论双原子分子光谱常数的普适性
On the universality of spectroscopic constants of diatomic molecules
摘要：
我们通过机器学习的方法，证明了双原子分子的平衡距离，谐波振动频率和结合能是普遍相关的。特别是，光谱常数之间的关系是有效的独立的分子键。然而，它们强烈地依赖于组成原子的基团和周期。结果表明，利用分子内原子的基团和周期，可以预测光谱常数，其精度为$\lesssim 5\%$。最后，当采用{ \it ab initio}和密度泛函理论（DFT）电子结构方法的光谱常数时，满足相同的普适关系。
英文摘要：
We show, through a machine learning approach, that the equilibrium distance, harmonic vibrational frequency, and binding energy of diatomic molecules are universally related. In particular, the relationships between spectroscopic constants are valid independently of the molecular bond. However, they depend strongly on the group and period of the constituent atoms. As a result, we show that by employing the group and period of atoms within a molecule, the spectroscopic constants are predicted with an accuracy of $\lesssim 5\%$. Finally, the same universal relationships are satisfied when spectroscopic constants from {\it ab initio} and density functional theory (DFT) electronic structure methods are employed.
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