ArticleJournal of computational chemistry2026
Physically Informed Population Initialization Improves Bonobo Optimizer Searches for Atomic Cluster Global Minima.
Article in Journal of computational chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Reliable global optimization of atomic clusters is limited not only by the ruggedness of the potential energy surface but also by the quality of the starting population supplied to the search algorithm. We examine this issue for the Bonobo Optimizer (BO) using a workflow that combines external PyAR-style population initialization, local Lennard-Jones relaxation, geometry repair, and structural similarity filtering. The initializer generates physically reasonable and structurally diverse trial clusters before the BO loop begins, while the relaxation and filtering steps map candidates to local minima and reduce redundant population updates. On Lennard-Jones clusters, the unmodified BO framework becomes unreliable beyond LJ
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