Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

Progress in the discovery of new materials has been accelerated by the development of
reliable quantum-mechanical approaches to crystal structure prediction. The properties of a …

Abstract The Materials Genome Initiative (MGI) advanced a new paradigm for materials
discovery and design, namely that the pace of new materials deployment could be …

Thanks to the development of experimental high-pressure techniques and methods for
crystal-structure prediction based on quantum mechanics, in the past decade, numerous …

Pressure is a fundamental thermodynamic variable that can be used to control the properties
of materials, because it reduces interatomic distances and profoundly modifies electronic …

In the search for new functional materials, quantum mechanics is an exciting starting point.
The fundamental laws that govern the behaviour of electrons have the possibility, at the …

High-throughput computational materials design is an emerging area of materials science.
By combining advanced thermodynamic and electronic-structure methods with intelligent …

Crystal structure predictions based on first-principles calculations have gained great
success in materials science and solid state physics. However, the remaining challenges …

The use of high-throughput density functional theory (DFT) calculations to screen for new
materials and conduct fundamental research presents an exciting opportunity for materials …

Predictions of observable properties by density-functional theory calculations (DFT) are
used increasingly often by experimental condensed-matter physicists and materials …