Shen Yifan, Yarkony David R
Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States.
J Phys Chem A. 2020 Jun 4;124(22):4539-4548. doi: 10.1021/acs.jpca.0c02763. Epub 2020 May 21.
We present, for systems of moderate dimension, a fitting framework to construct quasi-diabatic Hamiltonians that accurately represent adiabatic electronic structure data including the effects of conical intersections. The framework introduced here minimizes the difference between the fit prediction and the data obtained in the adiabatic representation, which is singular at a conical intersection seam. We define a general and flexible merit function to allow arbitrary representations and propose a representation to measure the fit- difference at geometries near electronic degeneracies. A fit Hamiltonian may behave poorly in insufficiently sampled regions, in which case a machine learning theory analysis of the fit representation suggests a regularization to address the deficiency. Our fitting framework including the regularization is used to construct the full 39-dimensional coupled diabatic potential energy surfaces for cyclopentoxy relevant to cyclopentoxide photoelectron detachment.
对于中等维度的系统,我们提出了一个拟合框架,用于构建准绝热哈密顿量,该哈密顿量能准确表示绝热电子结构数据,包括锥形交叉点的影响。这里引入的框架将拟合预测与在绝热表示中获得的数据之间的差异最小化,而绝热表示在锥形交叉点接缝处是奇异的。我们定义了一个通用且灵活的优值函数,以允许任意表示,并提出一种表示方法来测量电子简并附近几何构型处的拟合差异。拟合哈密顿量在采样不足的区域可能表现不佳,在这种情况下,对拟合表示的机器学习理论分析表明需要一种正则化方法来解决这一不足。我们包括正则化的拟合框架用于构建与环戊氧基光电子脱离相关的环戊氧基的完整39维耦合非绝热势能面。