# Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy

arXiv: https://arxiv.org/abs/1811.00080 · HTML: https://mlsanity.com/paper/1811.00080/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns](https://mlsanity.com/paper/2202.00204/) (2022)
- [Uncovering Temporal Patterns in Visualizations of High-Dimensional Data](https://mlsanity.com/paper/2403.19040/) (2024)
- [Context-Aware Deep Learning for Defect Classification in Atomic-Resolution STEM](https://mlsanity.com/paper/2606.09419/) (2026)

## Abstract (arXiv)

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.