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

Artificial intelligence and specifically machine learning applications are nowadays used in a
variety of scientific applications and cutting-edge technologies, where they have a …

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 …

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

The reliability of organic molecular crystal structure prediction has improved tremendously in
recent years. Crystal structure predictions for small, mostly rigid molecules are quickly …

Abstract Machine learning (ML) algorithms are currently emerging as powerful tools in all
areas of science. Conventionally, ML is understood as a fundamentally data‐driven …

The adsorption energies of molecular adsorbates on catalyst surfaces are key descriptors in
computational catalysis research. For the relatively large reaction intermediates frequently …

Co-crystals are a highly interesting material class as varying their components and
stoichiometry in principle allows tuning supramolecular assemblies toward desired physical …

Calculating sublimation enthalpies of molecular crystal polymorphs is relevant to a wide
range of technological applications. However, predicting these quantities at first-principles …

Tight-binding approaches, especially the Density Functional Tight-Binding (DFTB) and the
extended tight-binding schemes, allow for efficient quantum mechanical simulations of large …