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

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 …

We introduce a local machine-learning method for predicting the electron densities of
periodic systems. The framework is based on a numerical, atom-centered auxiliary basis …

The electron density of a molecule or material has recently received major attention as a
target quantity of machine-learning models. A natural choice to construct a model that yields …

In this work, we explore the quantum chemical foundations of descriptors for molecular
similarity. Such descriptors are key for traversing chemical compound space with machine …

The computational study of energy storage and conversion processes calls for simulation
techniques that can reproduce the electronic response of metal electrodes under electric …

Heisenberg exchange spin coupling between metal centers is essential for describing and
understanding the electronic structure of many molecular catalysts, metalloenzymes, and …

The on-top pair density [Π r] is a local quantum-chemical property that reflects the probability
of two electrons of any spin to occupy the same position in space. Being the simplest …

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

A current challenge in atomistic machine learning is that of efficiently predicting the
response of the electron density under electric fields. We address this challenge with …