Multistate Coupled Diabatic Neural Network potential for the quantum non-adiabatic Photofragmentation of CH$_2^+$

TL;DR

This paper presents an automated neural network-based diabatization method for accurate potential energy matrices, validated on CH$_2^+$ photodissociation, enabling detailed cross-section calculations of various fragmentation channels.

Contribution

The authors introduce a fully automated neural network approach for diabatization that enforces physical symmetry constraints, improving accuracy in modeling non-adiabatic photodissociation processes.

Findings

Validated method with wavepacket calculations up to 13.6 eV.

Computed partial cross-sections for multiple fragmentation channels.

Found high cross-section for CH radical formation.

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 CH$_2^+$, incorporating relevant states up to $\approx 13.6$~eV. Finally, we compute partial cross-sections for all fragmentation channels -- including total and partial fragmentation yielding \ce{CH+}, \ce{CH}, \ce{H2}, and \ce{H2+} diatoms -- revealing a notably high cross-section for the formation of the \ce{CH} radical.