DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems.
- interfaced with TensorFlow, one of the most popular deep learning frameworks, making the training process highly automatic and efficient.
- interfaced with high-performance classical MD and quantum (path-integral) MD packages, i.e., LAMMPS and i-PI, respectively.
- implements the Deep Potential series models, which have been successfully applied to finite and extended systems including organic molecules, metals, semiconductors, and insulators, etc.
- implements MPI and GPU supports, makes it highly efficient for high performance parallel and distributed computing.
- highly modularized, easy to adapt to different descriptors for deep learning based potential energy models.