What is it about?
We developed a deep-learning potential for AgPd nanoalloys based on a multiscale dataset ranging from nanocluster to bulk configurations, which exhibits precise predictions of mechanical properties and formation energies with near-density functional theory accuracy, calculates the surface energies of Ag and Pd closer to experimental values compared to those obtained by Gupta potentials, and is applied to investigate the shape reconstruction of single-crystalline AgPd nanoalloys from cuboctahedron to icosahedron geometries.
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Why is it important?
The deep-learning potential (DP) is developed for AgPd nanoalloys based on a multiscale dataset ranging from nanocluster to bulk configurations for the first time, which calculates the surface energies of Ag and Pd closer to experimental values compared to those obtained by Gupta potentials, and is successfully utilized in investigating the mechanism of deformation twining in Pd@Ag nanoalloys and Ag outward diffusion mechanism in Ag@Pd nanoalloys. The structural transformations of single-crystalline Pd@Ag nanoalloys is characterized by a displacive transformation, distinguishing it from the diffusion-coupled transformation of Ag@Pd nanoalloys.
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This page is a summary of: Structural transformations in single-crystalline AgPd nanoalloys from multiscale deep potential molecular dynamics, The Journal of Chemical Physics, July 2023, American Institute of Physics,
DOI: 10.1063/5.0158918.
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