From Prediction to Materials Design: Machine Learning in Electrocatalytic Water Splitting

Abstract

Machine learning (ML) is emerging as a powerful strategy to accelerate electrocatalyst discovery for water splitting, yet its impact is still limited by data quality, thermodynamic bias, and weak coupling to realistic experiments. This perspective surveys recent ML-guided efforts on both oxygen and hydrogen evolution reactions, with emphasis on compositionally complex platforms such as layered double hydroxides, metal–organic frameworks, high-entropy alloys and related multimetal systems. It highlights how supervised models, ML–density functional theory (DFT) workflows and interpretable surrogates are being used for rapid composition screening, dopant optimization and non-linear descriptor discovery beyond simple volcano-type relations. At the same time, the article identifies structural bottlenecks including small, compositionally clustered datasets, overreliance on static adsorption energies, underrepresentation of kinetics and stability, and a persistent gap between computational predictions and performance at industrially relevant current densities. Finally, the perspective outlines opportunities for integrating active learning, operando-informed labels, physics-based and microkinetic models, and explainable artificial intelligence (SHapley Additive exPlanations, symbolic regression, counterfactual design) into closed-loop, synthesis-aware workflows, positioning ML as a mechanistically meaningful design tool for robust, earth-abundant water-splitting electrocatalysts.