Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices
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. Here, 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$$(\tilde{X} / \tilde{B})$$ and NH3$$(\tilde{X} $$/Ã) non-adiabatic photodissociation.
- Authors:
-
- Univ. of New Mexico, Albuquerque, NM (United States)
- Johns Hopkins Univ., Baltimore, MD (United States); Stanford Univ., CA (United States)
- Johns Hopkins Univ., Baltimore, MD (United States)
- Publication Date:
- Research Org.:
- Univ. of New Mexico, Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1612363
- Alternate Identifier(s):
- OSTI ID: 1476847
- Grant/Contract Number:
- SC0015997
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Chemical Physics
- Additional Journal Information:
- Journal Volume: 149; Journal Issue: 14; Journal ID: ISSN 0021-9606
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Chemistry; Physics; Quantum chemical dynamics; Artificial neural networks; Diabatization; Machine learning; Potential energy surfaces; Photodissociation; Absorption spectroscopy
Citation Formats
Xie, Changjian, Zhu, Xiaolei, Yarkony, David R., and Guo, Hua. Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices. United States: N. p., 2018.
Web. doi:10.1063/1.5054310.
Xie, Changjian, Zhu, Xiaolei, Yarkony, David R., & Guo, Hua. Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices. United States. https://doi.org/10.1063/1.5054310
Xie, Changjian, Zhu, Xiaolei, Yarkony, David R., and Guo, Hua. Tue .
"Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices". United States. https://doi.org/10.1063/1.5054310. https://www.osti.gov/servlets/purl/1612363.
@article{osti_1612363,
title = {Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices},
author = {Xie, Changjian and Zhu, Xiaolei and Yarkony, David R. and Guo, Hua},
abstractNote = {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. Here, 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$(\tilde{X} / \tilde{B})$ and NH3$(\tilde{X} $/Ã) non-adiabatic photodissociation.},
doi = {10.1063/1.5054310},
journal = {Journal of Chemical Physics},
number = 14,
volume = 149,
place = {United States},
year = {Tue Oct 09 00:00:00 EDT 2018},
month = {Tue Oct 09 00:00:00 EDT 2018}
}
Web of Science
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