A review is presented of recent advancements in characterization and automation and how
this has led to a new generation of automated serial-sectioning facilities, which has allowed …

Physics‐driven discovery in an autonomous experiment has emerged as a dream
application of machine learning in physical sciences. Here, this work develops and …

Historically, the advent of robotics has important roots in metallurgy. The first industrial robot,
Unimate, was used by General Motors to handle hot metal—transporting die castings and …

Gathering 3D material microstructural information is time-consuming, expensive, and energy-
intensive. Acquisition of 3D data has been accelerated by developments in serial sectioning …

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

Abstract A Supervised Learning Approach for Dynamic Sampling (SLADS) addresses
traditional issues with the incorporation of stochastic processes into a compressed sensing …

This paper discusses the reconstruction of partially sampled spectrum-images to accelerate
the acquisition in scanning transmission electron microscopy (STEM). The problem of image …

Interfaces in energy materials and devices often involve beam‐sensitive materials such as
fast ionic, soft, or liquid phases. 4D scanning transmission electron microscopy (4D‐STEM) …

Microstructure characterisation has been greatly enhanced through the use of electron
backscatter diffraction (EBSD), where rich maps are generated through analysis of the …

Technological advances in electron microscopy, particularly improved detectors and
aberration correctors, have led to higher throughput and less invasive imaging of materials …