Extending the Representation of Multistate Coupled Potential Energy Surfaces to Include Properties Operators Using Neural Networks: Application to the 1,21A States of Ammonia
Yafu Guan, Hua Guo, David R. Yarkony
Abstract: Fitting coupled adiabatic potential energy surfaces using coupled diabatic states enables, for accessible systems, nonadiabatic dynamics to be performed with superior accuracy, when compared with that attainable using on-the-fly dynamics. However on-the-fly dynamics has advantages not the least of which is the ability to compute molecular properties and interactions 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 1,21A states of ammonia to show how to bring these advantages to the fit-coupled surface method using a diabatic representation.
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