B$_{18}$Y$_{2}$团簇的结构预测：机器学习辅助的盆地跳跃研究
Structural prediction of B$_{18}$Y$_{2}$ cluster: A Machine-Learning-Assisted Basin-Hopping Study
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中文总结 AI 辅助
本研究通过机器学习辅助的盆地跳跃搜索和DFT计算，预测了双钇掺杂硼团簇B$_{18}$Y$_2$的稳定双环结构，并揭示了其光学性质及钇掺杂的稳定化作用。
中文摘要 AI 辅助
利用密度泛函理论计算，系统研究了双钇掺杂硼团簇B$_{18}$Y$_2$的结构和光学性质。通过由预训练的MACE机器学习势加速的广泛盆地跳跃搜索，确定了最低能量结构，并在DFT水平上进行了进一步精修和验证。得到的全局最小结构采用双环几何构型，由两个融合的B$_9$环组成，并通过位于硼骨架上方和下方的钇原子稳定。振动频率计算确认了优化结构的动力学稳定性，而计算得到的红外和紫外-可见光谱则提供了Y-B相互作用和硼骨架电子结构的特征信号。这些结果表明，钇掺杂可以稳定不寻常的双环硼结构，并突显了机器学习辅助的盆地跳跃搜索在探索掺杂硼团簇复杂势能面方面的有效性。
英文摘要
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.
发表机构
- Centro de Investigaciones en Óptica A.C.（光学研究中心）
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