SURVEYING A POTENTIAL-ENERGY SURFACE BY EIGENVECTOR-FOLLOWING - APPLICATIONS TO GLOBAL OPTIMIZATION AND THE STRUCTURAL TRANSFORMATIONS OF CLUSTERS

We have developed a method to search potential energy surfaces which a voids some of the difficulties associated with trapping in local minim a. Steps are directly taken between minima using eigenvector-following . Exploration of this space by low temperature Metropolis Monte Carlo is a useful global optimisation tool. This method successfully finds t he lowest energy icosahedral minima of Lennard-Jones clusters from ran dom starting configurations, but cannot find the global minimum in a r easonable time for difficult cases such as the 38-atom Lennard-Jones c luster where the face-centred-cubic truncated octahedron is lowest in energy. However, by performing searches at higher temperatures, we hav e found a pathway between the truncated octahedron and the lowest ener gy icosahedral minima. Such a pathway may be illustrative of some of t he structural transformations that are observed for supported metal cl usters by electron microscopy.