Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy

Abstract

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 the 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.

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Cited by in corpus (6)

• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Exploring order parameters and dynamic processes in disordered systems via variational autoencoders
• Visualising energy landscapes through manifold learning
• Imaging defects in two-dimensional crystals by convergent beam electron diffraction
• Structure retrieval from 4D-STEM: statistical analysis of potential pitfalls in high-dimensional data
• Entanglement Clustering for ground-stateable quantum many-body states