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Nanoparticles have become increasingly interesting for a wide range ofapplications, because in principle it is possible to tailor their properties bycontrolling size, shape and composition. One of these applications isheterogeneous catalysis, and a fundamental understanding of the structuraldetails of the nanoparticles is essential for any knowledge-based improvementof reactivity and selectivity. In this work we investigate the atomic structureof brass nanoparticles containing up to 5000 atoms as a typical example for abinary alloy consisting of Cu and Zn. As systems of this size are too large forelectronic structure calculations, in our simulations we use a recentlyparametrized machine learning potential providing close to density functionaltheory accuracy. This potential is employed for a structural characterizationas a function of chemical composition by various types of simulations likeMonte Carlo in the Semi-Grand Canonical Ensemble and simulated annealingmolecular dynamics. Our analysis reveals that the distribution of both elementsin the nanoparticles is inhomogeneous, and zinc accumulates in the outermostlayer, while the first subsurface layer shows an enrichment of copper. Only forhigh zinc concentrations alloying can be found in the interior of thenanoparticles, and regular patterns corresponding to crystalline bulk phases of$\alpha$-brass can then be observed. The surfaces of the investigated clustersexhibit well-ordered single-crystal facets, which can give rise to grainboundaries inside the clusters. The melting temperature of the nanoparticles isfound to decrease with increasing zinc-atom fraction, a trend which iswell-known also for the bulk phase diagram of brass.

Properties of {\alpha}-Brass Nanoparticles II: Structure and Composition