2004/10/28 by Masao Iwamatsu, Yutaka Okabe
Biochemistry, Genetics and Molecular Biology · Chemistry · Materials Science · Mathematics · Physics and Astronomy · #Computer science #Energy landscape #Geology #Jumping #Machine Learning in Materials Science #Mass Spectrometry Techniques and Applications #Mathematical optimization #Mathematics #Minification #Monte Carlo method #Paleontology #Physics #Protein Structure and Dynamics #Statistical physics #Statistics #Structural basin #Thermodynamics #cond-mat.mtrl-sci
paper · pdf · doi:10.1016/j.cplett.2004.10.032
published as Chemical Physics Letters vol. 399 (2004) pp. 396-400
arxiv created 2004/10/28 · openalex publication_date 2004/11/04 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Basin-Hopping (BH) or Monte-Carlo Minimization (MCM) is so far the most reliable algorithms in chemical physics to search for the lowest-energy structure of atomic clusters and macromolecular systems. BH transforms the complex energy landscape into a collection of basins, and explores them by hopping, which is achieved by random Monte Carlo moves and acceptance/rejection using the Metropolis criterion. In this report, we introduce the jumping process in addition to the hopping process in BH. Jumping are invoked when the hopping stagnates by reaching the local optima, and are achieved using the Monte Carlo move at the temperature T=∞ without rejection. Our Basin-Hopping with Occasional Jumping (BHOJ) algorithm is applied to the Lennard-Jones clusters of several notoriously difficult sizes. It was found that the probability of locating the true global optima using BHOJ is significantly higher than the original BH.