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

Kohn–Sham Density Functional Theory (KSDFT) is the most widely used electronic structure
method in chemistry, physics, and materials science, with thousands of calculations cited …

The ideal electrolyte for the widely used LiNi0. 8Mn0. 1Co0. 1O2 (NMC811)|| graphite
lithium-ion batteries is expected to have the capability of supporting higher voltages (≥ 4.5 …

Large-scale simulations with complex electron interactions remain one of the greatest
challenges for atomistic modelling. Although classical force fields often fail to describe the …

Establishing appropriate metal-support interactions is imperative for acquiring efficient and
corrosion-resistant catalysts for water splitting. Herein, the interaction mechanism between …

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 …

Molecular dynamics (MD) simulations allow insights into complex processes, but accurate
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …

Inorganic-rich solid-electrolyte interphases (SEIs) on Li metal anodes improve the
electrochemical performance of Li metal batteries (LMBs). Therefore, a fundamental …

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 …

The carbon dioxide and carbon monoxide electroreduction reactions, when powered using
low-carbon electricity, offer pathways to the decarbonization of chemical manufacture …