  Owing to the excellent catalysis properties of Ag-Au binary nanoalloy,
nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, have been
under intense investigation. To achieve high accuracy in molecular simulations
of the Ag-Au nanoalloys, the surface properties are required to be modeled with
first-principles precision. In this work, we propose a generalizable
machine-learning interatomic potential for the Ag-Au nanoalloys based on deep
neural networks, trained from a database constructed with the first-principle
calculations. This potential is highlighted by the accurate prediction of Au
(111) surface reconstruction and the segregation of Au towards the Ag-Au
nanoalloy surface, where the empirical force field failed in both cases.
Moreover, regarding the adsorption and diffusion of adatoms on surfaces, the
overall performance of our potential is better than the empirical force fields.
We stress that the reported surface properties are blind to the potential
modeling in the sense that none of the surface configurations is explicitly
included in the training database, therefore, the reported potential is
expected to have a strong ability of generalization to a wide range of
properties and to play a key role in the investigation of nanostructured Ag-Au
evolution, where the accurate descriptions of free surfaces are necessary.
