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

摘要

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.