Hong Yingyue, Yin Zhengxi, Guan Yafu, Zhang Zhaojun, 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, P.R. China 116023.
University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
J Phys Chem Lett. 2020 Sep 17;11(18):7552-7558. doi: 10.1021/acs.jpclett.0c02173. Epub 2020 Aug 27.
We propose a numerically simple and straightforward, yet accurate and efficient neural networks-based fitting strategy to construct coupled potential energy surfaces (PESs) in a quasi-diabatic representation. The fundamental invariants are incorporated to account for the complete nuclear permutation inversion symmetry. Instead of derivative couplings or interstate couplings, a so-called modified derivative coupling term is fitted by neural networks, resulting in accurate description of near degeneracy points, such as the conical intersections. The adiabatic energies, energy gradients, and derivative couplings are well reproduced, and the vanishing of derivative couplings as well as the isotropic topography of adiabatic and diabatic energies in asymptotic regions are automatically satisfied. All of these features of the coupled global PESs are requisite for accurate dynamics simulations. Our approach is expected to be very useful in developing highly accurate coupled PESs in a quasi-diabatic representation in an efficient machine learning-based way.
我们提出了一种数值简单直接、准确高效的基于神经网络的拟合策略，用于在准绝热表示中构建耦合势能面（PESs）。纳入基本不变量以考虑完整的核置换反演对称性。通过神经网络拟合一个所谓的修正导数耦合项，而不是导数耦合或态间耦合，从而精确描述近简并点，如锥形交叉点。绝热能量、能量梯度和导数耦合都能得到很好的再现，并且自动满足渐近区域中导数耦合的消失以及绝热和非绝热能量的各向同性形貌。耦合全局PESs的所有这些特性都是精确动力学模拟所必需的。我们的方法有望以基于机器学习的高效方式，在开发准绝热表示的高精度耦合PESs方面非常有用。
