Nested Sampling for Exploring Lennard-Jones Clusters

TL;DR

This paper applies nested sampling with slice sampling to explore Lennard-Jones clusters, effectively identifying stable configurations and phase transitions, and benchmarking the method's efficiency for small and larger clusters.

Contribution

It demonstrates the use of nested sampling with slice sampling to efficiently explore Lennard-Jones potential energy surfaces and identify stable configurations.

Findings

Nested_fit recovers phase transitions.

Identifies different stable configurations.

Slice sampling impacts computational cost.

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.