On the relationship between spectroscopic constants of diatomic molecules: a 
machine learning approach

Liu, Xiangyue; Meijer, Gerard; Pérez-Ríos, Jesús

doi:10.1039/D1RA02061G

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On the relationship between spectroscopic constants of diatomic molecules: a machine learning approach

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Liu, Xiangyue
Molecular Physics, Fritz Haber Institute, Max Planck Society;

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Meijer, Gerard
Molecular Physics, Fritz Haber Institute, Max Planck Society;

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Pérez-Ríos, Jesús
Molecular Physics, Fritz Haber Institute, Max Planck Society;

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Citation

Liu, X., Meijer, G., & Pérez-Ríos, J. (2021). On the relationship between spectroscopic constants of diatomic molecules: a machine learning approach. RSC Advances, 11(24), 14552-14561. doi:10.1039/D1RA02061G.

Cite as: https://hdl.handle.net/21.11116/0000-0008-5452-D

Abstract

Through a machine learning approach, we show that the equilibrium distance, harmonic vibrational frequency and binding energy of diatomic molecules are related, independently of the nature of the bond of a molecule; they depend solely on the group and period of the constituent atoms. As a result, we show that by employing the group and period of the atoms that form a molecule, the spectroscopic constants are predicted with an accuracy of <5%, whereas for the A-excited electronic state it is needed to include other atomic properties leading to an accuracy of <11%.