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

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 …

The design of heterogeneous catalysts is necessarily surface‐focused, generally achieved
via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure …

As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …

Rational design of compounds with specific properties requires understanding and fast
evaluation of molecular properties throughout chemical compound space—the huge set of …

Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …

Atomic‐scale modeling and understanding of materials have made remarkable progress,
but they are still fundamentally limited by the large computational cost of explicit electronic …

We present an accurate machine learning (ML) model for atomistic simulations of carbon,
constructed using the Gaussian approximation potential (GAP) methodology. The potential …

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and
interfaces bestow them with various exceptional properties. These properties, however, also …

Conspectus The visualization of data is indispensable in scientific research, from the early
stages when human insight forms to the final step of communicating results. In …