用神经网络模型和数据驱动的群分析方法计算电子-四氢呋喃的自洽截面
Self-consistent electron-THF cross sections derived using data-driven swarm analysis with a neural network model
摘要：
我们提出了一组气体四氢呋喃（THF）中电子输运的自洽截面，其改进了我们先前研究中发表的集[J. de Urquijo et al.，J. Chem. Phys. 151，054309（2019）]，提出了对准弹性动量转移、中性解离、电离和电子附着截面的修改。这些调整是通过分析脉冲汤森群输运系数，电子输运在纯THF和THF与氩气的混合物。为了自动化该分析，我们采用了一个神经网络模型，该模型经过训练，可以解决LXCat项目中真实截面的逆群问题。通过比较分析的群输运系数测量值与通过Boltzmann方程的数值解模拟的测量值，评估了所提出的改进THF截面集的准确性、完整性和自洽性。
英文摘要：
We present a set of self-consistent cross sections for electron transport in gaseous tetrahydrofuran (THF), that refines the set published in our previous study [J. de Urquijo et al., J. Chem. Phys. 151, 054309 (2019)] by proposing modifications to the quasielastic momentum transfer, neutral dissociation, ionisation and electron attachment cross sections. These adjustments are made through the analysis of pulsed-Townsend swarm transport coefficients, for electron transport in pure THF and in mixtures of THF with argon. To automate this analysis, we employ a neural network model that is trained to solve this inverse swarm problem for realistic cross sections from the LXCat project. The accuracy, completeness and self-consistency of the proposed refined THF cross section set is assessed by comparing the analysed swarm transport coefficient measurements to those simulated via the numerical solution of Boltzmann's equation.
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