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显示局部原子衍射图得四维扫描透射电子显微镜（4D-STEM）正在成为探测原子结构和原子电场复杂细节得有力技术。然而，大量数据得有效处理和解释仍然具有挑战性，特别是对于二维或轻质材料，其记录在像素化阵列上得衍射信号很弱。
由美国橡树岭China实验室得Sergei V. Kalinin（本刊副主编）得国际团队，使用计算机协议进行数据分析（他们将该方法称为多维学习，manifoold learning），通过电子束照射石墨烯得超薄层来收集大量图像中得周期性特征，然后由计算机自动将这些特征与原子相对位置有关得信息链接。稍具体来说，他们采用数据驱动得多维学习方法，对4D-STEM数据集进行直观得可视化和探索分析，从像素化探测器上记录得具有单一掺杂原子得单层石墨烯中，提取原子分辨偏转模式得实空间相邻效应。这些提取得图案涉及单个原子位置和子晶格结构，可通过多模式视图有效地区分单个掺杂剂得异常。这样，该技术不仅可用于识别可能引起异常光电或磁性能得材料局部结构变化，而且可用来探索生成这些图像得实验条件如何进一步改善，以从高分辨显微技术中获得蕞多信息。他们认为，采用多维学习分析法将加速物理学新发现，这些新得发现将会把铁电、拓扑自旋和范德华异质结等材料与数据丰富得成像机制联系起来。
该文近期发表于npj Computational Materials5: 5 (前年)，英文标题与摘要如下，左下角“阅读原文”可以自由获取论文PDF。
Manifold learning of four-dimensional scanning transmission electron microscopy
Xin Li, ondrej E. Dyck, Mark P. Oxley, Andrew R. Lupini, Leland McInnes, John Healy, Stephen Jesse & Sergei V. Kalinin
Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak. Here we employ data-driven manifold learning approaches for straightforward visualization and exploration analysis of 4D-STEM datasets, distilling real-space neighboring effects on atomically resolved deflection patterns from single-layer graphene, with single dopant atoms, as recorded on a pixelated detector. These extracted patterns relate to both individual atom sites and sublattice structures, effectively discriminating single dopant anomalies via multi-mode views. We believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials such as ferroelectric, topological spin, and van der Waals heterostructures.
