A critical review of machine learning of energy materials
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 …
change landscapes for physics and chemistry. With its ability to solve complex tasks …
Structure prediction drives materials discovery
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 …
reliable quantum-mechanical approaches to crystal structure prediction. The properties of a …
New frontiers for the materials genome initiative
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 …
discovery and design, namely that the pace of new materials deployment could be …
Chemistry under high pressure
Thanks to the development of experimental high-pressure techniques and methods for
crystal-structure prediction based on quantum mechanics, in the past decade, numerous …
crystal-structure prediction based on quantum mechanics, in the past decade, numerous …
Materials discovery at high pressures
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 …
of materials, because it reduces interatomic distances and profoundly modifies electronic …
Computational predictions of energy materials using density functional theory
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 …
The fundamental laws that govern the behaviour of electrons have the possibility, at the …
The high-throughput highway to computational materials design
High-throughput computational materials design is an emerging area of materials science.
By combining advanced thermodynamic and electronic-structure methods with intelligent …
By combining advanced thermodynamic and electronic-structure methods with intelligent …
MAGUS: machine learning and graph theory assisted universal structure searcher
Crystal structure predictions based on first-principles calculations have gained great
success in materials science and solid state physics. However, the remaining challenges …
success in materials science and solid state physics. However, the remaining challenges …
A high-throughput infrastructure for density functional theory calculations
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 …
materials and conduct fundamental research presents an exciting opportunity for materials …
Error estimates for solid-state density-functional theory predictions: an overview by means of the ground-state elemental crystals
Predictions of observable properties by density-functional theory calculations (DFT) are
used increasingly often by experimental condensed-matter physicists and materials …
used increasingly often by experimental condensed-matter physicists and materials …