Inelastic N+H collisions and quantum-classical rate coefficients: large datasets and machine learning predictions

Qizhen Hong; Loriano Storchi; Massimiliano Bartolomei; Fernando Pirani; Quanhua Sun; Cecilia Coletti

doi:10.1140/epjd/s10053-023-00688-4

2024 Impact factor 1.5

Atomic, Molecular, Optical and Plasma Physics

Atomic and Molecular Data and Their Applications: ICAMDATA 2022

Open Access

Eur. Phys. J. D (2023) 77: 128
https://doi.org/10.1140/epjd/s10053-023-00688-4

Regular Article – Atomic and Molecular Collisions

Inelastic N $_{2}$ +H $_{2}$ collisions and quantum-classical rate coefficients: large datasets and machine learning predictions

Qizhen Hong¹^,2, Loriano Storchi³, Massimiliano Bartolomei⁴, Fernando Pirani⁵, Quanhua Sun¹^,2 and Cecilia Coletti³^f

¹ State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, 100190, Beijing, China
² School of Engineering Science, University of Chinese Academy of Sciences, 100049, Beijing, China
³ Dipartimento di Farmacia, Università G. d’Annunzio Chieti-Pescara, via dei Vestini, 66100, Chieti, Italy
⁴ Instituto de Física Fundamental - CSIC, C/ Serrano 123, Madrid, Spain
⁵ Dipartimento di Chimica, Biologia e Biotecnologie, Università di Perugia, via Elce di Sotto 8, 06123, Perugia, Italy

^f ccoletti@unich.it

Received: 26 March 2023
Accepted: 26 May 2023
Published online: 5 July 2023

Abstract

Rate coefficients for vibrational energy transfer are calculated for collisions between molecular nitrogen and hydrogen in a wide range of temperature and of initial vibrational states ( $v \leq 40$ $v\le 40$ for N $_{2}$ $$_2$$ and $w \leq 10$ $w\le 10$ for H $_{2}$ $$_2$$ ). These data are needed for the modelling of discharges in N $_{2}$ $$_2$$ /H $_{2}$ $$_2$$ plasma or of atmospheric and interstellar medium chemistry in different temperature ranges. The calculations were performed by a mixed quantum-classical method, to recover quantum effects associated with the vibrational motion, on a refined potential energy surface. The obtained rates present striking discrepancies with those evaluated by first-order perturbation theories, like the SSH (Schwartz, Slavsky, Herzfeld) theory, which are often adopted in kinetic modelling. In addition, we present a detailed, though preliminary, analysis on the performance of different Machine Learning models based on the Gaussian Process or Neural Network techniques to produce complete datasets of inelastic scattering rate coefficients. Eventually, by using the selected models, we give the complete dataset (i.e., covering the whole vibrational ladder) of rate coefficients for the $N_{2} (v) + H_{2} (0) ⟶ N_{2} (v - Δ v) + H_{2} (0)$ $\textrm{N}_2(v)+\textrm{H}_2(0) \longrightarrow \textrm{N}_2(v-\Delta v)+\textrm{H}_2(0)$ , $Δ v = 1, 2, 3$ $\Delta v=1,2,3$ processes.

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjd/s10053-023-00688-4.

© The Author(s) 2023

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