Elucidation of Cu–Zn Surface Alloying on Cu(997) by Machine-Learning Molecular Dynamics

Abstract

The Cu–Zn surface alloy has been extensively involved in the investigation of the true active site of Cu/ZnO/Al2O3, the industrial catalyst for methanol synthesis which remains under controversy. The challenge lies in capturing the interplay between the surface and reaction under operating conditions, which can be overcome given that the explicit dynamics of the system is known. To provide a better understanding of the dynamic of Cu–Zn surface at the atomic level, the structure and the formation process of the Cu–Zn surface alloy on Cu(997) were investigated by machine-learning molecular dynamics (MD). Gaussian process regression aided with on-the-fly learning was employed to build the force field used in the MD. The simulation reveals atomistic details of the alloying process, that is, 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 the upper terrace is dominated by the confinement of Zn step adatoms by other adatoms, highlighting the importance of step fluctuations in the alloying process. On the other hand, the alloying scheme at the lower terrace is dominated by direct exchange between the 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 adatoms. The short propagation of alloyed Zn in the middle terrace was observed to proceed by means of indirect exchange instead of local exchange as proposed in the previous scanning tunneling microscopy (STM) observation. The comparison of migration rate and activation energies to the result of STM observation is also made. We have found that at a certain distance from the surface, the STM tip significantly affects the elementary events such as vacancy formation and direct exchange.

Visualizing the gas-sensitive structure of the CuZn surface in methanol synthesis catalysis

“…The contrast in atomically resolved STM images involves an electronic contribution originating from the local density of states (LDOS) that often makes it possible to discriminate the individual atomic species in alloys 54 , 55 . In accordance, our atomically resolved UHV-STM image of the resulting CuZn/Cu(111) surface reveals a Cu(111) lattice where isolated bright protrusions are located on the Cu positions, reflecting that the surface now consists of randomly distributed Zn atoms substitutionally alloyed into the Cu(111) matrix 37 , 56 . In agreement, in Fig.…”

Data‐Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts

“…Chapman et al [57] . applied MLFF for MD simulation of Al surface melting at up to 1000 K. Halim et al [58] . used the Gaussian process (GP) force field to reduce computational demand of AIMD to simulate the complex process of migration and structural transformation of Cu−Zn surface alloying on Cu(997), and reported comparable results with STM experiment.…”

“…Faraji et al [55] used a charge equilibration neural network technique (CENT) to generate the MLP for MD on CaF 2 (100) surface structures over a wide temperature range of 300-1200 K. The CENT potential applies to ionic materials because it describes charge transfer for ionic bonding, [56] and its predicted structure was verified by DFT computation. Chapman et al [57] applied MLFF for MD simulation of Al surface melting at up to 1000 K. Halim et al [58] used the Gaussian process (GP) force field to reduce computational demand of AIMD to simulate the complex process of migration and structural transformation of CuÀ Zn surface alloying on Cu(997), and reported comparable results with STM experiment.…”