Applications of machine learning in ion beam analysis of materials

TL;DR

This paper reviews how machine learning algorithms are transforming ion beam analysis by improving data processing speed, automation, and interpretability, with practical applications across various techniques and future outlooks.

Contribution

It provides a comprehensive overview of machine learning applications in IBA, highlighting recent advancements and potential for future research.

Findings

ML accelerates data analysis in IBA

ML automates repetitive tasks in IBA workflows

ML enhances interpretability of IBA results

Abstract

Ion Beam Analysis (IBA) is an established tool for material characterization, providing precise information on elemental composition, depth profiles, and structural information in the region near the surface of materials. However, traditional data processing methods can be slow and computationally intensive, limiting the efficiency and speed of the analysis. This article explores the current landscape of applying Machine Learning Algorithms (MLA) in the field of IBA, demonstrating the immense potential to optimize and accelerate processes. We present how ML has been employed to extract valuable insights from large datasets, automate repetitive tasks, and enhance the interpretability of results, with practical examples of applications in various IBA techniques, such as RBS, PIXE, and others. Finally, perspectives on using MLA to approach open problems in IBA are also discussed.