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

Xiangyue LiuGerard MeijerJesus Pérez-Ríos
Published in: RSC advances (2021)
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%.
Keyphrases
  • machine learning
  • molecular docking
  • molecular dynamics simulations
  • artificial intelligence
  • big data
  • energy transfer
  • molecular dynamics
  • deep learning
  • density functional theory
  • binding protein
  • dna binding