Investigation of the cross sections for electron collision ionization of complex molecules

An accurate and computationally efficient determination of the cross sections for electron collision ionization of molecules has various applications, such as plasma physics and atmospheric science. In the case of large molecules, ab initio calculations are often difficult and time-consuming. Here, we develop a feed forward neural network to predict the electron impact ionization cross sections of complex molecules. The training (predicting) set in the method consists of a series of theoretical ionization cross sections for small (large) molecules obtained from the combined model, which integrates the Binary-Encounter-Bethe and Deutsch-Märk models. Several complex systems or targets involving electron collision ionization are evaluated, including molecules such as CH${}_4$, C${}_3$H${}_8$, C${}_5$H${}_8$, C${}_6$H${}_{10}$, C${}_6$, C${}_2$H${}_6$O, and C${}_6$H${}_6$O. The root mean square errors of the trained and predicted cross sections by the $2\times 3\times 3\times 1$ neural network (compared to the values from the combined model) are found to be approximately .0086 and .0930 (in 10${}^{-20}$ cm${}^2$), respectively, (using the C${}_2$H${}_6$O molecule as an example), indicating our results are very high accuracy. The excellent agreement between the predicted values and the actual values indicates that the neural network is a practical and powerful tool for determining the electron collision ionization cross sections of complex molecules and can provide valuable insights into the dynamics process. Apart from its fundamental importance, this study has far-reaching implications for gas discharge, low-temperature plasmas, and fusion edge plasmas and so forth.