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 …

Quantum computers can in principle solve certain problems exponentially more quickly than
their classical counterparts. We have not yet reached the advent of useful quantum …

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 …

Catalyst discovery is increasingly relying on computational chemistry, and many of the
computational tools are currently being automated. The state of this automation and the …

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 …

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 …

Force fields (FFs) form the basis of molecular simulations and have significant implications
in diverse fields such as materials science, chemistry, physics, and biology. A suitable FF is …

Neural network (NN) potential energy surfaces (PESs) have been widely used in atomistic
simulations with ab initio accuracy. While constructing NN PESs, their training data points …