Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable
part of physics research, with domain applications ranging from theory and materials …

Many material and biological samples in scientific imaging are characterized by nonlocal
repeating structures. These are studied using scanning electron microscopy and electron …

While aberration correction for scanning transmission electron microscopes (STEMs)
dramatically increased the spatial resolution obtainable in the images of materials that are …

Electron tomography (ET) plays an important role in revealing biological structures, ranging
from macromolecular to subcellular scale. Due to limited tilt angles, ET reconstruction …

Compressed sensing algorithms are used to decrease electron microscope scan time and
electron beam exposure with minimal information loss. Following successful applications of …

The concept of compressed sensing was recently proposed to significantly reduce the
electron dose in scanning transmission electron microscopy (STEM) while still maintaining …

The breadth, complexity, and volume of data generated by materials characterization using
various forms of microscopy has expanded significantly. Combined with increases in …

Recent advances in scanning transmission electron microscopy (STEM) have rekindled
interest in multi-channel detectors and prompted the exploration of unconventional scan …

The evolution of the scanning modules for scanning transmission electron microscopes
(STEM) allows now to generate arbitrary scan pathways, an approach currently explored to …