卷积神经网络框架用于过渡金属X射线光电谱的自动分析
Lukas Pielsticker1, Rachel L Nicholls1, Serena DeBeer1
1Max Planck Institute for Chemical Energy Conversion, Stiftstr. 34-36, 45470, Muelheim an der Ruhr, Germany.
Analytica chimica acta
|June 16, 2023
概括
本研究介绍了一种机器学习框架,使用人工卷积神经网络来自动量化X射线光电谱 (XPS) 数据. 这种方法快速准确地确定样本组成,帮助研究人员使用大型数据集.
科学领域:
- 材料科学 材料科学 材料科学
- 表面科学是一门学科.
- 分析化学 分析化学
背景情况:
- X射线光电子光谱 (XPS) 对于分析样品组成和电子结构至关重要.
- 用于XPS数据分析的手动峰值安装是耗时的,需要专业知识.
- 来自现代仪器的越来越大的XPS数据集需要自动化分析解决方案.
研究的目的:
- 开发一个自动化,用户友好的框架,用于定量XPS数据分析.
- 为了利用监督机器学习,特别是卷积神经网络 (CNN),用于XPS量化.
- 为过渡金属XPS数据提供准确和快速的组成确定.
主要方法:
- 一个使用人工卷积神经网络 (CNN) 的监督机器学习框架.
- 在已知元素度的人工生成XP光谱上训练CNN.
- 采用脱落变量推理来量化预测不确定性.
主要成果:
- 开发了适用于全球的CNN模型,用于自动化XPS量化.
- 与传统的手动峰值安装方法相比,实现了具有竞争力的量化准确性.
- 证明了框架在各种元素和实验参数上的灵活性.
结论:
- 提出的基于CNN的框架为XPS数据分析提供了高效和准确的解决方案.
- 自动化量化帮助研究人员,特别是那些经验较少的人,处理大型XPS数据集.
- 该方法为手动分析提供了可靠的替代方案,提高了吞吐量和可访问性.
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