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 …

Regularized inversion methods for image reconstruction are used widely due to their
tractability and their ability to combine complex physical sensor models with useful regularity …

The inpainting of deliberately and randomly sub-sampled images offers a potential means to
image specimens at a high resolution and under extremely low-dose conditions (⁠≤ 1 e−/Å …

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

Void collapse under shock loading has become a model problem to study the nucleation of
hot spots in high energy density materials. While experimental observation of this …

Electrochemical reactions and ionic transport underpin the operation of a broad range of
devices and applications, from energy storage and conversion to information technologies …

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

Atomic‐scale engineering typically involves bottom‐up approaches, leveraging parameters
such as temperature, partial pressures, and chemical affinity to promote spontaneous …

Sparse sampling schemes can broadly be classified into two main categories: static
sampling where the sampling pattern is predetermined, and dynamic sampling where each …