Report
Applications of machine learning in ion beam analysis of materials
| العنوان: | Applications of machine learning in ion beam analysis of materials |
|---|---|
| المؤلفون: | da Silva, Tiago Fiorini |
| سنة النشر: | 2024 |
| المجموعة: | Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Condensed Matter - Materials Science, Physics - Data Analysis, Statistics and Probability |
| الوصف: | 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. Comment: 9 pages, 3 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2412.12312 |
| رقم الانضمام: | edsarx.2412.12312 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |