Catalytic CO−O coupling on high-entropy alloys: A composition optimization dependent on the reaction assumptions

Abstract

Catalytically joining carbon monoxide and oxygen atoms is crucial for various environmental and industrial applications. However, the reaction environments for this process vary significantly across different applications. In this paper, we demonstrate how reaction environments and associated modeling assumptions influence the predictions of optimal disordered alloy catalysts for CO-O coupling. Our analysis is based on a dataset comprising several hundred *CO, *OH, and *O simulated adsorption energies on atomically disordered face-centered cubic (111) surfaces composed of Ag, Au, Cu, Pd, Pt, and Ru. This dataset is provided alongside this publication to support further machine learning-based searches for optimal CO-O coupling catalysts within this composition space for any CO-O coupling application. We illustrate the impact of different modeling assumptions on the identification of CO-O coupling catalysts through two key examples: electrochemical methanol oxidation and CO-tolerant hydrogen fuel cells.