Automated Learning of a Dense Manifold of Electronic States and Electronic Energy Transfer and Reactions in Singlet O Collisions with N2.

Calculations of collisions involving excited electronic states play an important role in many high-energy environments, for example, in simulating thermal energy content and heat flux in flows around hypersonic reentry vehicles, and useful data are usually not available from either experiment or theory. We apply a deep learning framework-compatibilization by deep neural network-to automatically discover and fit a compatible potential energy matrix (CPEM) for singlet oxygen atom collisions with N2 in the 1 A' manifold of N2O. The procedure yields not only a fit to the CPEM and its gradient but also analytic representations of the adiabatic potential energy surfaces and their gradients across a dense 13-state manifold; these potential energy surfaces are suitable for dynamics calculations of inelastic and reactive collisions across a broad range of collision energies extending above the dissociation threshold of N2. We propose a new asymptotically extended formulation of the curvature-driven coherent switches with decay of mixing (κCSDM) semiclassical dynamics method that resolves the conflict between differing symmetries of the interacting atom-diatom system and the completely separated final states. We use the new dynamics method with the analytic representation of potential surface gradients to compute electronically nonadiabatic cross-sections for N2(X ) + O(1S) collisions, primarily producing N2(X) + O(1D), N2(A) + O(3P), and NO(X) + N(2D). Our methods provide new capabilities for modeling electronic energy transfer under extreme conditions, with implications across chemistry, physics, and aerospace engineering.