Heuristic Optimization Of The Au Cluster

A cluster is a microstructure that is clustered together by multiple molecules or atoms.Its special physical or chemical properties won the attention of various fields.In order to accurately describe the interaction of atoms or molecules in clusters,various potential energy models are proposed by scholars to calculate the potential energy of clusters.However,as the number of atoms in the cluster increases,the number of local minima on the potential of the cluster increases exponentially,so finding the global optimal structure of the cluster is a NP-hard problem.To this end,a variety of heuristic algorithms have been proposed by scholars to optimize the cluster configuration.Among them,Basin-Hopping algorithm,dynamic lattice searching algorithm,surface operation and internal operation,genetic algorithm,adaptive immune optimization algorithm,are the commonly used cluster optimization algorithms.In order to solve the problem of Au cluster configuration optimization,Gupta potential energy model is used to describe the interaction between atoms in Au clusters.Using the distance-based perturbation strategy to improve the traditional Basin-Hopping algorithm,combined with improved surface operation and internal operation,A two-stage optimization algorithm based on distance and energy strategy is proposed.The algorithm obtains several new Au cluster configurations by experiment,and refreshes the potential energy of five examples.Among them,the potential energy of many examples is much better than the lowest potential energy in published literature.Based on the comparison of potential energy and local minimization,this paper analyzes the reasonable values of each parameter of Basin-Hopping algorithm.Based on the descent trajectory of potential energy value,this paper analyzes the specific role of the two different stages of the algorithm,and the advantages of the two-stage combination.Finally,through the main view and the top view of the cluster structure,this paper intuitively analyzes the types of the optimal configurations of Au clusters.