Multistate coupled diabatic neural network potential for the quantum non-adiabatic photofragmentation of CH2+

Abstract

Tracking the complex non-adiabatic transitions in far-ultraviolet photodissociation demands highly accurate diabatic potential energy matrices (PEMs) across numerous excited states. To address this, we introduce a fully automated diabatization method that leverages artificial neural networks to fit PEMs. Our approach divides the PEM into a physically grounded zeroth-order diagonal term, which is then corrected by a neural network matrix to capture electronic couplings. By enforcing symmetry constraints on off-diagonal elements and sharing degenerate diabatic states between the A′ and A″ irreducible representations, the diabatization process becomes completely automatic. We validate this method using time-dependent wavepacket calculations to simulate the photodissociation of CH₂⁺, incorporating relevant states up to ≈13.6 eV. Finally, we compute partial cross-sections for all fragmentation channels—including total and partial fragmentation yielding CH⁺, CH, H₂, and H₂⁺ diatoms—revealing a notably high cross-section for the formation of the CH radical.