Recent advances in artificial intelligence boosting materials design for electrochemical energy storage

Xinxin Liu, Kexin Fan, Xinmeng Huang, Jiankai Ge, Yujie Liu, Haisu Kang

Abstract: In the rapidly evolving landscape of electrochemical energy storage (EES), the advent of artificial intelligence (AI) has emerged as a keystone for innovation in material design, propelling forward the design and discovery of batteries, fuel cells, supercapacitors, and many other functional materials. This review paper elucidates the burgeoning role of AI in materials from foundational machine learning (ML) techniques to its current pivotal role in advancing the frontiers of materials science for energy storage, including enhancing the performance, durability, and safety of battery technologies, fuel cell efficiency and longevity, and the materials fine-tuning in supercapacitors to achieve superior energy storage capabilities. Collectively, we present a comprehensive overview of the recent AI advancements that have significantly accelerated the development of next-generation materials for EES, offering insights into future research trajectories and the potential for AI to unlock new horizons in materials science.