Applications of machine learning in ion beam analysis of materials

Autor(es)

da Silva, Tiago Fiorini

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF VACUUM SCIENCE & TECHNOLOGY A; v. 43, n. 2, p. 9-pg., 2025-03-01.

Resumo

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 (MLAs) 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. (AU)

• Processo FAPESP: 22/03043-1 - Detectores gasosos microestruturados de radiação e suas aplicações
• Beneficiário: Tiago Fiorini da Silva
• Modalidade de apoio: Auxílio à Pesquisa - Projeto Inicial