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

TL;DR

DeepCSNet is a deep learning model that accurately predicts electron-impact ionization cross sections for atoms and molecules, even with limited data and for molecules not seen during training.

Contribution

We introduce DeepCSNet, a novel deep learning approach capable of predicting ionization cross sections with high accuracy and good generalization, addressing data scarcity and computational challenges.

Findings

Achieves less than 5% relative L2 error in predictions.

Successfully generalizes to unseen molecules and a wide energy range.

Effective for large molecules with over 10 atoms.

Abstract

Electron-impact ionization cross sections of atoms and molecules are essential for plasma modelling. However, experimentally determining the absolute cross sections is not easy, 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 limited training data. We present two configurations of DeepCSNet: one tailored for specific molecules and another for various molecules. Both configurations can typically achieve a relative L2 error less than 5%. The present numerical results, focusing on electron-impact doubly differential ionization cross sections, demonstrate DeepCSNet's generalization ability, predicting cross sections across a wide range of energies and incident angles. Additionally, DeepCSNet shows promising results in predicting cross sections for molecules not included in the training set, even large molecules with more than 10 constituent atoms, highlighting its potential for practical applications.