Revisiting and Accelerating the Basin Hopping Algorithm for Lennard-Jones Clusters: Adaptive and Parallel Implementation in Python

TL;DR

This paper introduces an adaptive, parallel Python implementation of the Basin Hopping algorithm for Lennard-Jones clusters, significantly improving efficiency and scalability for global energy minimization tasks.

Contribution

It presents a novel adaptive and parallel framework for Basin Hopping, enabling faster exploration of energy landscapes and scalability to high-performance computing environments.

Findings

Nearly linear speedup with up to eight parallel minimizations

Effective balance between exploration and local refinement

Framework extendable to ab initio calculations like DFT

Abstract

We present an adaptive and parallel implementation of the Basin Hopping (BH) algorithm for the global optimization of atomic clusters interacting via the Lennard-Jones (LJ) potential. The method integrates local energy minimization with adaptive step-size Monte Carlo moves and simultaneous evaluation of multiple trial structures, enabling efficient exploration of complex potential energy landscapes while maintaining a balance between exploration and refinement. Parallel evaluation of candidate structures significantly reduces wall-clock time, achieving nearly linear speedup for up to eight concurrent local minimizations. This framework provides a practical and scalable strategy to accelerate Basin Hopping searches, directly extendable to ab initio calculations such as density functional theory (DFT) on high-performance computing architectures.