Scanning precession electron diffraction involves the acquisition of a two-dimensional precession electron diffraction pattern at every probe position in a two-dimensional scan. The data typically comprise many more diffraction patterns than the number of distinct microstructural volume elements (e.g. crystals) in the region sampled. A dimensionality reduction, ideally to one representative diffraction pattern per distinct element, may then be sought. Further, some diffraction patterns will contain contributions from multiple crystals sampled along the beam path, which may be unmixed by harnessing this oversampling. Here, we report on the application of unsupervised machine learning methods to achieve both dimensionality reduction and signal unmixing. Potential artefacts are discussed and precession electron diffraction is demonstrated to improve results by reducing the impact of bending and dynamical diffraction so that the data better approximate the case in which each crystal yields a given diffraction pattern.

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无监督机器学习应用于进动电子衍射数据的扫描

扫描进动电子衍射涉及在二维扫描的每个探针位置处获取二维进动电子衍射图案。数据通常包括比采样区域中不同的微结构体元素（例如晶体）数量更多的衍射图样。然后可以寻求尺寸减小，理想地是每个不同元素具有一个代表性的衍射图案。此外，某些衍射图将包含沿光束路径采样的多个晶体的贡献，可以利用这种过度采样将其混合。在这里，我们报告了无监督机器学习方法在实现降维和信号分解方面的应用。