The Elucidation of Cu-Zn Surface Alloying by Machine Learning Molecular Dynamics
Harry H. Halim (a*), Yoshitada Morikawa (b)

Graduate School of Engineering, Osaka University

Abstract

The Cu-Zn surface alloy has been extensively involved in the investigation of the active site of Cu/ZnO/Al2O3, the industrial catalysts for methanol synthesis which remains under controversy.[1-2] To provide better understanding of the dynamic of Cu-Zn surface at the atomic level, the structure and the formation process of Cu-Zn surface alloy on Cu(997) were investigated by machine-learning molecular dynamics. Gaussian Process (GP) regression aided with on-the-fly learning were employed to build force-field used to drive molecular dynamics simulation.[3]
The simulation reveals atomistic details of the alloying process, i.e., the incorporation of deposited Zn adatoms to the Cu substrate. The surface alloying is found to start at upper and lower terraces near the step edge, which emphasize the role of steps and kinks in the alloying. The incorporation of Zn at the middle terrace was found at the later stage of the simulation. The rationalization of alloying behavior was performed based on statistics and barriers of various elementary events that occur during the simulation. It was observed that the alloying scheme at upper terrace is dominated by the confinement of Zn step adatom by other adatoms, highlighting the importance of step fluctuations in the alloying process. On the other hand, the alloying scheme at lower terrace is dominated by direct-exchange between Zn step adatom and the Cu atom underneath. The alloying at the middle terrace is dominated by the wave deposition mechanism and deep confinement of Zn adatom.

References
[1] Studt, F. et al., ChemCatChem 7 (7), 1105-1111, 2015.
[2] Kattel, S. et al., Science 355 (6331), 1296, 2017.
[3] Vandermause, J. et al., Npj Comput. Mater 6 (1), 20, 2020.

Keywords: Cu-Zn alloy, molecular-dynamics, density functional theory, machine-learning

Topic: CHEMISTRY AND MATERIAL SCIENCES