Aug 2026· Journal of Computational Chemistry· Vol 47· 0 citations· 61 references
Medicine
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 LJ13, and local relaxation with similarity filtering alone remains insufficient for the double‐funnel LJ38 benchmark, reaching the global minimum only six times in 25 runs. Adding the Tabu‐style initializer changes this outcome: LJ38 is solved in all 20 independent runs, and the full LJ2–LJ100 benchmark reaches 943 successes in 1030 runs, corresponding to an overall success rate of 91.5%. The main remaining failures occur for LJ75–LJ77, consistent with the known difficulty of the Marks‐decahedral exceptions. These results identify physically informed population construction as a practical route for improving BO‐based cluster global optimization.
Global structure optimization in computational chemistry is often limited not by leaving the current local minimum, which can be achieved by sufficiently large random moves, but by proposing productive moves that exploit local funnel structure without losing diversity. Minima hopping addresses this problem through short molecular-dynamics escape trajectories, local relaxation, and history-dependent feedback, but its efficiency depends strongly on the initial escape direction. We benchmark a curvature-assisted variant in which inverse-Hessian information accumulated by Broyden–Fletcher–Goldfarb–Shanno (BFGS) and limited-memory BFGS (L-BFGS) relaxation is recycled as an escape model. This requires no explicit second derivatives and no additional force evaluations before proposing low-curvature directions. Lennard–Jones (LJ) clusters with 60–74 particles provide controlled landscapes for comparing random and softened random directions, single Hessian modes, multi-mode Hessian combinations, and mixed Hessian-random directions. Dense BFGS curvature information identifies physically meaningful escape subspaces and can reduce repeated local exploration. Single deterministic modes, however, oversample local funnels, and L-BFGS curvature information is not reliable enough for direct mode selection. Combining several BFGS modes improves robustness, but softened random directions with L-BFGS remain the lowest-cost baseline. Curvature reuse is therefore most useful when it provides an inexpensive soft-mode subspace while preserving stochastic diversity, especially when conventional softening or trial-move optimization is expensive.
Daniel Schärf, T. Kühne· Theoretical Chemistry accoun...· 0 citations
Efficiently exploring the global potential energy surface (PES) of different materials has been a challenging yet very important task for theoretical simulation. By accurately characterizing the global PES and strategically combining the genetic algorithm (GA) with the stochastic surface walking (SSW) method, here we propose a novel method for global PES exploration, achieving efficient localization of the global minimum. Notably, by leveraging the extensive configuration network sampled during the search, this framework further enables the automated identification of long-range, low-energy transition pathways between distant superbasins, providing deep insights into the structural evolution mechanisms. Through tests on different systems, such as atomic clusters, ligand-protected clusters, molecular clusters, surface-supported clusters, atomic crystals, and molecular crystals, it has been proven that the new algorithm is highly effective in describing the overall characteristics of the PES. It has shown significant improvements in exploring the global PES compared with standalone GA and SSW methods, especially for complex and large-scale chemical systems.
Wen Liu, Zhi-Pan Liu, Cheng Shang· Journal of Chemical Theory a...· 0 citations
The efficiency of Bayesian optimization (BO) of atomic configurations depends strongly on how configurations are encoded. We introduce the Weisfeiler-Lehman (WL) subtree kernel, which views configurations as element-labeled graphs and measures their similarity by how many local structural patterns they share, into Bayesian-optimization-based configuration search. Because this kernel is reproduced as the plain inner product of explicit features (L$^2$-normalized histograms of local topological patterns), introducing it reduces to introducing the corresponding features: the encoding enters existing BO frameworks as an ordinary descriptor. In a benchmark ground-state configuration search of cubic BC$_2$N evaluated with a universal machine-learning interatomic potential, the WL encoding reached the ground state almost immediately after a shared random initialization of 100 samples in every one of five independent rounds (108$\pm$5 evaluations on average), whereas the one-hot baseline required 280$\pm$122 evaluations; the WL-driven sampler first exhausted the degenerate ground-state group and then discovered the metastable degenerate groups from the bottom up, in order of increasing energy.
Akira Kusaba, Tatoshi Yonemori, Tetsuji Kuboyama et al.· 0 citations
An enhanced local optimization strategy based on curved line search (CLS) is introduced and integrated into AutoDock Vina, resulting in Vina_CLS, demonstrating that improved local optimization can substantially enhance docking performance.
Leo Gaskin, Matthias Welsch, J. Kirchmair et al.· Journal of Chemical Theory a...· 0 citations
Track seeding strongly affects both the quality and computational cost of charged-particle reconstruction, yet its many configuration parameters are commonly tuned through expert intuition and repeated trial and error. ACTS reduces this burden with an Optuna Tree-structured Parzen Estimator auto-tuner, but expensive evaluations, a restricted search space, and a scalarized objective can limit evaluation efficiency, exclude promising configurations, and obscure performance trade-offs. We investigate whether Bayesian optimization can address these limitations using ACTS with the Open Data Detector (ODD). Under identical search ranges and a common 100-trial budget, we compare Expected Improvement and Upper Confidence Bound with TPE and random search on the existing eight-parameter problem, extend the best-performing Bayesian method to fifteen parameters, and apply Expected Hypervolume Improvement to optimize efficiency, fake rate, duplicate rate, and runtime without fixed scalar weights. Candidate configurations are evaluated through the full ACTS reconstruction chain and validated on disjoint held-out events. The Bayesian acquisition methods identify strong configurations earlier than TPE, and their advantage persists in held-out validation. Expanding the search further improves performance, while multi-objective optimization reveals competitive non-dominated solutions spanning distinct trade-offs. These results indicate that Bayesian optimization can strengthen ACTS auto-tuning through efficient evaluation, broader parameter searches, and post-hoc expert selection among non-dominated alternatives.
Efficient exploration of the complex potential-energy landscapes of amorphous materials is central to computational structure discovery and refinement. Conventional Monte Carlo, reverse Monte Carlo, and related methods typically sample configuration space through small, local trial moves and may require millions to tens of millions of moves to converge. Here, we evaluate larger, nonlocal perturbations followed by local geometry relaxation as an alternative sampling strategy. We develop and test four perturbation types using amorphous Al$_2$O$_3$ as a model system. Among them, moving an oxygen atom to change the coordination numbers of two aluminum atoms, allows access to low-energy configurations with substantially fewer trial moves than a conventional Monte Carlo trajectory. These results suggest that relaxation-assisted nonlocal moves could reduce trapping in local minima and improve sampling in structure-search and reverse Monte Carlo workflows.
Coraline Du, Hye Sol Kim, S. Warren· 0 citations
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