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

TL;DR

This paper develops a machine learning model that predicts energy distributions in reactive atom+diatom collisions, validated against classical simulations, enabling efficient and accurate modeling for atmospheric re-entry applications.

Contribution

It introduces a novel ML approach combining spectroscopic data and classical dynamics to accurately predict collision outcomes, improving over traditional methods.

Findings

ML models achieve R^2 ~ 0.98 with QCT data

Thermal rates from ML models match explicit simulations

ML retains atomistic details suitable for coarse-grained simulations

Abstract

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. Due to 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 $R^2 \sim 0.98$. 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.