Machine Learning-Guided Discovery of Alloy Nanoclusters: Steering Morphology-Based Activity and Selectivity Relationships in Bifunctional Electrocatalysts

Abstract

Nanoclusters designed with atomic precision are poised to transform next-generation electrode materials for energy devices due to their exceptional performance. However, traditional computational studies often focus solely on individual nanoclusters, neglecting the impact of structurally diverse, low-energy isomers that coexist in a sample. Herein, we present a data-driven approach to screen late-transition metal-based core-shell nanoclusters for bifunctional electrocatalysis. Utilizing geometric and electronic properties, we establish morphology-based relationships for activity and selectivity, emphasizing the critical role of structural diversity in fuel cell applications. We identify the unique single-cluster catalyst identity of M55 nanoclusters, where intermediate adsorption is primarily governed by the constituent metals’ electronic and elemental characteristics. Our findings identified the Au48W7 nanocluster as the most efficient electrocatalyst, exhibiting the lowest bifunctional overpotential of 0.76 V, with ηOER = 0.33 V and ηORR = 0.43 V, highlighting its outstanding catalytic performance at the nano regime. Guided by the Sabatier principle, we highlight the limitations of conventional numerical methods and reshape the activity volcano, transitioning from RuO2 and Pt to Au/Ag-based nanoclusters. Furthermore, the trained ML model enables the screening of electrocatalysts for two- and four-electron pathways, steering selectivity between H2O2 and H2O formation. This study provides intuitive guidelines for designing efficient bifunctional electrocatalysts, redefining activity volcanoes, and modulates selectivity in nanocluster alloys. © 2025 American Chemical Society.