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 …

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 …

Symmetry considerations are at the core of the major frameworks used to provide an
effective mathematical representation of atomic configurations that is then used in machine …

Active space quantum chemical methods could provide very accurate description of strongly
correlated electronic systems, which is of tremendous value for natural sciences. The proper …

We introduce multiconfiguration data-driven functional methods (MC-DDFMs), a group of
methods which aim to correct the total or classical energy of a qualitatively accurate …

We have trained a new machine-learning (ML) model which predicts mutual information (MI)
for strongly correlated systems. This is a complex quantity, which is much more difficult to …

Machine learning (ML) algorithms have undergone an explosive development impacting
every aspect of computational chemistry. To obtain reliable predictions, one needs to …

The ab initio determination of electronic excited state (ES) properties is the cornerstone of
theoretical photochemistry. Yet, traditional ES methods become impractical when applied to …

The application of machine learning to theoretical chemistry has made it possible to
combine the accuracy of quantum chemical energetics with the thorough sampling of finite …