From prediction to materials design: machine learning in electrocatalytic water splitting

-informed labels, physics-based and microkinetic models, and explainable artificial intelligence (SHapley Additive exPlanations, symbolic regression, and counterfactual design) into closed-loop, synthesis-aware workflows, positioning ML as a mechanistically meaningful design tool for robust, earth-abundant water-splitting electrocatalysts.