Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

The subject of this paper is the technology (the'how') of constructing machine-learning
interatomic potentials, rather than science (the'what'and'why') of atomistic simulations using …

The use of supervised machine learning to develop fast and accurate interatomic potential
models is transforming molecular and materials research by greatly accelerating atomic …

On the one hand, Irradiation is everywhere and irradiation effect, especially the material
irradiation damage effect under strong irradiation field, is one of the key factors for the …

The combination of multiple-principal element materials, known as high-entropy materials
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …

Multi-component alloys have demonstrated excellent performance in various applications,
but the vast range of possible compositions and microstructures makes it challenging to …

High entropy alloys (HEAs) are an important material class in the development of next-
generation structural materials, but the astronomically large composition space cannot be …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

With the development of artificial intelligence, machine learning has a wide range of
applications in the field of materials. The sparsity of data on the mechanical properties of …