Accurate Neural Network Representation of the Ab Initio
Determined Spin–Orbit Interaction in the Diabatic Representation
Including the Effects of Conical Intersections
posted on 2020-02-21, 20:42authored byYafu Guan, David R. Yarkony
A method
for fitting ab initio determined spin–orbit coupling
interactions, in the Breit–Pauli approximation, based on quasidiabatic
representations using neural network fits is reported. The algorithm generalizes our recently reported
neural network approach for representing the dipole interaction. The S0, S1, and T1 states of formaldehyde are used as an example.
First, the two singlet states S0 and S1 are diabatized with a modified Boys Localization
diabatization method. Second, the spin–orbit coupling between
singlet and triplet states is transformed to the diabatic representation.
This removes the discontinuities in the adiabatic representation.
The diabatized spin–orbit couplings are then fit with smooth
neural network functions. The analytic representation of spin–orbit
coupling interactions in a diabatic basis by neural networks will
make accurate full-dimensional quantum dynamical treatment of both
internal conversion and intersystem crossing possible, which will
help us to gain better understanding of both processes.