Exhaustive State-to-State Cross Sections and Rate Coefficients for Inelastic N-N Collisions using QCT Combined with Neural Network Models.

Using the quasi-classical trajectory method, we systematically studied the state-to-state vibrational relaxation process of N() + N() collisions over a wide temperature range (5000-30,000 K). Different temperature dependencies of the single- and multiquantum VV and VT events in various (,) collisions are captured, with the dominant channel being related to the initial vibrational energy levels ( = 50). At a specified relative translational energy, there is a monotonic relationship of the VT cross sections with the vibrational energy level, particularly in high-energy collisions. Additionally, we constructed well-trained neural network models (-values reaching 0.99) using limited quasi-classical trajectory (QCT) data sets, which can be used to predict the state-to-state cross sections and rate coefficients of the VV processes N() + N() → N( - Δ) + N( + Δ) and VT processes N() + N() → N( - Δ) + N() (Δ = ±1, ±2, ±3) for collisions with arbitrary initial vibrational states. This work not only significantly reduces computational resources but also serves as a reference for the study of the state-to-state dynamics of all four-atom collision systems in hypersonic flows.