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.
Phys Chem Chem Phys. 2019 Sep 18;21(36):20372-20383. doi: 10.1039/c9cp03592c.
A general neural network (NN)-fitting procedure based on nonadiabatic couplings is proposed to generate coupled two-state diabatic potential energy surfaces (PESs) with conical intersections. The elements of the diabatic potential energy matrix (DPEM) can be obtained directly from a combination of the NN outputs in principle. Instead, to achieve higher accuracy, the adiabatic-to-diabatic transformation (ADT) angle (mixing angle) for each geometry is first solved from the NN outputs, followed by individual NN fittings of the three terms of the DPEM, which are calculated from the ab initio adiabatic energies and solved mixing angles. The procedure is applied to construct a new set of two-state diabatic potential energy surfaces of ClH2. The ab initio data including adiabatic energies and derivative couplings are well reproduced. Furthermore, the current diabatization procedure can describe well the vicinity of conical intersections in high potential energy regions, which are located in the T-shaped (C2v) structure of Cl-H2. The diabatic quantum dynamical results on diabatic PESs show large differences as compared with the adiabatic results in high collision energy regions, suggesting the significance of nonadiabatic processes in conical intersection regions at high energies.
提出了一种基于非绝热耦合的通用神经网络（NN）拟合程序，用于生成具有交叉点的耦合双态绝热势能面（PES）。原则上，DPEM 的绝热势能矩阵（DPEM）元素可以直接从 NN 输出的组合中获得。但是，为了获得更高的精度，首先从 NN 输出中求解每个几何形状的绝热到非绝热变换（ADT）角（混合角），然后分别对 DPEM 的三个项进行单独的 NN 拟合，这些项是从从头算绝热能量和求解的混合角计算得出的。该程序应用于构建一组新的 ClH2 双态绝热势能面。很好地再现了包括绝热能和导数耦合在内的从头算数据。此外，当前的键合程序可以很好地描述高能区域交叉点附近的情况，这些交叉点位于 Cl-H2 的 T 形（C2v）结构中。与高能区域的绝热结果相比，在非绝热PES 上的量子动力学结果存在很大差异，这表明在高能区域交叉点区域中非绝热过程的重要性。
