We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

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 essential demand for functional materials enabling the realization of new energy
technologies has triggered tremendous efforts in scientific and industrial research in recent …

Refractory high-entropy alloys (RHEAs) are designed for high elevated-temperature
strength, with both edge and screw dislocations playing an important role for plastic …

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 …

Materials machine learning (ML) is revolutionizing various areas in a fast speed, aiming to
efficiently design novel materials with superior performance. Here we reviewed the recent …

With the development of modern society, the requirement for energy has become
increasingly important on a global scale. Therefore, the exploration of novel materials for …

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

Refractory multi-principal element alloys (MPEAs) have exceptional mechanical properties,
including high strength-to-weight ratio and fracture toughness, at high temperatures. Here …