Machine learning interatomic potentials for lithium battery electrolyte design

Abstract Lithium‐ion batteries have dominated the energy storage landscape for decades, driven by their high energy density and operating voltage. However, increasing demands for wide operating temperature ranges and fast charging push current technologies to their limits, requiring next‐generation lithium batteries with superior safety and extended cycle life. In this context, the rational design of electrolytes remains a major challenge, as intricate bulk and interfacial mechanisms govern system viability. Specifically, solid‐state electrolytes struggle with low ionic conductivity, while liquid electrolytes are limited by complex chemical interactions. Machine learning interatomic potentials (MLIPs) address these issues by combining first‐principles accuracy with computational efficiency. These models allow us to simulate atomistic interactions and interfacial reactions that were previously impractical. We summarize recent advances in the application of MLIPs, focusing on bulk and interfacial phenomena in solid‐state and liquid electrolytes.