Grand canonical molecular dynamics simulations of Cu–Au nanoalloys in thermal equilibrium using reactive ANN potentials

Abstract Nanoalloys are a promising class of (electro-) catalysts for applications in sustainable energy technologies, such as carbon dioxide conversion and proton exchange membrane fuel cells. In this article, we establish a methodology for the prediction of the composition and atomic ordering of alloy nanoparticles in thermal equilibrium. The approach is based on a combination of site-based Monte-Carlo simulations to sample the composition space and simultaneous molecular dynamics simulations to sample the structure space at set temperature and chemical potentials. To provide an accurate description of the atomic interactions that allows the required extensive sampling of the configurational space, we employ artificial neural network potentials to interpolate density-functional theory reference calculations. For the example of Cu–Au nanoalloys with 1.0 nm and 1.5 nm diameters, the equilibrium compositions and structures at 300 K and 500 K are compared to the ground state configurations. Consistent with previous reports, we find that the most stable structures exhibit Cu(core)–Au(shell) configurations. However, we observe a temperature dependence of the alloy composition that gives rise to some Au concentration within the particle core at greater temperatures. Therefore, we argue that equilibrium nanoalloy configurations should be used as starting point for the computational investigation of the catalytic activity of alloy nanoparticles.