Revisiting and Accelerating the Basin Hopping Algorithm for Lennard-Jones Clusters: Adaptive and Parallel Implementation in Python
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914123795660800 |
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| author | Carmona, Oliver Rodríguez-Kessler, Peter Ludwig Salazar-Colores, Sebastián Muñoz-Castro, Alvaro |
| author_facet | Carmona, Oliver Rodríguez-Kessler, Peter Ludwig Salazar-Colores, Sebastián Muñoz-Castro, Alvaro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revisiting and Accelerating the Basin Hopping Algorithm for Lennard-Jones Clusters: Adaptive and Parallel Implementation in Python Carmona, Oliver Rodríguez-Kessler, Peter Ludwig Salazar-Colores, Sebastián Muñoz-Castro, Alvaro Materials Science 80A50, 82D80 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. |
| title | Revisiting and Accelerating the Basin Hopping Algorithm for Lennard-Jones Clusters: Adaptive and Parallel Implementation in Python |
| topic | Materials Science 80A50, 82D80 |
| url | https://arxiv.org/abs/2510.25938 |