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

Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

Structurally disordered materials pose fundamental questions,,–, including how different
disordered phases ('polyamorphs') can coexist and transform from one phase to another …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

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 …

Understanding the direct transformation from graphite to diamond has been a long-standing
challenge with great scientific and practical importance. Previously proposed transformation …

To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …

Nowadays, computer simulations have become a standard tool in essentially all fields of
chemistry, condensed matter physics, and materials science. In order to keep up with state …

Modern simulation techniques have reached a level of maturity which allows a wide range of
problems in chemistry and materials science to be addressed. Unfortunately, the application …

The ability to change material properties through phase engineering has long been sought,
with the goal of ad hoc tunability of the physical and chemical properties of the transformed …