One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

DScribe is a software package for machine learning that provides popular feature
transformations (“descriptors”) for atomistic materials simulations. DScribe accelerates the …

The atomistic structures of solid–solid interfaces are of fundamental interests for
understanding physical properties of interfacial materials. However, determination of …

The representations of a compound, called “descriptors” or “features”, play an essential role
in constructing a machine-learning model of its physical properties. In this study, we adopt a …

Discovering new materials with excellent performance is a hot issue in the materials
genome initiative. Traditional experiments and calculations often waste large amounts of …

Graph neural networks for crystal structures typically use the atomic positions and the atomic
species as input. Unfortunately, this information is not available when predicting new …

Significant developments in the microstructural characterization of superplasticity have been
achieved in the 2010s, which can be attributed to advanced electron microscopy and well …

Materials development often confronts a dilemma as it needs to satisfy multifunctional, often
conflicting, demands. For example, thermoelectric conversion requires high electrical …

Grain boundaries (GBs) in perovskite solar cells and optoelectronic devices are widely
regarded as detrimental defects that accelerate charge and energy losses through …