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 …

The electronic charge density plays a central role in determining the behavior of matter at
the atomic scale, but its computational evaluation requires demanding electronic-structure …

Point clouds are versatile representations of 3D objects and have found widespread
application in science and engineering. Many successful deep-learning models have been …

Statistical learning methods show great promise in providing an accurate prediction of
materials and molecular properties, while minimizing the need for computationally …

The molecular dipole polarizability describes the tendency of a molecule to change its
dipole moment in response to an applied electric field. This quantity governs key intra-and …

The impact of the vibrational coupling of the OH stretch mode on the spectra differs
significantly between IR and Raman spectra of water. Unified understanding of the …

Machine learning has revolutionized the high-dimensional representations for molecular
properties such as potential energy. However, there are scarce machine learning models …

Vibrational properties of molecular crystals are constantly used as structural fingerprints, in
order to identify both the chemical nature and the structural arrangement of molecules. The …

Collective modes and dynamics of dense disordered materials such as water are not well
understood, but nonlinear optics provides a sensitive probe to study polar modes in these …