Extending the Representation of Multistate Coupled Potential Energy Surfaces To Include Properties Operators Using Neural Networks: Application to the 1,2 1 A States of Ammonia.

Fitting coupled adiabatic potential energy surfaces using coupled diabatic states enables, for accessible systems, nonadiabatic dynamics to be performed with unprecedented accuracy, when compared with on-the-fly dynamics. On-the-fly dynamics has advantages, not the least of which is the ability to compute molecular properties including electric dipole moments, transition dipole moments, and spin-orbit couplings. The availability of these terms extends the range of processes that can be treated with on-the-fly methods. In this work we use the example of fitting electric dipole and transition dipole moments of the 1,2 ¹ A states of ammonia to show how to bring these advantages to the fit-coupled-surface method using a diabatic representation.