Predictability of machine learning framework in cross-section data

Abstract

Today, the use of artificial intelligence in electron optics, as in many other fields, has begun to increase. In this scope, we present a machine learning framework to predict experimental cross-section data. Our framework includes 8 deep learning models and 13 different machine learning algorithms that learn the fundamental structure of the data. This article aims to develop a machine learning framework to accurately predict double-differential cross-section values. This approach combines multiple models such as convolutional neural networks, machine learning algorithms, and autoencoders to create a more robust prediction system. The data for training the models are obtained from experimental data for different atomic and molecular targets. We developed a methodology for learning tasks, mainly using rigorous prediction error limits. Prediction results show that the machine learning framework can predict the scattering angle and energy of scattering electrons with high accuracy, with an R-squared score of up to 99% and a mean squared error of <0.7. This performance result demonstrates that the proposed machine learning framework can be used to predict electron scattering events, which could be useful for applications such as medical physics.