Molecular dynamics

LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

MXene molecular sieving membranes for highly efficient gas separation

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Water Determines the Structure and Dynamics of Proteins

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Machine learning molecular dynamics for the simulation of infrared spectra

MOFSimBench: evaluating universal machine learning interatomic potentials in metal-organic framework molecular modeling

From density functional theory to machine learning: Emerging paradigms in energy materials discovery

Multiscale modeling of graphene and carbon nanostructures: advances in atomistic, coarse-grained, and machine learning approaches

CeNEP: A general-purpose machine learning interatomic potential for cementitious materials

Machine learning interatomic potential for the structural properties of iron oxides

Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS ₂