Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium Batteries

Solid-state lithium batteries have attracted considerable research attention for their potential advantages over conventional liquid electrolyte lithium batteries. The discovery of lithium solid-state electrolytes (SSEs) is still undergoing to solve the remaining challenges, and machine learning (ML) approaches could potentially accelerate the process significantly. This review introduces common ML techniques employed in materials discovery and an overview of ML applications in lithium SSE discovery, with perspectives on the key issues and future outlooks.

Automated summary

Solid-state lithium batteries have received significant research attention for their potential advantages over conventional liquid electrolyte lithium batteries. The discovery of lithium solid-state electrolytes (SSEs) is still ongoing to address remaining challenges, and machine learning (ML) approaches could greatly expedite this process.