LAMMPS: ML-IAP#

Caution

Currently the parallel implementation of LAMMPS/ML-IAP is not tested.

Requirements#

  • cython == 3.0.11

  • cupy-cuda12x

  • flashTP (optional, follow here)

  • cuEquivariance (optional, follow here)

Install via:

pip install sevenn[mliap]

Build#

Get LAMMPS source code:

git clone https://github.com/lammps/lammps lammps-mliap
cd lammps-mliap
git checkout ccca772

Note

We found that some of the latest versions of LAMMPS produce inconsistent energies. Therefore, we highly recommend using this specific commit. This restriction will be relaxed once consistency checks are completed.

Configure the LAMMPS build with the necessary options:

cd ./lammps-mliap
cmake \
    -B build \
    -D CMAKE_BUILD_TYPE=Release \
    -D CMAKE_CXX_COMPILER=$(pwd)/lib/kokkos/bin/nvcc_wrapper \
    -D PKG_KOKKOS=ON \
    -D Kokkos_ENABLE_CUDA=ON \
    -D BUILD_MPI=ON \
    -D PKG_ML-IAP=ON \
    -D PKG_ML-SNAP=ON \
    -D MLIAP_ENABLE_PYTHON=ON \
    -D PKG_PYTHON=ON \
    -D BUILD_SHARED_LIBS=ON \
    cmake

Build LAMMPS and install the Python bindings:

cmake --build build -j 8
cd build
make install-python

Note

If the compilation fails, consider specifying the GPU architecture of the node you are building on in KOKKOS. For example, add the following flag to your cmake command: -D KOKKOS_ARCH_AMPERE86=ON when you are using GPU with Ampere 86 architecture like RTX A5000.

(Optional) Build with GPU-D3 pair style#

To build LAMMPS with the GPU-accelerated Grimme’s D3 method (see CUDA-accelerated Grimme’s D3), follow the steps below:

  1. Clone the SevenNet source repository.

git clone https://github.com/MDIL-SNU/SevenNet.git

  1. Copy the pair_d3-related scripts into lammps/src.

cp {sevenn_src_path}/sevenn/pair_e3gnn/{pair_d3.cu,pair_d3.h,pair_d3_pars.h} {lammps_src_path}/src/

  1. Modify {lammps_src_path}/cmake/CMakeLists.txt. Lines starting with - indicate the original content, and lines starting with + indicate the modified or added content.

# Declare CUDA at the header. This part depends to LAMMPS version.
 project(lammps
         DESCRIPTION "The LAMMPS Molecular Dynamics Simulator"
         HOMEPAGE_URL "https://www.lammps.org"
-        LANGUAGES CXX C)
+        LANGUAGES CXX C CUDA)

...

# Compile pair_d3.cu
-file(GLOB ALL_SOURCES CONFIGURE_DEPENDS ${LAMMPS_SOURCE_DIR}/[^.]*.cpp)
+file(GLOB ALL_SOURCES CONFIGURE_DEPENDS ${LAMMPS_SOURCE_DIR}/[^.]*.cpp  ${LAMMPS_SOURCE_DIR}/[^.]*.cu)

...

# At the end of the CMakeLists.txt, add cuda-related flags
+find_package(CUDA)
+set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -fmad=false -O3 --expt-relaxed-constexpr")
+target_link_libraries(lammps PUBLIC ${CUDA_LIBRARIES} cuda)

  1. Add the target CUDA architecture in the cmake configuration. An example configuration is shown below.

cmake \
    -B build \
    -D CMAKE_BUILD_TYPE=Release \
    -D CMAKE_CXX_COMPILER=$(pwd)/lib/kokkos/bin/nvcc_wrapper \
    -D PKG_KOKKOS=ON \
    -D Kokkos_ENABLE_CUDA=ON \
    -D Kokkos_ARCH_AMPERE89=ON \  # for ML-IAP Kokkos implementation
    -D BUILD_MPI=ON \
    -D PKG_ML-IAP=ON \
    -D PKG_ML-SNAP=ON \
    -D MLIAP_ENABLE_PYTHON=ON \
    -D PKG_PYTHON=ON \
    -D BUILD_SHARED_LIBS=ON \
    -D CMAKE_CUDA_ARCHITECTURES="89" \  # for CUDA D3 pair style
    cmake

  1. Build LAMMPS and install the Python bindings as above.

Usage#

Potential deployment#

An ML-IAP potential checkpoint can be deployed using sevenn get_model command with --use_mliap flag.

  • By default, output file name will be deployed_serial_mliap.pt. (You can customize the output file name using --output_prefix flag.)

  • You can accelerate the inference with --enable_cueq or --enable_flashTP flag:

sevenn get_model \
    {pretrained_name or checkpoint_path} \
    --use_mliap \
    --modal {task_name}  # Required when using multi-fidelity model

Single-GPU MD#

Below is an example snippet of a LAMMPS script for using SevenNet models with ML-IAP:

    # ---------- Initialization ----------
    units           metal
    boundary        p p p
    atom_style      atomic
    atom_modify     map yes
    newton          on

    read_data       {your_system_file_path}

    # ----- ML-IAP potential settings ----
    pair_style      mliap unified {your_potential_file_path.pt} 0
    pair_coeff      * * {space separated chemical species}

Note

If the LAMMPS is built with GPU-D3 pair style, you can combine SevenNet with D3 through the pair/hybrid command as the example below. For detailed instruction about parameters, supporting functionals and damping types, refer to CUDA-accelerated Grimme’s D3.

pair_style hybrid/overlay mliap unified {your_potential_file_path.pt} 0 d3 9000 1600 damp_bj pbe
pair_coeff      * * mliap {space separated chemical species}
pair_coeff      * * d3    {space separated chemical species}

Example command to run a LAMMPS simulation with ML-IAP:

/path/to/lammps-mliap/build/lmp -in lammps.in -k on g 1 -sf kk -pk kokkos newton on neigh half