2025/10/29 by Carmona, Oliver, Rodríguez-Kessler, Peter Ludwig, Salazar-Colores, Sebastián +1
#80A50 #82D80 #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci)
paper · doi:10.48550/arxiv.2510.25938
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.