  Nanoparticles have become increasingly interesting for a wide range of
applications, because in principle it is possible to tailor their properties by
controlling size, shape and composition. One of these applications is
heterogeneous catalysis, and a fundamental understanding of the structural
details of the nanoparticles is essential for any knowledge-based improvement
of reactivity and selectivity. In this work we investigate the atomic structure
of brass nanoparticles containing up to 5000 atoms as a typical example for a
binary alloy consisting of Cu and Zn. As systems of this size are too large for
electronic structure calculations, in our simulations we use a recently
parametrized machine learning potential providing close to density functional
theory accuracy. This potential is employed for a structural characterization
as a function of chemical composition by various types of simulations like
Monte Carlo in the Semi-Grand Canonical Ensemble and simulated annealing
molecular dynamics. Our analysis reveals that the distribution of both elements
in the nanoparticles is inhomogeneous, and zinc accumulates in the outermost
layer, while the first subsurface layer shows an enrichment of copper. Only for
high zinc concentrations alloying can be found in the interior of the
nanoparticles, and regular patterns corresponding to crystalline bulk phases of
$\alpha$-brass can then be observed. The surfaces of the investigated clusters
exhibit well-ordered single-crystal facets, which can give rise to grain
boundaries inside the clusters. The melting temperature of the nanoparticles is
found to decrease with increasing zinc-atom fraction, a trend which is
well-known also for the bulk phase diagram of brass.
