To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …

The use of biomedical metallic materials in research and clinical applications has been an
important focus and a significant area of interest, primarily owing to their role in enhancing …

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 …

Deep neural network (DNN) potentials have recently gained popularity in computer
simulations of a wide range of molecular systems, from liquids to materials. In this study, we …

Interphase boundaries are essential in the deformation and phase transformations in
titanium (Ti) alloys. While static structures of semicoherent α/β interfaces in various Ti alloys …

Ti exhibits complex plastic deformation controlled by active dislocation and twinning
systems. Understandings on dislocation cores and twin interfaces are currently not complete …

To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …

BCC transition metals (TMs) exhibit complex temperature and strain-rate dependent plastic
deformation behavior controlled by individual crystal lattice defects. Classical empirical and …

Machine learning potentials (MLPs) capable of accurately describing complex ab initio
potential energy surfaces (PESs) have revolutionized the field of multiscale atomistic …

Abstract Machine Learning (ML)-based force fields are attracting ever-increasing interest
due to their capacity to span spatiotemporal scales of classical interatomic potentials at …