Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …

The fields of brain‐inspired computing, robotics, and, more broadly, artificial intelligence (AI)
seek to implement knowledge gleaned from the natural world into human‐designed …

Neuromorphic computing and artificial intelligence hardware generally aims to emulate
features found in biological neural circuit components and to enable the development of …

Coherent imaging techniques provide an unparalleled multi-scale view of materials across
scientific and technological fields, from structural materials to quantum devices, from …

The problem of phase retrieval underlies various imaging methods from astronomy to
nanoscale imaging. Traditional phase retrieval methods are iterative and are therefore …

Understanding and interpreting dynamics of functional materials in situ is a grand challenge
in physics and materials science due to the difficulty of experimentally probing materials at …

Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-
picosecond time resolutions, but these capabilities come with large volumes of data, which …

In this study we demonstrate the possibility of finding interpretable domain-appropriate
models of biological images and propose that such a strategy can be used to derive …

Electrocatalysis is at the heart of a broad range of physicochemical applications that play an
important role in the present and future of a sustainable economy. Among the myriad of …