# CGWGAN | [Paper](https://www.oaepublish.com/articles/jmi.2024.24?utm_campaign=website&utm_medium=email&utm_source=sendgrid.com)


**Content**
+ Site Template: open.db.gz  
+ M3GNet-Calculated Phonon: merge.db  
+ VASP Relaxation Structure Comparison with PyXtal: random_vs.db  

## Crystal Generative Framework Based on Wyckoff Generative Adversarial Network  
In this study, we present the Crystal Generative Framework based on the Wyckoff Generative Adversarial Network (CGWGAN).  

All templates with 3-4 asymmetric units generated in our work are available as open-source resources in the CGWGAN datasets.  

## Python Implementation  

```python
from ase.db import connect

database = connect('open.db')
entry_id = 1  # The crystal index 
atoms = database.get_atoms(id=entry_id)

# Chemical symbols
symbols = atoms.get_chemical_symbols()
# Volume
latt_vol = atoms.get_volume()
# Fractional positions
positions = atoms.get_scaled_positions()
# etc...
```

## Operating and Displaying the DB File  
```bash
# Install CryDBkit
pip install CryDBkit

from CryDBkit import website

website.show('open.db')
```

If you utilize the data or code from this repository, please reference [our paper](https://www.oaepublish.com/articles/jmi.2024.24?utm_campaign=website&utm_medium=email&utm_source=sendgrid.com).

```
@article{su2024cgwgan,
  title={CGWGAN: crystal generative framework based on Wyckoff generative adversarial network},
  author={Su, Tianhao and Cao, Bin and Hu, Shunbo and Li, Musen and Zhang, Tong-Yi},
  journal={Journal of Materials Informatics},
  volume={4},
  number={4},
  pages={N--A},
  year={2024},
  publisher={OAE Publishing Inc.}
}
```