标题： 机器学习在材料离子束分析中的应用 显示英文标题

标题： Applications of machine learning in ion beam analysis of materials

摘要： 离子束分析（IBA）是一种成熟用于材料表征的工具，可提供有关材料表面附近区域元素组成、深度剖面和结构信息的精确信息。 然而，传统的数据处理方法可能速度较慢且计算密集，限制了分析的效率和速度。 本文探讨了机器学习算法（MLA）在IBA领域的应用现状，展示了优化和加速过程的巨大潜力。 我们介绍了如何利用ML从大数据集中提取有价值的见解、自动化重复任务以及增强结果的可解释性，并提供了在各种IBA技术（如RBS、PIXE等）中的实际应用示例。 最后，还讨论了使用MLA解决IBA领域开放问题的前景。 显示英文摘要

摘要： 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.