Global optimization of copper clusters at the ZnO(101¯0) surface using a DFT-based neural network potential and...

Area of Science:

• Computational materials science
• Surface science
• Catalysis

Background:

• Determining stable metal cluster structures on surfaces is computationally challenging due to complex potential-energy landscapes.
• Accurate prediction requires methods that balance computational cost with first-principles accuracy.

Purpose of the Study:

• To develop and apply an efficient computational approach for identifying global minima of supported metal clusters.
• To investigate the structures of copper clusters (4-10 atoms) on the ZnO(101¯0) surface.

Main Methods:

• Utilized a high-dimensional neural network potential for accurate energy and force predictions.
• Employed a global optimization scheme with genetic algorithms for structure searching.
• Investigated copper clusters on ZnO(101¯0) surfaces.

Main Results:

• Identified stable structures for copper clusters on ZnO(101¯0), revealing features resembling Cu(111) and Cu(110) surfaces.
• Characterized the detailed geometries of the emerging metal-oxide interface structures.
• Demonstrated that a frozen substrate approximation can lead to the omission of relevant cluster configurations.

Conclusions:

• The combined neural network potential and genetic algorithm approach is highly efficient for studying supported metal clusters.
• The study provides insights into the structural motifs formed by copper clusters on oxide surfaces.
• Emphasizes the necessity of including substrate relaxation in simulations for accurate structural determination.