Clusters are relatively stable microscopic and submicroscopic collectives composed of several or even thousands of atoms, molecules or ions through physical or chemical binding forces. As transition states between atoms and solids, clusters have attracted extensive attention due to the existence of novel properties different from their bulk materials, such as superparamagnetism, anomalous multiferroicity and high-efficiency catalytic activity. Determining the ground state structure of a cluster is an important step to further explore its physical and chemical properties. However, it is a time-consuming task to find the globally minimum energy structure on the potential energy surface of a cluster, especially for the cluster containing many atoms. Therefore, it is of great practical significance to develop an efficient and fast global search algorithm for cluster structures. At present, we have successfully independently developed an efficient global optimization program multi algorithm structure searcher (MASS) that integrates four typical global optimization algorithms, including basin hopping algorithm, particle swarm optimization algorithm, genetic algorithm and social emotion optimization algorithm, for atomic and molecular cluster structures. The ground-state structures of a series of V and Cr doped silicon clusters TMSin- (TM=V1-3, Cr1-2; n=14-20) have been confirmed by using the MASS program combined with high-precision photoelectron spectra and density functional theory. At present, we are studying the structural evolution and properties of transition metal clusters including Cu, Ag, Au, Nb, Ta, Tm. In the future, we will conduct in-depth research on the structures and properties of more clusters.
Fig1. Structures of CrSi14-18- clusters
