  DeePMD-kit is a powerful open-source software package that facilitates
molecular dynamics simulations using machine learning potentials (MLP) known as
Deep Potential (DP) models. This package, which was released in 2017, has been
widely used in the fields of physics, chemistry, biology, and material science
for studying atomistic systems. The current version of DeePMD-kit offers
numerous advanced features such as DeepPot-SE, attention-based and hybrid
descriptors, the ability to fit tensile properties, type embedding, model
deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range
(DPLR), GPU support for customized operators, model compression, non-von
Neumann molecular dynamics (NVNMD), and improved usability, including
documentation, compiled binary packages, graphical user interfaces (GUI), and
application programming interfaces (API). This article presents an overview of
the current major version of the DeePMD-kit package, highlighting its features
and technical details. Additionally, the article benchmarks the accuracy and
efficiency of different models and discusses ongoing developments.
