Machine Learning-Guided Modulation of Li⁺ Solvation Structures towards Optimal Electrolyte Systems for...

Area of Science:

• Electrochemistry
• Materials Science
• Battery Technology

Background:

• Electrolytes are crucial in lithium-oxygen batteries (LOBs), influencing solid electrolyte interphase (SEI) formation and lithium peroxide (Li₂O₂) growth.
• Precisely controlling multi-component electrolytes for balanced anode and cathode reactions in LOBs is a significant challenge.

Purpose of the Study:

• To develop advanced electrolytes for LOBs using Bayesian optimization.
• To achieve a stable, inorganic-rich SEI and controlled Li₂O₂ morphologies.
• To enhance overall LOB performance through electrolyte modulation.

Main Methods:

• Employed Bayesian optimization for electrolyte development.
• Investigated solvation structures of Li⁺ in dual-solvent electrolytes.
• Evaluated SEI composition and Li₂O₂ morphology in optimized LOBs.

Main Results:

• Developed a dual-solvent electrolyte that promotes a stable, inorganic-rich SEI.
• Achieved modulated Li₂O₂ morphologies on the cathode.
• Optimized LOBs demonstrated a discharge capacity of 14,063 mAh g^-1 at 500 mA g^-1, significantly exceeding single-solvent electrolytes.

Conclusions:

• The solvation structure of electrolytes critically impacts LOB performance.
• Bayesian optimization is an effective strategy for designing advanced LOB electrolytes.
• This work provides theoretical guidance for electrolyte composition modulation in LOBs.