Manifold learning of four-dimensional scanning transmission electron microscopy

By Xin Li, Ondrej Dyck, M. Oxley, A. Lupini, Leland McInnes, John Healy, Stephen Jesse, S. Kalinin. 2018

56 citations recorded by the source corpus.

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.Artificial intelligence: getting more from microscopy imagesAutomatic algorithms can extract information on the atomic details of materials hidden in high-resolution microscopy images. An international team led by Sergei V. Kalinin at the Oak Ridge National Laboratory, USA, use a computer protocol for data analysis, called manifold learning, to find recurrent features in a large set of images collected by illuminating an ultrathin layer of graphene with a beam of electrons. Then, the computer automatically linked such features to information related to the relative position of the atoms. This technique may be used not only to identify local changes in the material structure that may be responsible for unusual optoelectronic or magnetic properties, but also to understand how the experimental conditions used to generate these images can be further improved to obtain the most from high-resolution microscopy techniques.

Subfields: Atomic and Molecular Physics, and Optics, Structural Biology, Surfaces, Coatings and Films, Representation & Self-Supervised Learning, AI for Physical Sciences

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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (2018, methodology): “Based upon preliminary releases of a soware implementation, UMAP has already found widespread use in the elds of bioinformatics [5, 12, 17, 46, 2, 45, 15], materials science [34, 23], and machine learning [14, 20, 21, 24, 19, 47] among others.”