DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning-based models 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.

Input Section

Inference in CP2K is performed through the DEEPMD section. As an example, the relevant section for DeePMD-kit is:


where the W.pb refers to the DeePMD model that was deployed using DeePMD-kit. An example for the full input file can be found and on the regtests, see DeePMD_W.inp

Input details

The tag ATOMS expects a list of elements/kinds and ATOMS_DEEPMD_TYPE expects a list of their index that is consistent with the type_map in DeePMD-kit parameters. If this is not done unphysical results will be obtained. Spotting such issues is quite straightforward as the energy is significantly wrong.

Compiling CP2K with Libdeepmd_c

Running with DeePMD-kit requires compiling CP2K with the libdeepmd_c library. For the CP2K binaries, please install the toolchain using the flag --with-deepmd, which would download libdeepmd_c from DeePMD-kit Github release and compile. GPU support is enabled when CUDA envrionment exists.

Further Resources

For additional references on Deep Potential and DeePMD-kit see: