CGCNN-HD (https://dx.doi.org/10.1021/acs.jcim.0c00003)
The CGCNN-HD is a python code for dropout-based uncertainty quantification for stability prediction with CGCNN (by T. Xie et al.) developed by prof. Yousung Jung group at KAIST (contact: ysjn@kaist.ac.kr).
Juhwan Noh
- Python3.6
- Numpy
- Pytorch == 0.4.1.post2 (CUDA 8.0)
- Pymatgen
- Sklearn
- ASE
1. Database setting Reference formation energy value of the previous our ChemComm paper (Chem. Commun.,2019,55,13418-13421) can be found in Mg-Mn-O_database/MgMnO_form_e.data.k500.json
If you want to use org_cif database
- cd Mg-Mn-O_database
- tar xvf org_cifs.tar
- cp id_prop.r4_nn8.orgcif.csv org_cifs/id_prop.csv
- cp atom_init.json org_cifs/
If you want to use scaled database
- cd Mg-Mn-O_database
- tar xvf lattice_scaled.tar
- cp id_prop.r4_nn8.scaled.csv lattice_scaled/id_prop.csv
- cp atom_init.json lattice_scaled/
2. Dropout Sampling
If you want to use org_cif database
- Currently, crystal graph is constructed only if maximum number of neighboring atom = 8 and cutoff radius = 4A
- Change root_dir = '/your/data/path/' in dropout_sampling.py to Mg-Mn-O_database/org_cifs/
- python dropout_sampling.py cgcnn_hd_rcut4_nn8.best.pth.tar
- You may get dropout_test.csv file (name,predicted mean,predicted standard deviation)
If you want to use scaled database
- Currently, crystal graph is constructed only if maximum number of neighboring atom = 8 and cutoff radius = 4A
- Change root_dir = '/your/data/path/' in dropout_sampling.py to Mg-Mn-O_database/lattice_scaled/
- python dropout_sampling.py cgcnn_hd_rcut4_nn8.best.pth.tar
- You may get dropout_test.csv file (name,predicted mean,predicted standard deviation)
3. Training with your own database
- Currently, crystal graph is constructed only if maximum number of neighboring atom = 8 and cutoff radius = 4A
- Change root_dir = '/your/data/path/' in model_train.py to path for your own dataset
- Set MYPYTHON="your/python/path" and MODELPREF="your/model/pref" in training.sh with your own setting
- sh training.sh