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

In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

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 …

ABSTRACT A computer algorithm to search the symmetries of crystal structures, as
implemented in the spglib code, is described. An iterative algorithm is employed to robustly …

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 …

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 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 …

Designing a stable solid–electrolyte interphase on a Li anode is imperative to develo**
reliable Li metal batteries. Herein, we report a suspension electrolyte design that modifies …