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 …

Various eutectic systems have been proposed and studied over the past few decades. Most
of the studies have focused on three typical types of eutectics: eutectic metals, eutectic salts …

Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry,
but accuracies for many molecules are limited to 2-3 kcal⋅ mol− 1 with presently-available …

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 …

In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …

Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …

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

Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …