LAMMPS pair style for NequIP

This pair style allows you to use NequIP models in LAMMPS simulations.

Note: MPI is not supported due to the message-passing nature of the network.

Pre-requisites

• PyTorch or LibTorch >= 1.10.0

Usage in LAMMPS

pair_style	nequip
pair_coeff	* * deployed.pth <type name 1> <type name 2> ...

where deployed.pth is the filename of your trained model.

The names after the model path deployed.pth indicate, in order, the names of the NequIP model's atom types to use for LAMMPS atom types 1, 2, and so on. The number of names given must be equal to the number of atom types in the LAMMPS configuration (not the NequIP model!). The given names must be consistent with the names specified in the NequIP training YAML in chemical_symbol_to_type or type_names.

Building LAMMPS with this pair style

Download LAMMPS

git clone -b stable_29Sep2021_update2 --depth 1 git@github.com:lammps/lammps

or your preferred method. (--depth 1 prevents the entire history of the LAMMPS repository from being downloaded.)

Download this repository

git clone git@github.com:mir-group/pair_nequip

Patch LAMMPS

Automatically

From the pair_nequip directory, run:

./patch_lammps.sh /path/to/lammps/

Manually

First copy the source files of the pair style:

cp /path/to/pair_nequip/*.cpp /path/to/lammps/src/
cp /path/to/pair_nequip/*.h /path/to/lammps/src/

Then make the following modifications to lammps/cmake/CMakeLists.txt:

• Change set(CMAKE_CXX_STANDARD 11) to set(CMAKE_CXX_STANDARD 14)
• Append the following lines:

find_package(Torch REQUIRED)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
target_link_libraries(lammps PUBLIC "${TORCH_LIBRARIES}")

Configure LAMMPS

If you have PyTorch installed:

cd lammps
mkdir build
cd build
cmake ../cmake -DCMAKE_PREFIX_PATH=`python -c 'import torch;print(torch.utils.cmake_prefix_path)'`

If you don't have PyTorch installed, you need to download LibTorch from the PyTorch download page. Unzip the downloaded file, then configure LAMMPS:

cd lammps
mkdir build
cd build
cmake ../cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch

CMake will look for MKL and, optionally, CUDA and cuDNN. You may have to explicitly provide the path for your CUDA installation (e.g. -DCUDA_TOOLKIT_ROOT_DIR=/usr/lib/cuda/) and your MKL installation (e.g. -DMKL_INCLUDE_DIR=/usr/include/).

Pay attention to warnings and error messages.

MKL: If MKL_INCLUDE_DIR is not found and you are using a Python environment, a simple solution is to run conda install mkl-include or pip install mkl-include and append:

-DMKL_INCLUDE_DIR="$CONDA_PREFIX/include"

to the cmake command if using a conda environment, or

-DMKL_INCLUDE_DIR=`python -c "import sysconfig;from pathlib import Path;print(Path(sysconfig.get_paths()[\"include\"]).parent)"`

if using plain Python and pip.

CUDA: Note that the CUDA that comes with PyTorch when installed with conda (the cudatoolkit package) is usually insufficient (see here, for example) and you may have to install full CUDA seperately. A minor version mismatch between the available full CUDA version and the version of cudatoolkit is usually not a problem, as long as the system CUDA is equal or newer. (For example, PyTorch's requested cudatoolkit==11.3 with a system CUDA of 11.4 works, but a system CUDA 11.1 will likely fail.)

Build LAMMPS

make -j$(nproc)

This gives lammps/build/lmp, which can be run as usual with /path/to/lmp -in in.script. If you specify -DCMAKE_INSTALL_PREFIX=/somewhere/in/$PATH (the default is $HOME/.local), you can do make install and just run lmp -in in.script.