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San Vicente Veliz, Juan Carlos; Arnold, Julian; Bemish, Raymond J.; Meuwly, Markus
The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 11/2022, Letnik: 126, Številka: 43Journal Article
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 R 2 ∼ 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.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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