Abstract
Owing to the excellent catalytic properties of Ag-Au binary nanoalloys, nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, has been under intense investigation. To achieve high accuracy in molecular simulations of Ag-Au nanoalloys, the surface properties must be modeled with first-principles precision. In this work, we constructed a generalizable machine learning interatomic potential for Ag-Au nanoalloys based on deep neural networks trained from a database constructed with first-principles calculations. This potential is highlighted by the accurate prediction of Au (111) surface reconstruction and the segregation of Au toward 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 generalization ability to a wide range of properties and to play a key role in investigating nanostructured Ag-Au evolution, where accurate descriptions of free surfaces are necessary.
Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth
