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Title: Quantum and quasi-classical dynamics of the OH + CO → H + CO{sub 2} reaction on a new permutationally invariant neural network potential energy surface

Abstract

A permutationally invariant global potential energy surface for the HOCO system is reported by fitting a larger number of high-level ab initio points using the newly proposed permutation invariant polynomial-neural network method. The small fitting error (∼5 meV) indicates a faithful representation of the potential energy surface over a large configuration space. Full-dimensional quantum and quasi-classical trajectory studies of the title reaction were performed on this potential energy surface. While the results suggest that the differences between this and an earlier neural network fits are small, discrepancies with state-to-state experimental data remain significant.

Authors:
;  [1]; ;  [2]
  1. Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131 (United States)
  2. State key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian 116023 (China)
Publication Date:
OSTI Identifier:
22255178
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Chemical Physics; Journal Volume: 140; Journal Issue: 4; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; CARBON DIOXIDE; CARBON MONOXIDE; NEURAL NETWORKS; POLYNOMIALS; POTENTIAL ENERGY; SURFACES

Citation Formats

Li, Jun, Guo, Hua, E-mail: zhangdh@dicp.ac.cn, E-mail: hguo@unm.edu, Chen, Jun, and Zhang, Dong H., E-mail: zhangdh@dicp.ac.cn, E-mail: hguo@unm.edu. Quantum and quasi-classical dynamics of the OH + CO → H + CO{sub 2} reaction on a new permutationally invariant neural network potential energy surface. United States: N. p., 2014. Web. doi:10.1063/1.4863138.
Li, Jun, Guo, Hua, E-mail: zhangdh@dicp.ac.cn, E-mail: hguo@unm.edu, Chen, Jun, & Zhang, Dong H., E-mail: zhangdh@dicp.ac.cn, E-mail: hguo@unm.edu. Quantum and quasi-classical dynamics of the OH + CO → H + CO{sub 2} reaction on a new permutationally invariant neural network potential energy surface. United States. doi:10.1063/1.4863138.
Li, Jun, Guo, Hua, E-mail: zhangdh@dicp.ac.cn, E-mail: hguo@unm.edu, Chen, Jun, and Zhang, Dong H., E-mail: zhangdh@dicp.ac.cn, E-mail: hguo@unm.edu. 2014. "Quantum and quasi-classical dynamics of the OH + CO → H + CO{sub 2} reaction on a new permutationally invariant neural network potential energy surface". United States. doi:10.1063/1.4863138.
@article{osti_22255178,
title = {Quantum and quasi-classical dynamics of the OH + CO → H + CO{sub 2} reaction on a new permutationally invariant neural network potential energy surface},
author = {Li, Jun and Guo, Hua, E-mail: zhangdh@dicp.ac.cn, E-mail: hguo@unm.edu and Chen, Jun and Zhang, Dong H., E-mail: zhangdh@dicp.ac.cn, E-mail: hguo@unm.edu},
abstractNote = {A permutationally invariant global potential energy surface for the HOCO system is reported by fitting a larger number of high-level ab initio points using the newly proposed permutation invariant polynomial-neural network method. The small fitting error (∼5 meV) indicates a faithful representation of the potential energy surface over a large configuration space. Full-dimensional quantum and quasi-classical trajectory studies of the title reaction were performed on this potential energy surface. While the results suggest that the differences between this and an earlier neural network fits are small, discrepancies with state-to-state experimental data remain significant.},
doi = {10.1063/1.4863138},
journal = {Journal of Chemical Physics},
number = 4,
volume = 140,
place = {United States},
year = 2014,
month = 1
}
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