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 first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

Abstract High-entropy (HE) ceramics, by analogy with HE metallic alloys, are an emerging
class of solid solutions composed of a large number of species. These materials offer the …

Adopting low-index single-crystal surfaces as models for metal nanoparticle catalysts has
been questioned by the experimental findings of adsorbate-induced formation of …

The accuracy and efficiency of electronic-structure methods to understand, predict and
design the properties of materials has driven a new paradigm in research. Simulations can …

Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

The atomic cluster expansion is developed as a complete descriptor of the local atomic
environment, including multicomponent materials, and its relation to a number of other …

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …

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