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).
DeePMD-kit
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.
The NGC DeePMD-kit container builds upon the NGC TensorFlow container, adding DeePMD-kit with LAMMPS plugin support.
System Requirements
- Pascal(sm60), Volta(sm70), or Ampere(sm80) NVIDIA GPU(s)
- x86_64 CPU supporting avx2_256 instruction set
- CUDA driver version >= 510.39.01, -or- r418(>=40.04), r450(>=36.06), r460(>=27.04), r470(>=57.02)
Running with Docker
Start an interactive session in the container
docker run -it --rm --gpus all --shm-size=1g --ulimit memlock=-1 nvcr.io/hpc/deepmd-kit:2.1.1
Fetch the DeePMD-kit example data
git clone https://github.com/deepmodeling/deepmd-kit.git
Train the example
cd deepmd-kit/examples/water/se_e2_a
dp train input.json
Freeze the model
dp freeze -o frozen_model.pb
Run the LAMMPS example simulation
cd ../lmp
cp ../se_e2_a/frozen_model.pb .
mpirun --allow-run-as-root -n 1 lmp -k on g 1 -sf kk -pk kokkos cuda/aware on neigh full comm device -in in.plugin.lammps