Installation
Requirements
To use DeepH-pack, following environments and packages are required:
Python packages
Prepare the Python 3.9 interpreter. Install the following Python packages required:
NumPy
SciPy
PyTorch = 1.9.1
PyTorch Geometric = 1.7.2
e3nn = 0.3.5
pymatgen
h5py
TensorBoard
pathos
psutil
In Linux, you can quickly achieve the requirements by running:
# install miniconda with python 3.9
wget https://repo.anaconda.com/miniconda/Miniconda3-py39_4.10.3-Linux-x86_64.sh
bash Miniconda3-py39_4.10.3-Linux-x86_64.sh
# install packages by conda
conda install numpy
conda install scipy
conda install pytorch==1.9.1 ${pytorch_config}
conda install pytorch-geometric=1.7.2 -c rusty1s -c conda-forge
conda install pymatgen -c conda-forge
# install packages by pip
pip install e3nn==0.3.5
pip install h5py
pip install tensorboard
pip install pathos
pip install psutil
with ${pytorch_config} replaced by your own configuration.
You can find how to set it in the official website of PyTorch.
Julia packages
Prepare the Julia 1.5.4 interpreter. Install the following Julia packages required with Julia’s builtin package manager:
Arpack.jl
HDF5.jl
ArgParse.jl
JLD.jl
JSON.jl
IterativeSolvers.jl
DelimitedFiles.jl
StaticArrays.jl
LinearMaps.jl
Pardiso.jl
In Linux, you can quickly achieve the requirements by first running:
# install julia 1.6.6
wget https://julialang-s3.julialang.org/bin/linux/x64/1.6/julia-1.6.6-linux-x86_64.tar.gz
tar xzvf julia-1.6.6-linux-x86_64.tar.gz
# open the julia REPL
julia
Then enter the pkg REPL by pressing ] from the Julia REPL. In the pkg REPL run:
(@v1.6) pkg> add Arpack
(@v1.6) pkg> add HDF5
(@v1.6) pkg> add ArgParse
(@v1.6) pkg> add JLD
(@v1.6) pkg> add JSON
(@v1.6) pkg> add IterativeSolvers
(@v1.6) pkg> add DelimitedFiles
(@v1.6) pkg> add StaticArrays
(@v1.6) pkg> add LinearMaps
Follow these instructions to install Pardiso.jl.
Install DeepH-pack
Run the following command in the path of DeepH-pack:
git clone https://github.com/mzjb/DeepH-pack.git
cd DeepH-pack
pip install .
Install one of the supported DFT packages
One of the supported DFT packages is required to obtain the dataset. DeepH-pack supports DFT results made by ABACUS, OpenMX, FHI-aims or SIESTA, and will support HONPAS soon.