Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions

Arnold, Julian and Koner, Debasish and Kaeser, Silvan and Singh, Narendra and Bemish, Raymond J. and Meuwly, Markus. (2020) Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions. Journal of Physical Chemistry A, 124 (35). pp. 7177-7190.

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Official URL: https://edoc.unibas.ch/80555/

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Machine learning based models to predict product state distributions from a distribution of reactant conditions for atom-diatom collisions are presented and quantitatively tested. The models are based on function-, kernel-, and grid-based representations of the reactant and product state distributions. All three methods predict final state distributions from explicit quasi-classical trajectory simulations with R-2 > 0.998. Although a function-based approach is found to be more than two times better in computational performance, the grid-based approach is preferred in terms of prediction accuracy, practicability, and generality. For the function-based approach, the choice of parametrized functions is crucial and this aspect is explicitly probed for final vibrational state distributions. Applications of the grid-based approach to nonequilibrium, multitemperature initial state distributions are presented, a situation common to energy and state distributions in hypersonic flows. The role of such models in direct simulation Monte Carlo and computational fluid dynamics simulations is also discussed.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Chemie > Physikalische Chemie (Meuwly)
UniBasel Contributors:Meuwly, Markus
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:American Chemical Society
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:16 Mar 2021 10:26
Deposited On:15 Mar 2021 16:54

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