Three-Dimensional Diabatic Potential Energy Surfaces of Thiophenol withNeural Networks  

神经网络方法构建苯硫酚三维非绝热势能面

作　　者：Chaofan Li Siting Hou Changjian Xie 李超凡;侯思婷;谢长建(西北大学现代物理研究所,西安710127;陕西省理论物理前沿重点实验室,西安710127)

机构地区：[1]Institute of Modern Physics,Northwest University,Xi'an 710127,China [2]Shaanxi Key Laboratory for Theoretical Physics Frontiers,Xi'an 710127,China

出　　处：《Chinese Journal of Chemical Physics》2021年第6期825-832,I0003,共9页化学物理学报（英文）

基　　金：supported by the National Natural Science Foundation of China(No.22073073);Changjian Xie thanks the Startup Foundation of Northwest University;The Double First-class University Construction Project of Northwest University is acknowledged。

摘　　要：Three-dimensional(3D)diabatic potential energy surfaces(PESs)of thiophenol involving the S0,and coupled 1ππ^(*) and 1πσ^(*) states were constructed by a neural network approach.Specifically,the diabatization of the PESs for the 1ππ^(*) and 1πσ^(*) states was achieved by the fitting approach with neural networks,which was merely based on adiabatic energies but with the correct symmetry constraint on the off-diagonal term in the diabatic potential energy matrix.The root mean square errors(RMSEs)of the neural network fitting for all three states were found to be quite small(<4 meV),which suggests the high accuracy of the neural network method.The computed low-lying energy levels of the S_(0) state and lifetime of the 0^(0) state of S_(1) on the neural network PESs are found to be in good agreement with those from the earlier diabatic PESs,which validates the accuracy and reliability of the PESs fitted by the neural network approach.

关 键 词：Diabatic potential energy surfaces Neural networks PHOTODISSOCIATION 

分 类 号：O625.32[理学—有机化学] TP183[理学—化学]