Data-Driven Design of Ru-Based Electrocatalysts for Accelerated Alkaline Hydrogen Evolution

Abstract

Anion exchange membrane water electrolyzers (AEMWEs) are promising for hydrogen production, yet their performance is bottlenecked by the alkaline hydrogen evolution reaction (HER) with sluggish kinetics induced by high water dissociation barriers and imbalanced H*/OH* adsorption-desorption. Herein, interpretable machine learning (ML) is exploited as a core tool for precise catalyst structure optimization, guiding the fabrication of a Ru2Ni3-carbon nanotubes (CNTs) hybrid catalyst. The ML-engineered catalyst exhibits high HER activity, with an ultra-low overpotential of 14 mV at 10 mA cm–2 and a Tafel slope of 34.3 mV dec–1. When integrated into an AEMWE with a NiFe-LDH anode, the system achieves 1.86 V at 1 A cm–2 (80 °C, no iR correction) and maintains stability for 200 h. Experimental and theoretical studies confirm that the ML-tailored Ru2Ni3-CNTs synergy modulates d-band centers, reduces reaction barriers, and optimizes intermediate adsorption, highlighting ML’s pivotal role in rational electrocatalyst design for advanced AEMWEs.