Manifold learning of four-dimensional scanning transmission electron microscopy

npj Computational Materials

Sergei V. Kalinin¹  Xin Li¹  Ondrej E. Dyck¹  Andrew R. Lupini¹  Stephen Jesse¹  Mark P. Oxley¹  Leland McInnes²  John Healy² 

[1] Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, 37831, Oak Ridge, TN, USA;Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, 37831, Oak Ridge, TN, USA;Tutte Institute for Mathematics and Computing, Ottawa, Canada;

DOI  :  10.1038/s41524-018-0139-y

来源: Springer

【 摘 要 】

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 leaning 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.

【 授权许可】

CC BY