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 …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

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 …

Theoretical studies on the MgCl2–KCl eutectic heavily rely on ab initio calculations based
on density functional theory (DFT). However, neither large-scale nor long-time calculations …

Materials composed of Ag, Au, and Ag–Au alloys remain of great interest despite decades of
intense research scrutiny. We interpret these efforts as an impetus for developing robust …

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 …

Determining the atomic structure of clusters has been a long-term challenge in theoretical
calculations due to the high computational cost of density-functional theory (DFT). Deep …

Machine learning potentials (MLPs) are promising for various chemical systems, but their
complexity and lack of physical interpretability challenge their broad applicability. This study …

Predicting crystal structure information is a challenging problem in materials science that
clearly benefits from artificial intelligence approaches. The leading strategies in machine …

Over the last few decades, molecular dynamics (MD) simulations have attracted a great deal
of attention due to their wide range of applications in many fields such as condensed matter …