The purpose of this code is to design new crystal structures of Bi-Se system with low formation energy. This code is related to the work by T. Long, et al. "Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures." npj Computational Materials 7.1 (2021): 1-7. Please feel free to contact the conresponding author Prof. Hongbin Zhang ([email protected]) or Teng Long ([email protected]) for discussions.
We use the following environment to run this code, it has been tested by our colleborators as well. Please make sure that you have the same environment as us before running this code, please check them carefully if you have unexpected errors.
Name Version Build Channel
_libgcc_mutex 0.1 main
_tflow_select 2.1.0 gpu
absl-py 0.10.0 py37_0
ase 3.20.1 py_0 conda-forge
astor 0.8.1 py37_0
blas 1.0 mkl
c-ares 1.16.1 h7b6447c_0
ca-certificates 2020.10.14 0
certifi 2020.11.8 py37h06a4308_0
click 7.1.2 pyh9f0ad1d_0 conda-forge
cudatoolkit 10.1.243 h6bb024c_0
cudnn 7.6.5 cuda10.1_0
cupti 10.1.168 0
cycler 0.10.0 py_2 conda-forge
flask 1.1.2 pyh9f0ad1d_0 conda-forge
freetype 2.10.4 h7ca028e_0 conda-forge
gast 0.4.0 py_0
google-pasta 0.2.0 py_0
grpcio 1.31.0 py37hf8bcb03_0
h5py 2.10.0 py37hd6299e0_1
hdf5 1.10.6 hb1b8bf9_0
importlib-metadata 2.0.0 py_1
intel-openmp 2020.2 254
itsdangerous 1.1.0 py_0 conda-forge
jinja2 2.11.2 pyh9f0ad1d_0 conda-forge
joblib 0.17.0 py_0
jpeg 9d h36c2ea0_0 conda-forge
keras-applications 1.0.8 py_1
keras-preprocessing 1.1.0 py_1
kiwisolver 1.3.1 py37hc928c03_0 conda-forge
lcms2 2.11 hcbb858e_1 conda-forge
ld_impl_linux-64 2.33.1 h53a641e_7
libedit 3.1.20191231 h14c3975_1
libffi 3.3 he6710b0_2
libgcc-ng 9.1.0 hdf63c60_0
libgfortran-ng 7.3.0 hdf63c60_0
libpng 1.6.37 h21135ba_2 conda-forge
libprotobuf 3.13.0.1 hd408876_0
libstdcxx-ng 9.1.0 hdf63c60_0
libtiff 4.1.0 h4f3a223_6 conda-forge
libwebp-base 1.1.0 h36c2ea0_3 conda-forge
lz4-c 1.9.2 he1b5a44_3 conda-forge
markdown 3.3.2 py37_0
markupsafe 1.1.1 py37hb5d75c8_2 conda-forge
matplotlib-base 3.3.3 py37h4f6019d_0 conda-forge
mkl 2020.2 256
mkl-service 2.3.0 py37he904b0f_0
mkl_fft 1.2.0 py37h23d657b_0
mkl_random 1.1.1 py37h0573a6f_0
ncurses 6.2 he6710b0_1
numpy 1.19.1 py37hbc911f0_0
numpy-base 1.19.1 py37hfa32c7d_0
olefile 0.46 pyh9f0ad1d_1 conda-forge
openssl 1.1.1h h7b6447c_0
pillow 8.0.1 py37h63a5d19_0 conda-forge
pip 20.2.4 py37_0
protobuf 3.13.0.1 py37he6710b0_1
pyparsing 2.4.7 pyh9f0ad1d_0 conda-forge
python 3.7.9 h7579374_0
python-dateutil 2.8.1 py_0 conda-forge
python_abi 3.7 1_cp37m conda-forge
readline 8.0 h7b6447c_0
scipy 1.5.2 py37h0b6359f_0
setuptools 50.3.0 py37hb0f4dca_1
six 1.15.0 py_0
sqlite 3.33.0 h62c20be_0
tensorboard 1.14.0 py37hf484d3e_0
tensorflow 1.14.0 gpu_py37h74c33d7_0
tensorflow-base 1.14.0 gpu_py37he45bfe2_0
tensorflow-estimator 1.14.0 py_0
tensorflow-gpu 1.14.0 h0d30ee6_0
termcolor 1.1.0 py37_1
tk 8.6.10 hbc83047_0
tornado 6.1 py37h4abf009_0 conda-forge
werkzeug 1.0.1 py_0
wheel 0.35.1 py_0
wrapt 1.12.1 py37h7b6447c_1
xz 5.2.5 h7b6447c_0
zipp 3.3.1 py_0
zlib 1.2.11 h7b6447c_3
zstd 1.4.5 h6597ccf_2 conda-forgePlease make sure that the "database" folder is in the "main" folder, which consists of two folders, i.e., "geometries" and "properties".
1.Train the model first by type "python train_GAN.py" in the terminal.
2.The training process is only sucessful after seeing "the training process has finished" in the terminal.
3.Generate new structures by type "python generate_new_structure.py" in the terminal.
4.The generation process is only sucessful after seeing "the generation process has finished" in the terminal.
1.Please note that all generated strutures will be overwrite the previous one.
2.Please also note that at least 100GB hard disk is required during the training process.