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

High-entropy ceramics with five or more cations have recently attracted significant attention
due to their superior properties for various structural and functional applications. Although …

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

With the development of advanced electronic devices and electric power systems, polymer-
based dielectric film capacitors with high energy storage capability have become particularly …

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 …

High-temperature oxidation can precipitate chemical and mechanical degradations in
materials, potentially leading to catastrophic failures. Thus, understanding the mechanisms …

Graph networks are a new machine learning (ML) paradigm that supports both relational
reasoning and combinatorial generalization. Here, we develop universal MatErials Graph …

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

Nanotechnology has revealed the science of the nanoscale. The global challenge that will
follow is to build functional materials with the knowledge of nanoscale phenomena. This task …

Data‐driven science is heralded as a new paradigm in materials science. In this field, data is
the new resource, and knowledge is extracted from materials datasets that are too big or …