Properties of {\alpha}-Brass Nanoparticles II: Structure and Composition
Jan Weinreich, Mart\'in Leandro Paleico, and J\"org Behler

TL;DR
This study investigates the atomic structure and composition distribution of brass nanoparticles using machine learning potentials, revealing inhomogeneous element distribution, surface ordering, and melting temperature trends related to zinc content.
Contribution
It introduces a machine learning-based simulation approach to analyze large brass nanoparticles, providing new insights into their atomic arrangement and compositional inhomogeneity.
Findings
Zinc accumulates on the nanoparticle surface.
Copper enriches the subsurface layer.
Melting temperature decreases with zinc content.
Abstract
Nanoparticles have become increasingly interesting for a wide range of applications, because in principle it is possible to tailor their properties by controlling size, shape and composition. One of these applications is heterogeneous catalysis, and a fundamental understanding of the structural details of the nanoparticles is essential for any knowledge-based improvement of reactivity and selectivity. In this work we investigate the atomic structure of brass nanoparticles containing up to 5000 atoms as a typical example for a binary alloy consisting of Cu and Zn. As systems of this size are too large for electronic structure calculations, in our simulations we use a recently parametrized machine learning potential providing close to density functional theory accuracy. This potential is employed for a structural characterization as a function of chemical composition by various types of…
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Taxonomy
TopicsNanoporous metals and alloys · Quasicrystal Structures and Properties
