Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials

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Abstract

The shape, size, and composition of catalyst nanoparticles can have a significant influence on catalytic activity. Understanding such structure-reactivity relationships is crucial for the optimization of industrial catalysts and the design of novel catalysts with enhanced properties. In this letter, we employ a combination of first-principles computations and large-scale Monte-Carlo simulations with highly accurate neural network potentials to study the equilibrium surface structure and composition of bimetallic Au/Cu nanoparticles (NPs), which have recently been of interest as stable and efficient CO2 reduction catalysts. We demonstrate that the inclusion of explicit water molecules at a first-principles level of accuracy is necessary to predict experimentally observed trends in Au/Cu NP surface composition; in particular, we find that Au-coated core-shell NPs are thermodynamically favored in vacuum, independent of Au/Cu chemical potential and NP size, while NPs with mixed Au-Cu surfaces are preferred in aqueous solution. Furthermore, we show that both CO and O2 adsorption energies differ significantly for NPs with the equilibrium surface composition found in water and those with the equilibrium surface composition found in vacuum, suggesting large changes in CO2 reduction activity. Our results emphasize the importance of understanding and being able to predict the effects of catalytic environment on catalyst structure and activity. In addition, they demonstrate that first-principles-based neural network potentials provide a promising approach for accurately investigating the relationships between solvent, surface composition and morphology, surface electronic structure, and catalytic activity in systems composed of thousands of atoms.

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