Machine-learning guides discovery of multi-principal element alloys as electrocatalyst for hydrogen evolution reaction
image: A “ML-DFT-Experiment” integrated strategy accelerates the design of high-performance multi-principal alloy HER electrocatalysts via machine learning prediction. Density functional theory calculations and experiments validate the model, identifying NbZnCo2 alloy as the cost-performance optimal candidate.
Credit: HIGHER EDUCATION PRESS
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. 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 work entitled “Machine-learning guides discovery of multi-principal element alloys as electrocatalyst for hydrogen evolution reaction” was published on Acta Physico-Chimica Sinica (published on December 4, 2025).