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无监督机器学习与4D扫描传输电子显微镜相结合,用于双模纳米结构分析

Koji Kimoto¹, Jun Kikkawa², Koji Harano²

¹Center for Basic Research On Materials, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan. kimoto.koji@nims.go.jp.

Scientific reports

|February 5, 2024

概括

无监督机器学习与四维扫描传输电子显微镜 (4D-STEM) 结合,可以在纳米尺度上详细分析晶体结构. 这种方法成功地识别了金属玻璃中的纳米级沉物,克服了传统方法的局限性.

科学领域:

材料科学材料科学材料科学
数据科学数据科学数据科学
纳米技术纳米技术

背景情况:

扫描传输电子显微镜 (STEM) 为材料分析提供高空间分辨率.
从四维STEM (4D-STEM) 这样的技术分析大数据集带来了计算挑战.
无监督机器学习为从复杂数据中提取有意义的信息提供了强大的工具.

研究的目的:

将无监督机器学习与4D-STEM相结合,用于高级晶体结构分析.
开发优化的机器学习方法来处理大规模的4D-STEM数据.
应用这些综合技术来表征材料中的纳米结构.

主要方法:

利用无监督的机器学习技术,包括非负矩阵分解 (NMF) 和等级聚类.
开发了定制软件用于数据预处理,NMF和针对电子衍射和STEM成像量身定制的层次聚类.
实施了带有交叉相关性的等级分类,用于旋转和转移纠正分析.

主要成果:

成功处理大规模的4D-STEM数据,使用缩小维度的技术.
已识别的纳米级晶体沉物 (约. 7nm) 在Zr-Cu-Al金属玻璃的无形矩阵内.
实现了用传统的STEM方法难以解决的沉物的检测.

结论:

将4D-STEM与优化的无监督机器学习集成,使材料纳米结构的综合双模分析成为可能.
这种方法提高了纳米尺度详细晶体结构分析的能力.
开发的方法提供了一个强大的工具,用于材料的特征,特别是复杂的系统,如金属玻璃.

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