Toward a complete and comprehensive cross section database for electron scattering from NO using machine learning

Stokes, P.W., White, R.D., Campbell, L., and Brunger, M.J. (2021) Toward a complete and comprehensive cross section database for electron scattering from NO using machine learning. Journal of Chemical Physics, 155 (8). 084305.

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Abstract

We review experimental and theoretical cross sections for electron scattering in nitric oxide (NO) and form a comprehensive set of plausible cross sections. To assess the accuracy and self-consistency of our set, we also review electron swarm transport coefficients in pure NO and admixtures of NO in Ar, for which we perform a multi-term Boltzmann equation analysis. We address observed discrepancies with these experimental measurements by training an artificial neural network to solve the inverse problem of unfolding the underlying electron-NO cross sections while using our initial cross section set as a base for this refinement. In this way, we refine a suitable quasielastic momentum transfer cross section, a dissociative electron attachment cross section, and a neutral dissociation cross section. We confirm that the resulting refined cross section set has an improved agreement with the experimental swarm data over that achieved with our initial set. We also use our refined database to calculate electron transport coefficients in NO, across a large range of density-reduced electric fields from 0.003 to 10 000 Td.

Item ID: 70183
Item Type: Article (Research - C1)
ISSN: 1089-7690
Copyright Information: Published under an exclusive license by AIP Publishing.
Funders: Australian Research Council (ARC)
Projects and Grants: ARC DP180101655
Date Deposited: 19 Apr 2022 01:38
Downloads: Total: 57
Last 12 Months: 48
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