DeepCSNet: a deep learning method for predicting electron-impact doubly differential ionization cross sections

Electron-impact ionization cross sections of atoms and molecules are essential for plasma modeling, yet their acquisition remains challenging. Experimental measurements often face difficulties due to particle instability, and ab initio calculations become computationally prohibitive as molecular complexity increases. Existing AI-based prediction methods suffer from limited data availability and poor generalization. To address these issues, we propose DeepCSNet, a deep learning approach designed to predict electron-impact ionization cross sections using minimal training data. We present two configurations of DeepCSNet: one tailored for specific molecules and another for various molecules. Our numerical results, focusing on electron-impact doubly differential ionization cross sections, demonstrate DeepCSNet's superior generalization ability, accurately predicting cross sections across a wide range of energies and incident angles. Additionally, DeepCSNet shows promising results in predicting cross sections for previously unencountered molecules, highlighting its potential for practical applications.