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The authors resolve the ground-state geometry of doubly yttrium-doped boron clusters ($\text{B}_{18}\text{Y}_2$) using basin-hopping global optimization accelerated by a pre-trained MACE machine-learning interatomic potential and refined via density functional theory. The search identifies a dynamically stable, double-ring global-minimum structure formed by two fused $\text{B}_9$ rings axially coordinated by yttrium atoms, alongside its characteristic vibrational and UV–Vis spectral signatures. The findings show that pre-trained universal ML potentials can accurately bypass expensive ab initio evaluations when exploring complex, highly fluxional coordination landscapes.
Off-the-shelf equivariant machine learning potentials can reliably navigate rugged potential-energy landscapes to uncover novel, dynamically stable transition-metal cluster architectures that standard quantum chemistry methods struggle to sample tractably.
The structural and optical properties of the doubly yttrium-doped boron cluster B$_{18}$Y$_2$ 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.