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. |