In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

Multiscale and multimodal imaging of material structures and properties provides solid
ground on which materials theory and design can flourish. Recently, KAIST announced 10 …

Research in molecular sciences witnessed the rise and fall of Artificial Intelligence
(AI)/Machine Learning (ML) methods, especially artificial neural networks, few decades ago …

Recurrent neural networks have led to breakthroughs in natural language processing and
speech recognition. Here we show that recurrent networks, specifically long short-term …

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

An automated experiment in multimodal imaging to probe structural, chemical, and
functional behaviors in complex materials and elucidate the dominant physical mechanisms …

The functionality of ferroelastic domain walls in ferroelectric materials is explored in real‐
time via the in situ implementation of computer vision algorithms in scanning probe …

The pneumatic artificial muscle (PAM) actuated with different input frequencies and
mechanical loads suffers from complex dynamic asymmetric hysteresis behaviors, leading to …

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

For next-generation technology, magnetic systems are of interest due to the natural ability to
store information and, through spin transport, propagate this information for logic functions …