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

In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

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 …

Using the Deep Potential methodology, we construct a model that reproduces accurately the
potential energy surface of the SCAN approximation of density functional theory for water …

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 …

Classical empirical force fields have dominated biomolecular simulation for over 50 years.
Although widely used in drug discovery, crystal structure prediction, and biomolecular …

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 …

We introduce a scheme based on machine learning and deep neural networks to model the
environmental dependence of the electronic polarizability in insulating materials. Application …

It is well known in the field of machine learning that committee models improve accuracy,
provide generalization error estimates, and enable active learning strategies. In this work …

Fast and accurate simulation of complex chemical systems in environments such as
solutions is a long standing challenge in theoretical chemistry. In recent years, machine …