论文标题
使用数据驱动的群体分析得出的自洽电子-THF横截面具有神经网络模型
Self-consistent electron-THF cross sections derived using data-driven swarm analysis with a neural network model
论文作者
论文摘要
我们提出了一组在气态四氢呋喃（THF）中用于电子传输的自一致的横截面，该截面完善了我们先前的研究中发表的集合[J. De Urquijo等，J。Chem。物理。 151，054309（2019）]提出对准动量转移，中性分离，电离和电子附着横截面的修改。这些调整是通过分析脉冲镇的群群传输系数的分析，用于在纯THF中的电子传输以及与氩气的混合物中的混合物。为了自动进行此分析，我们采用了一种神经网络模型，该模型经过训练，可以解决LXCAT项目的现实横截面的反向群问题。通过比较分析的群体传输系数测量值与通过Boltzmann's方程的数值解决方案模拟的群体传输系数测量值来评估所提出的精制THF横截面集的准确性，完整性和自隔离。
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.
