Structural prediction of B18Y2 cluster: A Machine-Learning-Assisted Basin-Hopping Study
Abstract
The structural and optical properties of the doubly yttrium-doped boron cluster B18Y2 have been systematically investigated using density functional theory calculations. The lowest-energy structure was identified through extensive basin-hopping searches accelerated by a pre-trained MACE machine-learning potential and subsequently refined and validated at the DFT level. The resulting global-minimum structure adopts a double-ring geometry, consisting of two fused B9 rings stabilized by yttrium atoms positioned above and below the boron framework. Vibrational frequency calculations confirm the dynamical stability of the optimized structure, while the calculated infrared and UV--Vis spectra provide characteristic signatures of the Y--B interactions and the electronic structure of the boron framework. These results demonstrate how yttrium doping can stabilize unusual double-ring boron architectures and highlight the effectiveness of machine-learning-assisted basin-hopping searches for exploring the complex potential-energy landscapes of doped boron clusters.
Related
- Cluster-based Structural Similarity for Dataset Visualization and Data Selection for Machine Learning Interatomic Potentials
- Machine Learning Assisted Parametrisation and Prediction of Bare to Neutral β- Decay Rate Ratios of Fully Ionised Atoms
- Machine Learning-Assisted Discovery of Carbon-Related Defect Formation Mechanisms in Diamond
- Extracting dipole orientations from asymmetric plasmonic nanostructures towards machine-learning-assisted spectropolarimetry
- Machine-learning-assisted phase-amplitude reduction for fast synchronization of airfoil wakes with constrained fluctuations
BibTeX
@misc{rodriguezkessler2026structural,
title = {{Structural prediction of B$_18$Y$_2$ cluster: A Machine-Learning-Assisted Basin-Hopping Study}},
author = {Peter Ludwig Rodríguez-Kessler},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.31533},
eprint = {2609.31533},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.31533},
}
RIS
TY - UNPB
TI - Structural prediction of B_18Y_2 cluster: A Machine-Learning-Assisted Basin-Hopping Study
AU - Peter Ludwig Rodríguez-Kessler
PY - 2026
DA - 2026/09/25
PB - arXiv
DO - 10.48550/arxiv.2609.31533
UR - https://arxiv.org/abs/2609.31533
AB - The structural and optical properties of the doubly yttrium-doped boron cluster B_18Y_2 have been systematically investigated using density functional theory calculations. The lowest-energy structure was identified through extensive basin-hopping searches accelerated by a pre-trained MACE machine-learning potential and subsequently refined and validated at the DFT level. The resulting global-minimum structure adopts a double-ring geometry, consisting of two fused B_9 rings stabilized by yttrium atoms positioned above and below the boron framework. Vibrational frequency calculations confirm the dynamical stability of the optimized structure, while the calculated infrared and UV--Vis spectra provide characteristic signatures of the Y--B interactions and the electronic structure of the boron framework. These results demonstrate how yttrium doping can stabilize unusual double-ring boron architectures and highlight the effectiveness of machine-learning-assisted basin-hopping searches for exploring the complex potential-energy landscapes of doped boron clusters.
ER -