We propose a crystal structure prediction method based on a novel structure generation
algorithm and on-lattice machine-learning interatomic potentials. Our algorithm generates …

Lomer (L) and Lomer-Cottrell (LC) dislocations have long been considered to be central to
work hardening in face-centered cubic (FCC) metals and alloys. These dislocations act as …

Grain boundary structural transitions can lead to significant changes in the properties and
performance of materials. In multi-principal element alloys, understanding these transitions …

The Peierls barrier represents the inherent lattice resistance to dislocation glide, controlling
dislocation movement and dictating the resulting mechanical properties. The rise of multi …

Chemical short-range order (SRO) affects the distribution of elements throughout the solid-
solution phase of metallic alloys, thereby modifying the background against which …

Complex concentrated alloys (CCAs) have drawn immense attention from the materials
research community and beyond. Because the vast compositional and structural degrees of …

Ring polymer molecular dynamics (RPMD) has proven to be an accurate approach for
calculating thermal rate coefficients of various chemical reactions. For wider application of …

Machine learning potential is an emerging and powerful approach with which to address the
challenges of achieving both accuracy and efficiency in molecular dynamics simulations …

Multi-principal element, high entropy alloys (HEAs) are an emerging class of materials that
have found applications across the board. Owing to the multitude of possible candidate …

A nanocrystalline metal's strength increases significantly as its grain size decreases, a
phenomenon known as the Hall-Petch relation. Such relation, however, breaks down when …