Machine-learning guides discovery of multi-principal element alloys as electrocatalyst for hydrogen evolution reaction

Owing to synergistic interactions among their components, multi-principal element alloys manifest remarkable physicochemical properties that render them highly promising candidates for hydrogen evolution reaction (HER) electrocatalysts. Despite extensive experimental investigations, the intricate composition of multi-principal components and the absence of systematic machine learning (ML) screening poses significant challenges in identifying optimal elemental configurations for electrocatalysts, thereby constraining the rational design and development of multi-principal alloy electrocatalysts. In this work, the NbZnCo₂ multi-principal component alloy emerges as the optimal candidate from a pool of 601 candidate alloys using the Light Gradient Boosting model, demonstrating approximately 34-fold cost efficiency enhancement over Pt/C while surpassing HER activity. Combined density functional theory (DFT) calculations and experimental validation confirmed the ML model's reliability, with the micrometer NbZnCo₂ catalyst achieving an ultralow overpotential of 20 mV at 10 mA cm−2 and remarkable stability over a period of 60 h. Furthermore, the NbZnCo₂ nanoparticle retained exceptional HER properties, validating the universality of NbZnCo₂ element composition. Our work establishes a synergistic "ML-DFT-Experiment” framework for the precise design of high-performance HER electrocatalysis. This methodology exhibits extensibility to diverse other electrocatalytic processes, thereby broadening the applicability in sustainable energy conversion technologies.