  Binary metal clusters are of high interest for applications in heterogeneous
catalysis and have received much attention in recent years. To gain insights
into their structure and composition at the atomic scale, computer simulations
can provide valuable information if reliable interatomic potentials are
available. In this paper we describe the construction of a high-dimensional
neural network potential (HDNNP) intended for simulations of large brass
nanoparticles with thousands of atoms, which is also applicable to bulk
$\alpha$-brass and its surfaces. The HDNNP, which is based on reference data
obtained from density-functional theory calculations, is very accurate with a
root mean square error of 1.7 meV/atom for total energies and 39 meV/{\AA} for
the forces of structures not included in the training set. The potential has
been thoroughly validated for a wide range of energetic and structural
properties of bulk $\alpha$-brass, its surfaces as well as clusters of
different size and composition demonstrating its suitability for large-scale
molecular dynamics and Monte Carlo simulations with first principles accuracy.
