Density functional theory (DFT) plays a pivotal role in chemical and materials science
because of its relatively high predictive power, applicability, versatility, and computational …

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 electrocatalytic oxygen evolution reaction (OER) supplies the protons and electrons
needed to transform renewable electricity into chemicals and fuels. However, the OER is …

The unprecedented ability of computations to probe atomic-level details of catalytic systems
holds immense promise for the fundamentals-based bottom-up design of novel …

Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations
to account for a surrounding liquid electrolyte on the level of a continuous polarizable …

Physics-based electrochemical battery models derived from porous electrode theory are a
very powerful tool for understanding lithium-ion batteries, as well as for improving their …

To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …

Having served as a playground for fundamental studies on the physics of d and f electrons
for almost a century, magnetic molecules are now becoming increasingly important for …

The power of quantum chemistry to predict the ground and excited state properties of
complex chemical systems has driven the development of computational quantum chemistry …

The rapidly evolving Coronavirus 2019 (COVID-19) pandemic has led to millions of deaths
around the world, highlighting the pressing need to develop effective antiviral …