Nested Sampling for Exploring Lennard-Jones Clusters

Abstract

Lennard-Jones clusters, while an easy system, have a significant number of non equivalent configurations that increases rapidly with the number of atoms in the cluster. Here, we aim at determining the cluster partition function; we use the nested sampling algorithm, which transforms the multidimensional integral into a one-dimensional one, to perform this task. In particular, we use the nested_fit program, which implements slice sampling as search algorithm. We study here the 7-atom and 36-atom clusters to benchmark nested_fit for the exploration of potential energy surfaces. We find that nested_fit is able to recover phase transitions and find different stable configurations of the cluster. Furthermore, the implementation of the slice sampling algorithm has a clear impact on the computational cost.