Rechargeable batteries have become indispensable implements in our daily life and are
considered a promising technology to construct sustainable energy systems in the future …

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

Creating fast and accurate force fields is a long-standing challenge in computational
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …

A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …

Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a
fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow …

Since the classical molecular dynamics simulator LAMMPS was released as an open source
code in 2004, it has become a widely-used tool for particle-based modeling of materials at …

Heterogeneous catalysis underpins a wide variety of industrial processes including energy
conversion, chemical manufacturing and environmental remediation. Significant advances …

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …

Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …