Machine learning prediction of state-to-state rate constants for astrochemistry

Duncan Bossion (1, 2) , Gunnar Nyman (2) , Yohann Scribano (3)

1 IPR - Institut de Physique de Rennes
2 Department of Chemistry and Molecular Biology [Gothenburg]
3 LUPM - Laboratoire Univers et Particules de Montpellier

In this work, we investigate the possibility to use an artificial neural network to predict a large number of accurate state-to-state rate constants for atom-diatom collisions, from available rates obtained at two different accuracy levels, using a few accurate rates and many low-accuracy rates. The H + H 2 → H 2 + H chemical reaction is used to benchmark our neural network, as both low and high accuracy state-to-state rates are available in the literature. Our artificial neural network is a multilayer perceptron, using 8 input neurons including the lowaccuracy rate constants, with the high accuracy rate constants as the output neuron. The use of machine learning to predict rate constants is very encouraged, as the rates obtained are accurate, even using as low as 1% of the full dataset to train the neural network, and improve greatly the low accuracy rates previously available. This approach can be used to generate full rate constant datasets with a consistent accuracy, from sparse rates obtained with various methods of different accuracies.

Mots clés: Machine Learning; Quantum dynamics; Astrochemistry -Methods

Domaines: Réseau de neurones [cs.NE]; Physique [physics]; Chimie-Physique [physics.chem-ph]; Physique Numérique [physics.comp-ph]

Duncan Bossion, Gunnar Nyman, Yohann Scribano. Machine learning prediction of state-to-state rate constants for astrochemistry. Artificial Intelligence Chemistry, 2024, 2 (1), pp.100052. ⟨10.1016/j.aichem.2024.100052⟩. ⟨hal-04452423⟩