Machine learning prediction of state-to-state rate constants for astrochemistry 
Author(s): Bossion D., Nyman Gunnar, Scribano Y.
(Article) Published:
Artificial Intelligence Chemistry, vol. 2 p.xxx (2024)
DOI: 10.1016/j.aichem.2024.100052
Abstract: We investigate the possibility to use an artificial neural network in order to generate a large number of accurate state-to-state rate constants, from the available rates obtained at different accuracy levels, including small numbers of accurate rates and large numbers of low-accuracy rates.
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