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

Abinit is a material-and nanostructure-oriented package that implements density-functional
theory (DFT) and many-body perturbation theory (MBPT) to find, from first principles …

Developing algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …

Random body-centered-cubic (BCC)“High Entropy” alloys are a new class of alloys, some
having high strength and good ductility at room temperature and some having exceptional …

Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for
studying molecular mechanisms in the condensed phase, however, they are too expensive …

Hydrogen embrittlement jeopardizes the use of high-strength steels in critical load-bearing
applications. However, uncertainty regarding how hydrogen affects dislocation motion …

Interatomic potential models based on machine learning (ML) are rapidly developing as
tools for material simulations. However, because of their flexibility, they require large fitting …

Lithium ortho-thiophosphate (Li3PS4) has emerged as a promising candidate for solid-state
electrolyte batteries, thanks to its highly conductive phases, cheap components, and large …

We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy
configurations over a wide range of compositions. We compare two different approaches …