Rate coefficients for C and O₂ reactive collisions relevant to interstellar clouds from QCT and machine learning.

This article focuses on determining state-selected reactive cross sections and rate coefficients for the reaction between ground-state carbon atoms (C, ^3P) and molecular oxygen (O₂, ^3Σ_g^−) forming carbon monoxide (CO, ^1Σ^+) and atomic oxygen (O, ^1D) over a temperature range of 50–1500 K. The study combines quasi-classical trajectory (QCT) calculations with machine learning (ML) techniques—specifically Neural Network (NN) and Gaussian Process Regression (GPR)—to efficiently predict rovibrational transition parameters for initial O₂ vibrational states v = 0–4 and rotational states j = 0–70, producing CO vibrational states v′ = 0–15. Both ML models accurately reproduce QCT results with high correlation (R² > 0.96) and reduce computational costs, providing a comprehensive dataset valuable for astrophysical and atmospheric modeling of environments containing C and O₂. The work also confirms that lower-lying rovibrational states of O₂ dominate the reaction dynamics due to the exothermic and barrierless nature of the process.