Interfacial dynamics within chemical systems such as electron and ion transport processes
have relevance in the rational optimization of electrochemical energy storage materials and …

Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

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

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well
known from computer science is broadly affecting many aspects of various fields including …

Density functional theory describes matter at the quantum level, but all popular
approximations suffer from systematic errors that arise from the violation of mathematical …

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

Ligand-protected noble metal nanoclusters, commonly termed 'monolayer-protected metal
clusters'(MPCs), comprise a common set of structures with an inorganic core stabilized by an …

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 …

This paper introduces a new model to learn graph neural networks equivariant to rotations,
translations, reflections and permutations called E (n)-Equivariant Graph Neural Networks …

Large-scale computational molecular models provide scientists a means to investigate the
effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship …