Neural Network Based Quasi-diabatic Representation for S<sub>0</sub> and S<sub>1</sub> States of Formaldehyde
Yafu Guan, Changjian Xie, Hua Guo, David R. Yarkony
Abstract
A neural network based quasi-diabatic potential energy matrix Hd that describes the photodissociation of formaldehyde involving the two lowest singlet states S0 and S1 is constructed. It has strict complete nuclear permutation inversion symmetry encoded and can reproduce high level ab initio electronic structure data, including energies, energy gradients, and derivative couplings, with excellent accuracy. It has been fully saturated in the configuration space to cover all possible reaction pathways with a trajectory-guided point sampling approach. This Hd will not only enable the accurate full-dimensional dynamic simulations of the photodissociation of formaldehyde involving S0 and S1 but also provide a crucial ingredient for incorporating spin–orbit couplings into a diabatic framework, thus ultimately enabling the study of both internal conversion and intersystem crossing in formaldehyde on the same footing.