Structural optimization of atomic clusters using iterated dynamic lattice search: With application to silver clusters

Predicting the global minimum structures of atomic clusters has important practical implications in physics and chemistry. This is because the global minimum structures of their potential function theoretically correspond to their ground state structures, which determine some important physical and chemical properties of clusters. However, this prediction task is a very challenging global optimization problem due to the fact that the number of local minima on the potential energy surface of clusters increases exponentially with the cluster size. In this study, we propose an unbiased global optimization approach, called the iterated dynamic lattice search algorithm, to search for the global minimum structure of atomic clusters. Based on the iterated local search framework, the proposed algorithm employs the well-known monotonic basin-hopping method to improve the initial structures of clusters, a surface-based perturbation operator to randomly change the positions of selected surface atoms or central atom, a dynamic lattice search method to optimize the positions of surface atoms, and the Metropolis acceptance rule to accept the optimized new solutions. The performance of the algorithm is evaluated on the 300 widely studied silver clusters and experimental results show that the proposed algorithm is highly efficient compared to the existing algorithms. In particular, the proposed algorithm improves the best-known structures for 47 clusters and matches the best-known structures for the remaining clusters. Additional experiments are performed to analyze the key components of the algorithm and the landscape of the potential energy surface of several representative clusters.