Global Optimization of Copper Clusters at the ZnO(10-10) Surface Using a DFT-based Neural Network Potential and Genetic Algorithms

TL;DR

This study combines a neural network potential with genetic algorithms to efficiently identify the most stable copper cluster structures on a ZnO surface, revealing important structural features and limitations of frozen surface assumptions.

Contribution

It introduces a novel computational approach integrating neural network potentials with genetic algorithms for accurate global optimization of supported metal clusters.

Findings

Identified global and local minima for Cu clusters on ZnO surface.

Discovered structural features similar to Cu(111) and Cu(110) surfaces.

Showed that frozen substrate approximations can miss relevant structures.

Abstract

The determination of the most stable structures of metal clusters supported at solid surfaces by computer simulations represents a formidable challenge due to the complexity of the potential-energy surface. Here we combine a high-dimensional neural network potential, which allows to predict the energies and forces of a large number of structures with first-principles accuracy, with a global optimization scheme employing genetic algorithms. This very efficient setup is used to identify the global minima and low-energy local minima for a series of copper clusters containing between four and ten atoms adsorbed at the ZnO(10$\bar{1}$0) surface. A series of structures with common structural features resembling the Cu(111) and Cu(110) surfaces at the metal-oxide interface has been identified, and the geometries of the emerging clusters are characterized in detail. We demonstrate that the frequently employed approximation of a frozen substrate surface in global optimization can result in missing the most relevant structures.