alloy formation on Li metal.

Main Methods:

• Machine learning and density functional theory (DFT) for alloy screening.
• Chemical metathesis for in situ synthesis of the high-entropy alloy.
• Electrochemical testing of symmetric and asymmetric cells.

Main Results:

• Identified an optimized Fe-Co-Ni-Cu-Zn high-entropy alloy with desirable properties.
• Developed a mild, scalable method for creating a lithiophilic alloy on Li metal.
• Achieved stable cycling of 900 hours at 10 mA cm⁻² in symmetric cells.
• Demonstrated improved performance in asymmetric cells with various cathodes.

Conclusions:

• The developed high-entropy alloy effectively stabilizes the Li metal anode.
• The in situ fabrication method is scalable and promotes uniform Li plating/stripping.
• This approach significantly enhances battery cycle life and electrochemical stability.