Disordered multicomponent systems, occupying the mostly uncharted centres of phase
diagrams, were proposed in 2004 as innovative materials with promising applications. The …

In material science, traditional experimental and computational approaches require
investing enormous time and resources, and the experimental conditions limit the …

The need for improved functionalities in extreme environments is fuelling interest in high-
entropy ceramics,–. Except for the computational discovery of high-entropy carbides …

High-entropy materials have attracted considerable interest due to the combination of useful
properties and promising applications. Predicting their formation remains the major …

Nanostructured semiconductors emit light from electronic states known as excitons. For
organic materials, Hund's rules state that the lowest-energy exciton is a poorly emitting triplet …

Superconductivity has been the focus of enormous research effort since its discovery more
than a century ago. Yet, some features of this unique phenomenon remain poorly …

Over the past several decades, electron and scanning probe microscopes have become
critical components of condensed matter physics, materials science and chemistry research …

We propose an approach to materials prediction that uses a machine-learning interatomic
potential to approximate quantum-mechanical energies and an active learning algorithm for …

Abstract Machine learning models of material properties accelerate materials discovery,
reproducing density functional theory calculated results at a fraction of the cost,,,,–. To bridge …

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