Cluster structure prediction via revised particle-swarm optimization algorithm

The global minimization of cluster structure at the atomic level is emerging as a state-of-the-art approach to accelerate the functionality-driven discovery of cluster-based materials. In this work, we have developed a method for global optimization of Lennard-Jones (LJ), elemental metal and bimetallic clusters through revised particle swarm optimization (RPSO) algorithm within random learning procedure, competition operation and confusion mechanism. The approach requires only size and chemical composition for a given cluster to predict stable or metastable structures at given external conditions. Random learning procedure significantly improves the performance of RPSO such as converge rate and optimization efficiency. Moreover, competition operation can ensure the superiority of outstanding individuals and accept the bad solution with a certain probability. Confusion mechanism allows the clusters to fly in a wider space and makes large-scale optimization possible. Results of optimization based on test functions show that the convergence of RPSO is much faster and more reliable than several other algorithms. In addition, RPSO can also perform well on optimization of Lennard-Jones clusters, Pt and Pt–Pd clusters with various sizes and compositions. The high success rate of RPSO demonstrates the reliability of this methodology and provides crucial insights for understanding the rich and complex structures of clusters.