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

Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

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 review progress in neural network (NN)-based methods for the construction of
interatomic potentials from discrete samples (such as ab initio energies) for applications in …

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 …

We propose a simple, but efficient and accurate, machine learning (ML) model for
develo** a high-dimensional potential energy surface. This so-called embedded atom …

In this Perspective, we review recent advances in constructing high-fidelity potential energy
surfaces (PESs) from discrete ab initio points, using machine learning tools. Such PESs …

For molecules with more than three atoms, it is difficult to fit or interpolate a potential energy
surface (PES) from a small number of (usually ab initio) energies at points. Many methods …

Artificial Neural Networks (NN) are already heavily involved in methods and applications for
frequent tasks in the field of computational chemistry such as representation of potential …

Over the past decade, about 50 potential energy surfaces (PESs) for polyatomics with 4–11
atoms and for clusters have been calculated using the permutationally invariant polynomial …