The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

Manipulating the crystallographic orientation of semiconductor crystals plays a vital role in
fine-tuning their facet-dependent properties, such as surface properties, charge transfer …

Computational methods that automatically extract knowledge from data are critical for
enabling data-driven materials science. A reliable identification of lattice symmetry is a …

An equiatomic CoCrNi medium entropy alloy (MEA) with face-centered cubic (FCC) structure
exhibits excellent combination of strength and ductility. Here we employ interstitial do** to …

The formation of adcumulates necessitates the continued growth of primocrysts down to low
porosities. Gravitationally driven viscous compaction at the base of a crystal mushy layer on …

The development of high-fidelity mechanical property prediction models for the design of
polycrystalline materials relies on large volumes of microstructural feature data …

In this work we combine experiments and simulations to study the residual deformation state
near twins in titanium at different stages of the complete twin growth process, including the …

Fatigue failure processes in metallic materials are closely related to the evolution of strain
localisation under cyclic loading. Characterisation of this strain localisation is important in …

Determining the local orientation of crystals in engineering and geological materials has
become routine with the advent of modern crystallographic map** techniques. These …

The mechanical properties of eutectic high entropy alloys (EHEAs) can be significantly
improved by adjusting its microstructure; however, the conventional adjustment approaches …