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arXiv · 2510.25938

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

Oliver Carmona·Peter Ludwig Rodríguez-Kessler·Sebastián Salazar-Colores·Alvaro Muñoz-Castro
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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.

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Oliver Carmona, Peter Ludwig Rodríguez-Kessler, Sebastián Salazar-Colores, Alvaro Muñoz-Castro. 2025-10-29. Revisiting and Accelerating the Basin Hopping Algorithm for Lennard-Jones Clusters: Adaptive and Parallel Implementation in Python. https://arxiv.org/abs/2510.25938

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