Global optimization of bimetallic cluster structures. I. Size-mismatched Ag–Cu, Ag–Ni, and Au–Cu systems

A genetic algorithm approach is applied to the optimization of the potential energy of a wide range of binary metallic nanoclusters, Ag-Cu, Ag-Ni, Au-Cu, Ag-Pd, Ag-Au, and Pd-Pt, modeled by a semiempirical potential. The aim of this work is to single out the driving forces that make different structural motifs the most favorable at different sizes and chemical compositions. Paper I is devoted to the analysis of size-mismatched systems, namely, Ag-Cu, Ag-Ni, and Au-Cu clusters. In Ag-Cu and Ag-Ni clusters, the large size mismatch and the tendency of Ag to segregate at the surface of Cu and Ni lead to the location of core-shell polyicosahedral minimum structures. Particularly stable polyicosahedral clusters are located at size N = 34 (at the composition with 27 Ag atoms) and N = 38 (at the composition with 32 and 30 Ag atoms). In Ag-Ni clusters, Ag32Ni13 is also shown to be a good energetic configuration. For Au-Cu clusters, these core-shell polyicosahedra are less common, because size mismatch is not reinforced by a strong tendency to segregation of Au at the surface of Cu, and Au atoms are not well accommodated upon the strained polyicosahedral surface.

Global optimization of bimetallic cluster structures. II. Size-matched Ag-Pd, Ag-Au, and Pd-Pt systems

Genetic algorithm global optimization of Ag-Pd, Ag-Au, and Pd-Pt clusters is performed. The 34- and 38-atom clusters are optimized for all compositions. The atom-atom interactions are modeled by a semiempirical potential. All three systems are characterized by a small size mismatch and a weak tendency of the larger atoms to segregate at the surface of the smaller ones. As a result, the global minimum structures exhibit a larger mixing than in Ag-Cu and Ag-Ni clusters. Polyicosahedral structures present generally favorable energetic configurations, even though they are less favorable than in the case of the size-mismatched systems. A comparison between all the systems studied here and in the previous paper (on size-mismatched systems) is presented.

Strategies for increasing the efficiency of a genetic algorithm for the structural optimization of nanoalloy clusters

An improved genetic algorithm (GA) is described that has been developed to increase the efficiency of finding the global minimum energy isomers for nanoalloy clusters. The GA is optimized for the example Pt12Pd12, with specific investigation of: the effect of biasing the initial population by seeding; the effect of removing specified clusters from the population ("predation"); and the effect of varying the type of mutation operator applied. These changes are found to significantly enhance the efficiency of the GA, which is subsequently demonstrated by the application of the best strategy to a new cluster, namely Pt19Pd19.

Geometry optimisation of aluminium clusters using a genetic algorithm

The application of a Genetic Algorithm, for optimising the geometry of aluminium clusters with 21-55 atoms bound by the many-body Murrell-Mottram potential, is described. In this size regime, a number of different structural motifs are identified--face-centred cubic, hexagonal close packed, decahedral and icosahedral structures. The larger clusters consist of hollow icosahedral geometric shells, with Al55 having a centred icosahedral structure. Evolutionary Progress Plots for Al19 and Al38 reveal how the best structure evolves from generation to generation upon operation of the Genetic Algorithm.