Work

Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices

Year: 2018

Type: article

Abstract: A machine learning method is proposed for representing the elements of diabatic potential energy matrices (PEMs) with high fidelity. This is an extension of the so-called permutation invariant polynomial-neural network (PIP-NN) method for representing adiabatic potential energy surfaces. While for one-dimensional irreducible representations the diagonal elements of a diabatic PEM are invariant under exchange of identical nuclei in a molecular system, the off-diagonal elements require special symmetry consideration, particularly in the presence of a conical intersection. A multiplicative factor is introduced to take into consideration the particular symmetry properties while maintaining the PIP-NN framework. We demonstrate here that the extended PIP-NN approach is accurate in representing diabatic PEMs, as evidenced by small fitting errors and by the reproduction of absorption spectra and product branching ratios in both H2O(X̃/B̃) and NH3(X̃/Ã) non-adiabatic photodissociation.

Source: The Journal of Chemical Physics

DOI: 10.1063/1.5054310

Authors: Changjian Xie, Xiaolei Zhu, David R. Yarkony, Hua Guo

Institutions: University of New Mexico, Johns Hopkins University

Language: English

Topic: Advanced Chemical Physics Studies

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: U.S. Department of Energy

Award: NONADIABATIC PHOTOCHEMISTRY