Atom probe tomography (APT) provides three-dimensional compositional map** with sub-
nanometre resolution. The sensitivity of APT is in the range of parts per million for all …

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting
opportunity to revolutionize inorganic materials discovery and development. Herein, we …

Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

Despite significant advancements in microstructural characterization methods, the
interconnections between nanostructure and morphological diversity of calcium-silicate …

Image-based simulation, the use of 3D images to calculate physical quantities, relies on
image segmentation for geometry creation. However, this process introduces image …

The recent surge in the adoption of machine learning techniques for materials design,
discovery, and characterization has resulted in increased interest in and application of …

Image segmentation has been an active topic of research for many years. Edges
characterize boundaries, and therefore, detection of edges is a problem of fundamental …

Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of
polycrystalline materials. Hence, their structures have been investigated in great detail …

The ability to quickly analyze large imaging datasets is vital to the widespread adoption of
modern materials characterization tools, and thus the development of new materials. Image …