Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects

Abstract

Advancement of batteries is indispensable for further utilization of renewable energy sources to meet the increasing energy demand. The rapid development of machine learning (ML) approaches has propelled innovation across diverse domains, fundamentally reshaping the landscape of energy storage research. This comprehensive and authoritative discussion critically examines the application of artificial intelligence (AI) and ML techniques for the design of materials for various battery systems by navigating a large material space. We emphasize recent progress in the battery field propelled by ML, describe existing and forthcoming hurdles, and elucidate the prerequisites for optimizing ML methodologies. We also provided future directions and potential research areas in the application of advanced ML techniques for the optimization of battery systems. The goal is to facilitate the transfer of these advanced AI/ML tools to researchers involved in battery design research, fostering a comprehensive understanding of their potential and embracing the multifaceted aspects of battery research. © 2025 American Chemical Society.