Supercapacitors offer superior energy storage capabilities than traditional capacitors,
making them useful for applications such as electric vehicles and rapid large‐scale energy …

High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Novel functional materials enable fundamental breakthroughs across technological
applications from clean energy to information processing,,,,,,,,,–. From microchips to batteries …

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 …

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 …

The design of functional materials with desired properties is essential in driving
technological advances in areas like energy storage, catalysis, and carbon capture …

Molecular dynamics (MD) simulation techniques are widely used for various natural science
applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab …

Generating the periodic structure of stable materials is a long-standing challenge for the
material design community. This task is difficult because stable materials only exist in a low …

The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …

The generation of plausible crystal structures is often the first step in predicting the structure
and properties of a material from its chemical composition. However, most current methods …