Combining Machine Learning and Spectroscopy to Model Reactive Atom + Diatom Collisions

Veliz, Juan Carlos San Vicente and Arnold, Julian and Bemish, Raymond J. and Meuwly, Markus. (2022) Combining Machine Learning and Spectroscopy to Model Reactive Atom + Diatom Collisions. Journal of Physical Chemistry A, 126 (43). pp. 7971-7980.

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Official URL: https://edoc.unibas.ch/93274/

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The prediction of product translational, vibrational, and rotational energy distributions for arbitrary initial conditions for reactive atom + diatom collisions is of considerable practical interest in atmospheric re-entry. Because of the large number of accessible states, determination of the necessary information from explicit (quasi-classical or quantum) dynamics studies is impractical. Here, a machine-learned (ML) model based on translational energy and product vibrational states assigned from a spectroscopic, ro-vibrational coupled energy expression based on the Dunham expansion is developed and tested quantitatively. All models considered in this work reproduce final state distributions determined from quasi-classical trajectory (QCT) simulations with R2 ∼ 0.98. As a further validation, thermal rates determined from the machine-learned models agree with those from explicit QCT simulations and demonstrate that the atomistic details are retained by the machine learning which makes them suitable for applications in more coarse-grained simulations. More generally, it is found that ML is suitable for designing robust and accurate models from mixed computational/experimental data which may also be of interest in other areas of the physical sciences.
Faculties and Departments:05 Faculty of Science > Departement Physik > Physik > Theoretische Physik (Bruder)
UniBasel Contributors:Arnold, Julian
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 Feb 2023 11:16
Deposited On:09 Feb 2023 14:04

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