Small nanoclusters possess novel physical properties which differ from those of their bulk counterparts, including discrete energy levels , nonlinear optical properties , magnetism , high catalytic activity , multiple absorption bands , and enhanced photoluminescence . These properties emerge as a consequence of their small size and relatively high number of uncoordinated atoms on the surface, and they can be tuned by altering the size and shape of the cluster . The past few decades have shown significant progress in computational methods to predict these properties, but before a property can be calculated it is necessary to first determine the atomic structure of the cluster. A number of algorithms have been developed to predict the ground state atomic structure by comprehensively sampling the potential energy surface (PES) of the cluster. These methods include genetic algorithms , simulated annealing , particle swarm optimization , Bayesian optimization and basin-hopping methods. In each of these methods, identification of the ground state structure is accomplished by first calculating the energies of a large number of candidate structures and then selecting the structures with the lowest energies.