Electrochemical energy conversion and storage processes with machine learning

Abstract

The integration of artificial intelligence (AI)–machine learning (ML) in the field of electrochemistry is expected to reduce the burden of time and cost associated with experimental procedures. The application of AI–ML has pioneered a novel approach and has heralded a paradigm shift in catalyst development, optimization of operational conditions, prediction of battery lifespan, and the development of innovative descriptors. This review delves deep into these critical objectives, highlighting the intersection of AI–ML in the fields of water electrolysis, fuel cells, batteries, and carbon dioxide reduction. This review also underscores the potential of AI–ML to bridge theoretical computations with practical applications and to advance the electrochemical field. © 2024 Elsevier Inc.