Redefining atomistic simulations of all-solid-state batteries through machine learning interatomic potentials

All-solid-state batteries (ASSBs) represent a next-generation energy storage technology, offering enhanced safety, higher energy density, and improved cycling stability compared to conventional liquid-electrolyte-based lithium-ion batteries. Understanding and optimizing the complex chemistries and interfaces that underpin ASSB performance present significant challenges from both experimental and modeling perspectives. In particular, atomistic simulations face difficulties in capturing the complex structure, disorder, and dynamic evolution of materials and interfaces under practically relevant conditions. While established methods such as density functional theory and classical force fields have provided valuable insights, some questions remain difficult to address, particularly those involving large system sizes or long timescales. Recently, machine learning interatomic potentials (MLIPs) have emerged as a transformative tool, enabling atomistic simulations at length and time scales that were previously challenging to access with conventional approaches. By delivering near first-principles accuracy with much greater efficiency, MLIPs open new avenues for large-scale, long-timescale, and high-throughput simulations of solid-state battery materials. In this review, we present a comparative overview of density functional theory, classical force fields, and MLIPs, highlighting their respective strengths and limitations in ASSB research. We then discuss how MLIPs enable simulations that reach longer timescales, larger system sizes, and support high-throughput calculations, providing unique insights into ion transport and interfacial evolution in ASSBs. Finally, we conclude with a summary and outlook on current challenges and future opportunities for expanding MLIP capabilities and accelerating their impact in solid-state battery research.