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The notation of se_e2_a is short for the Deep Potential Smooth Edition (DeepPot-SE) constructed from all information (both angular and radial) of atomic configurations. The e2 stands for the embedding with two-atoms information. This descriptor was described in detail in the DeepPot-SE paper. Note that it is sometimes called a “two-atom embedding descriptor” which means the input of the embedding net is atomic distances. The descriptor does encode multi-body information (both angular and radial information of neighboring atoms). In this example, we will train a DeepPot-SE model for a water system. A complete training input script of this example can be found in the directory.
apt install git
git clone --recursive -b v2.2.2 https://github.com/deepmodeling/deepmd-kit.git deepmd-kit
pip3 install numpy==1.22.3
pip3 install deepmd-kit[gpu,cu10,lmp,ipi]==2.2.2
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pip3 install tensorflow-2.6.5+corex.3.1.0-cp38-cp38-linux_x86_64.whl
sed -i '473s/np.float64/np.float32/' deepmd/env.py
pip3 install .
deepmd_source_dir=`pwd`
cd $deepmd_source_dir/source
mkdir build && cd build
apt install cmake
cmake -DUSE_TF_PYTHON_LIBS=TRUE -DCMAKE_INSTALL_PREFIX=$deepmd_root ..
make -j4
make install
cmake -DDEEPMD_C_ROOT=./libdeepmd_c -DCMAKE_INSTALL_PREFIX=$deepmd_root ..
make -j8
make install
cd $deepmd_source_dir
wget https://github.com/lammps/lammps/archive/stable_23Jun2022_update4.tar.gz
tar xf stable_23Jun2022_update4.tar.gz
mkdir -p lammps-stable_23Jun2022_update4/build/
cd lammps-stable_23Jun2022_update4/build/
apt update
apt install openmpi-bin
apt install libopenmpi-dev
cmake -D PKG_PLUGIN=ON -D PKG_KSPACE=ON -D LAMMPS_INSTALL_RPATH=ON -D BUILD_SHARED_LIBS=yes -D CMAKE_INSTALL_PREFIX=${deepmd_root} -D CMAKE_INSTALL_LIBDIR=lib -D CMAKE_INSTALL_FULL_LIBDIR=${deepmd_root}/lib ../cmake
make -j4
make install
cd ../..
pip3 install -U i-PI
pip3 install pytest
cd $deepmd_source_dir/examples/water/se_e2_a/
export CUDA_VISIBLE_DEVICES=0
export TF_ENABLE_DEPRECATION_WARNINGS=1
DP_INTERFACE_PREC=low dp train input.json
| GPU | average training |
|---|---|
| 1 card | 0.0325 s/batch |
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