DeePMD-kit plugin for various graph neural network models

deepmd-gnn is a DeePMD-kit plugin for various graph neural network (GNN) models, which connects DeePMD-kit and atomistic GNN packages by enabling GNN models in DeePMD-kit.

Supported packages and models include:

MACE (PyTorch version)
NequIP (PyTorch version)

After installing the plugin, you can train the GNN models using DeePMD-kit, run active learning cycles for the GNN models using DP-GEN, perform simulations with the MACE model using molecular dynamic packages supported by DeePMD-kit, such as LAMMPS and AMBER. You can follow DeePMD-kit documentation to train the GNN models using its PyTorch backend, after using the specific model parameters.

Installation

First, clone this repository:

git clone https://github.com/njzjz/deepmd-gnn
cd deepmd-gnn

Python interface plugin

Python 3.9 or above is required. A C++ compiler that supports C++ 14 (for PyTorch 2.0) or C++ 17 (for PyTorch 2.1 or above) is required.

Assume you have installed DeePMD-kit (v3.0.0b2 or above) and PyTorch in an environment, then execute

# expose PyTorch CMake modules
export CMAKE_PREFIX_PATH=$(python -c "import torch;print(torch.utils.cmake_prefix_path)")

pip install .

C++ interface plugin

DeePMD-kit version should be v3.0.0b4 or later.

Follow DeePMD-kit documentation to install DeePMD-kit C++ interface with PyTorch backend support and other related MD packages. After that, you can build the plugin

# Assume libtorch has been contained in CMAKE_PREFIX_PATH
mkdir -p build
cd build
cmake .. -D CMAKE_INSTALL_PREFIX=/prefix/to/install
cmake --build . -j8
cmake --install .

libdeepmd_gnn.so will be installed into the directory you assign. When using any DeePMD-kit C++ interface, set the following environment variable in advance:

export DP_PLUGIN_PATH=/prefix/to/install/lib/libdeepmd_gnn.so

Usage

Follow Parameters section to prepare a DeePMD-kit input file.

dp --pt train input.json
dp --pt freeze

A frozen model file named frozen_model.pth will be generated. You can use it in the MD packages or other interfaces. For details, follow DeePMD-kit documentation.

Parameters

MACE

To use the MACE model, set "type": "mace" in the model section of the training script. Below is default values for the MACE model, most of which follows default values in the MACE package:

"model": {
  "type": "mace",
  "type_map": [
    "O",
    "H"
  ],
  "r_max": 5.0,
  "sel": "auto",
  "num_radial_basis": 8,
  "num_cutoff_basis": 5,
  "max_ell": 3,
  "interaction": "RealAgnosticResidualInteractionBlock",
  "num_interactions": 2,
  "hidden_irreps": "128x0e + 128x1o",
  "pair_repulsion": false,
  "distance_transform": "None",
  "correlation": 3,
  "gate": "silu",
  "MLP_irreps": "16x0e",
  "radial_type": "bessel",
  "radial_MLP": [64, 64, 64],
  "std": 1.0,
  "precision": "float32"
}

NequIP

"model": {
  "type": "nequip",
  "type_map": [
    "O",
    "H"
  ],
  "r_max": 5.0,
  "sel": "auto",
  "num_layers": 4,
  "l_max": 2,
  "num_features": 32,
  "nonlinearity_type": "gate",
  "parity": true,
  "num_basis": 8,
  "BesselBasis_trainable": true,
  "PolynomialCutoff_p": 6,
  "invariant_layers": 2,
  "invariant_neurons": 64,
  "use_sc": true,
  "irreps_edge_sh": "0e + 1e",
  "feature_irreps_hidden": "32x0o + 32x0e + 32x1o + 32x1e",
  "chemical_embedding_irreps_out": "32x0e",
  "conv_to_output_hidden_irreps_out": "16x0e",
  "precision": "float32"
}

DPRc support

In deepmd-gnn, the GNN model can be used in a DPRc way. Type maps that starts with m (such as mH) or OW or HW will be recognized as MM types. Two MM atoms will not build edges with each other. Such GNN+DPRc model can be directly used in AmberTools24.

deepmd-mace