Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Lithium-metal batteries (LMBs) offer high specific energy but face challenges with electrode-electrolyte interface stability and lithium electrode reactivity.
• Traditional "trial-and-error" methods for improving LMBs are time-consuming and expensive.
• Machine learning (ML) presents a paradigm shift for understanding complex structure-performance relationships in materials discovery.

Purpose of the Study:

• To review the applications of ML in discovering electrolytes, electrodes, and interface engineering for LMBs.
• To highlight ML-driven workflows for materials investigation, including data collection, feature engineering, and simulations.
• To outline future directions for ML in advancing stable lithium electrode-electrolyte interfaces.

Main Methods:

• Review of ML applications in electrolyte and electrode discovery for LMBs.
• Emphasis on ML-driven workflows: data collection, feature engineering, model selection, and ML-assisted simulations.
• Highlighting task-oriented ML for materials screening, descriptor extraction, mechanistic elucidation, and reverse design.

Main Results:

• ML effectively captures complex structure-performance relationships across vast material spaces.
• ML accelerates the identification of optimal materials and interfaces for LMBs.
• ML facilitates mechanistic understanding and reverse design of novel components.

Conclusions:

• ML is a powerful tool for overcoming challenges in LMB development, particularly interface stabilization.
• ML-driven approaches significantly expedite the discovery and design of stable electrode-electrolyte interfaces.
• Future ML integration with multiscale simulations and intelligent platforms will catalyze rational design for long-lasting LMBs.