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Owing to the excellent catalysis properties of Ag-Au binary nanoalloy,nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, have beenunder intense investigation. To achieve high accuracy in molecular simulationsof the Ag-Au nanoalloys, the surface properties are required to be modeled withfirst-principles precision. In this work, we propose a generalizablemachine-learning interatomic potential for the Ag-Au nanoalloys based on deepneural networks, trained from a database constructed with the first-principlecalculations. This potential is highlighted by the accurate prediction of Au(111) surface reconstruction and the segregation of Au towards the Ag-Aunanoalloy surface, where the empirical force field failed in both cases.Moreover, regarding the adsorption and diffusion of adatoms on surfaces, theoverall performance of our potential is better than the empirical force fields.We stress that the reported surface properties are blind to the potentialmodeling in the sense that none of the surface configurations is explicitlyincluded in the training database, therefore, the reported potential isexpected to have a strong ability of generalization to a wide range ofproperties and to play a key role in the investigation of nanostructured Ag-Auevolution, where the accurate descriptions of free surfaces are necessary.

A Generalizable Machine-learning Potential of Ag-Au Nanoalloys and its Application on Surface Reconstruction, Segregation and Diffusion