Yin Zhengxi, Braams Bastiaan J, Guan Yafu, Fu Bina, Zhang Dong H
State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China.
Centrum Wiskunde & Informatica (CWI), The Dutch national Center for Mathematics and Computer Science, The Netherlands.
Phys Chem Chem Phys. 2021 Jan 21;23(2):1082-1091. doi: 10.1039/d0cp05047d.
The fundamental invariant neural network (FI-NN) approach is developed to represent coupled potential energy surfaces in quasidiabatic representations with two-dimensional irreducible representations of the complete nuclear permutation and inversion (CNPI) group. The particular symmetry properties of the diabatic potential energy matrix of H3 for the 1A' and 2A' electronic states were resolved arising from the E symmetry in the D3h point group. This FI-NN framework with symmetry adaption is used to construct a new quasidiabatic representation of H3, which reproduces accurately the ab initio energies and derivative information with perfect symmetry behaviors and extremely small fitting errors. The quantum dynamics results on the new FI-NN diabatic PESs give rise to accurate oscillation patterns in the product state-resolved differential cross sections. These results strongly support the accuracy and efficiency of the FI-NN approach to construct reliable diabatic representations with complicated symmetry problems.
基本不变神经网络（FI-NN）方法被开发用于在准绝热表示中，用完全核排列与反演（CNPI）群的二维不可约表示来表示耦合势能面。对于H₃的¹A'和²A'电子态，其非绝热势能矩阵的特定对称性质源于D₃h点群中的E对称性。这种具有对称性适配的FI-NN框架被用于构建H₃的一种新的准绝热表示，它能精确再现从头算能量和导数信息，具有完美的对称行为且拟合误差极小。基于新的FI-NN非绝热势能面的量子动力学结果在产物态分辨的微分截面中产生了精确的振荡模式。这些结果有力地支持了FI-NN方法在构建具有复杂对称问题的可靠非绝热表示方面的准确性和效率。
