Machine learning-driven breakthroughs in water electrolysis and supercapacitors

Abstract

Electrochemical energy conversion and storage have attracted widespread interest as green and sustainable technologies. In particular, research on water electrolysis and supercapacitors (SCs) has experienced significant growth, focusing on novel electrodes/electrocatalysts with prominent performances. Recently, computational frameworks employing machine learning (ML) algorithms have revitalized the targeted design of advanced nanomaterials as electrodes/electrocatalysts with tunable electronic configurations and superior reactivity. Descriptor-based analysis has proven efficient in elucidating the structure–property (e.g., activity, selectivity, and stability) relationships, addressing the complex interactions between the catalytic surface and reactant species and predicting enormous data sets. In this contribution, we present an overview of ML-driven electrode/electrocatalyst design, highlighting several novel algorithms and descriptors. The latest advancements in ML approaches are presented to efficiently screen a wide range of metal-based materials. Leveraging recent achievements, this review describes the application of ML for the discovery of active and durable nanomaterials, including identifying active sites, manipulating compositions at the atomic level, predicting the structure/performance, and optimizing thermodynamic properties as well as kinetic barriers. Moreover, recent milestones and state-of-the-art progress in ML integration strategies-materials informatics to stimulate the design of highly efficient electrode/electrocatalyst systems for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and SCs are discussed. Finally, we highlight potential future directions for uncovering the revolutionary potential of ML in boosting sustainability and prediction efficiency in the electrochemical energy conversion and storage sector. This review intends to reinforce the junctions between industry and academia and merge endeavors from fundamental understanding to technological execution.