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 …

Scanning transmission electron microscopy (STEM) has emerged as a uniquely powerful
tool for structural and functional imaging of materials on the atomic level. Driven by …

Electron microscopy touches on nearly every aspect of modern life, underpinning materials
development for quantum computing, energy and medicine. We discuss the open, highly …

Over the past several decades, electron and scanning probe microscopes have become
critical components of condensed matter physics, materials science and chemistry research …

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and
spectroscopy of materials on length scales ranging from microns to atoms. By using a high …

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 …

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 …

The use of statistical/machine learning (ML) approaches to materials science is
experiencing explosive growth. Here, we review recent work focusing on the generation and …

Abstract Machine learning (ML) has become critical for post-acquisition data analysis in
(scanning) transmission electron microscopy,(S) TEM, imaging and spectroscopy. An …