1. Deep neural network for fitting analytical potential energy curve of diatomic molecules from ro-vibrational spectra
- Author
-
M. Krośnicki, T. Urbańczyk, Dominik Horwat, and Jarosław Koperski
- Subjects
General Chemical Engineering ,02 engineering and technology ,01 natural sciences ,chemistry.chemical_compound ,0103 physical sciences ,magnesium fluoride ,General Materials Science ,Diatomic molecule ,Physics::Chemical Physics ,Representation (mathematics) ,Magnesium fluoride ,Physics ,010304 chemical physics ,Artificial neural network ,potential energy curve ,expanded morse oscillator ,deep neural network ,General Chemistry ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,Potential energy ,chemistry ,Modeling and Simulation ,Atomic physics ,0210 nano-technology ,Information Systems ,Vibrational spectra - Abstract
We present a new approach which employs a deep neural network to obtain parameters of analytical representation of potential energy curve of diatomic molecule. We test the approach to find spectroscopic characteristics for the ground X2Σ+ electronic state of MgF molecule based on the experimental energies of ro-vibrational transitions. The result shows that a deep neural network can be applied in characterisation of interatomic potential of diatomic molecule. Our approach is competitive with those obtained using other methods tested, i.e. shallow neural network and the so-called brute force method.
- Published
- 2021