Potential energy surfaces for high-energy N + O2collisions

Zoltan Varga, Yang Liu, Jun Li, Yuliya Paukku, Hua Guo, Donald G. Truhlar

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Potential energy surfaces for high-energy collisions between an oxygen molecule and a nitrogen atom are useful for modeling chemical dynamics in shock waves. In the present work, we present doublet, quartet, and sextet potential energy surfaces that are suitable for studying collisions of O2(3ςg-) with N(4S) in the electronically adiabatic approximation. Two sets of surfaces are developed, one using neural networks (NNs) with permutationally invariant polynomials (PIPs) and one with the least-squares many-body (MB) method, where a two-body part is an accurate diatomic potential and the three-body part is expressed with connected PIPs in mixed-exponential-Gaussian bond order variables (MEGs). We find, using the same dataset for both fits, that the fitting performance of the PIP-NN method is significantly better than that of the MB-PIP-MEG method, even though the MB-PIP-MEG fit uses a higher-order PIP than those used in previous MB-PIP-MEG fits of related systems (such as N4 and N2O2). However, the evaluation of the PIP-NN fit in trajectory calculations requires about 5 times more computer time than is required for the MB-PIP-MEG fit.

Original languageEnglish (US)
Article number084304
JournalJournal of Chemical Physics
Issue number8
StatePublished - Feb 28 2021

Bibliographical note

Funding Information:
Continuing discussions with Tom Schwartzentruber and Graham Candler are greatly appreciated. Computational resources were provided by the Department of Aerospace Engineering and Mechanics at the University of Minnesota and by the Minnesota Supercomputing Institute. The work of Y.L. and J.L. was supported by the National Natural Science Foundation of China (Grant No. 21973009) and the Chongqing Municipal Natural Science Foundation (Grant No. cstc2019jcyj-msxmX0087). The work of H.G. and D.G.T. was supported by the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award No. DE-SC0015997.

Publisher Copyright:
© 2021 Author(s).

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