Energy transfer between vibrational modes can be quite facile, and it has been proposed as the dominant mechanism for energy pooling in extreme environments such as nonthermal plasmas and laser cavities. To understand such processes, we perform quasi-classical trajectory studies of CO(v) + CO(v) collisions on a new full-dimensional potential energy surface fit to high-level ab initio data using a neural network method and examine the key vibrational energy transfer channels. In addition to the highly efficient CO(v + 1) + CO(v − 1) channel, there exists a significant, sometimes dominant, CO(v + 2) + CO(v − 2) channel for large v states at low collision energies. The latter is shown to stem from the substantially increased interaction between highly vibrationally excited CO, which has a much larger dipole moment than at its equilibrium bond length. Finally, the vibrational state-specific cross sections and their energy dependence on the thermal range are predicted from a limited dataset using Gaussian process regression. The relevance of these results to plasma chemistry and laser engineering and the recently observed flipping of highly vibrationally excited CO adsorbates on a cold NaCl surface is discussed.
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7 August 2020
Research Article|
August 06 2020
Energy transfer between vibrationally excited carbon monoxide based on a highly accurate six-dimensional potential energy surface
Special Collection:
Machine Learning Meets Chemical Physics
Jun Chen
;
Jun Chen
1
Department of Chemistry and Chemical Biology, University of New Mexico
, Albuquerque, New Mexico 87131, USA
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Jun Li
;
Jun Li
2
School of Chemistry and Chemical Engineering, Chongqing University
, Chongqing 401331, China
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Joel M. Bowman
;
Joel M. Bowman
3
Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University
, Atlanta, Georgia 30322, USA
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Hua Guo
Hua Guo
a)
1
Department of Chemistry and Chemical Biology, University of New Mexico
, Albuquerque, New Mexico 87131, USA
a)Author to whom correspondence should be addressed: [email protected]
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a)Author to whom correspondence should be addressed: [email protected]
Note: This paper is part of the JCP Special Topic on Machine Learning Meets Chemical Physics.
J. Chem. Phys. 153, 054310 (2020)
Article history
Received:
May 25 2020
Accepted:
July 19 2020
Citation
Jun Chen, Jun Li, Joel M. Bowman, Hua Guo; Energy transfer between vibrationally excited carbon monoxide based on a highly accurate six-dimensional potential energy surface. J. Chem. Phys. 7 August 2020; 153 (5): 054310. https://doi.org/10.1063/5.0015101
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