Wang Yuchen, Guan Yafu, Guo Hua, Yarkony David R
Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA.
J Chem Phys. 2021 Mar 7;154(9):094121. doi: 10.1063/5.0037684.
Global coupled three-state two-channel potential energy and property/interaction (dipole and spin-orbit coupling) surfaces for the dissociation of NH(Ã) into NH + H and NH + H are reported. The permutational invariant polynomial-neural network approach is used to simultaneously fit and diabatize the electronic Hamiltonian by fitting the energies, energy gradients, and derivative couplings of the two coupled lowest-lying singlet states as well as fitting the energy and energy gradients of the lowest-lying triplet state. The key issue in fitting property matrix elements in the diabatic basis is that the diabatic surfaces must be smooth, that is, the diabatization must remove spikes in the original adiabatic property surfaces attributable to the switch of electronic wavefunctions at the conical intersection seam. Here, we employ the fit potential energy matrix to transform properties in the adiabatic representation to a quasi-diabatic representation and remove the discontinuity near the conical intersection seam. The property matrix elements can then be fit with smooth neural network functions. The coupled potential energy surfaces along with the dipole and spin-orbit coupling surfaces will enable more accurate and complete treatment of optical transitions, as well as nonadiabatic internal conversion and intersystem crossing.
报道了用于NH(Ã)解离为NH + H和NH + H的全局耦合三态双通道势能以及性质/相互作用（偶极和自旋 - 轨道耦合）表面。采用置换不变多项式神经网络方法，通过拟合两个耦合的最低单重态的能量、能量梯度和导数耦合以及拟合最低三重态的能量和能量梯度，来同时拟合和绝热化电子哈密顿量。在绝热基中拟合性质矩阵元的关键问题是绝热表面必须平滑，也就是说，绝热化必须消除原始绝热性质表面上由于锥形交叉缝处电子波函数切换而产生的尖峰。在这里，我们使用拟合势能矩阵将绝热表示中的性质转换为准绝热表示，并消除锥形交叉缝附近的不连续性。然后可以用平滑的神经网络函数拟合性质矩阵元。耦合的势能表面以及偶极和自旋 - 轨道耦合表面将能够更准确和完整地处理光学跃迁，以及非绝热内转换和系间窜越。
