深度学习用于核心损失电子能量损失光谱中的自动化材料表征
Arno Annys1, Daen Jannis1,2, Johan Verbeeck3,4
1EMAT, University of Antwerp, 2020, Antwerp, Belgium.
Scientific reports
|August 22, 2023
概括
本研究引入了电子能量损失光谱 (EELS) 的大量合成数据集,以自动化元素识别. 这使得在频谱成像中实现完全自动化的元素映射,克服了EELS分析的关键限制.
科学领域:
- 材料科学 材料科学 材料科学
- 频谱学是一种光谱学.
- 计算化学计算化学
背景情况:
- 电子能量损失光谱 (EELS) 是电子显微镜元素分析的强大工具.
- 在EELS中手动识别核心损失边缘是耗时且具有挑战性的,特别是在光谱成像方面.
- 在EELS中自动化元素识别对于高效的材料表征至关重要.
研究的目的:
- 开发一个全面的合成数据集,用于训练机器学习模型进行EELS分析.
- 评估各种神经网络架构,用于自动化核心损失边缘识别.
- 在光谱成像中使用机器学习来演示完全自动化的元素映射.
主要方法:
- 一个由736,000个标记的EELS光谱组成的合成数据集的生成,涵盖了107个核心损失边和80个元素.
- 培训和评估各种神经网络架构,包括MLP,CNN,U-Nets,ResNets,视觉转换器和紧卷积转换器.
- 使用一组神经网络来提高元素识别的性能.
主要成果:
- 合成数据集有效地代表了实验EELS核心损失边缘与通用寿命扩展.
- 各种神经网络架构在识别核心损失边缘方面表现出了熟练.
- 一个整体网络实现了高性能,使得在频谱图像中实现完全自动化的元素映射.
结论:
- 开发的合成数据集和训练有素的神经网络有助于在EELS.中实现自动化元素识别.
- 这种方法显著提高了频谱成像中的元素映射的效率和准确性.
- 该方法为使用EELS.的先进,自动化材料分析铺平了道路.
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