  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.
