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CGWGAN

Crystal Generative Framework based on Wyckoff Generative Adversarial Network

We present the Crystal Generative Framework based on the Wyckoff Generative Adversarial Network (CGWGAN). CGWGAN utilizes a strategy that focuses on generating crystal templates while effectively masking the occupancy information of elements at specific sites within the crystal structure.

Resources

  • Crystal templates: Available on Hugging Face.
  • Novel crystal data: Available on Figshare.
  • CGWGAN generator: Located in the 'model' folder.
  • Atom infill and high-throughput filter: Found in the 'opt_db' folder.

Prerequisites

  • Ensure that the following packages are installed: phonopy, pymatgen, ase, and a surrogate model such as m3gnet.

Example Setup

  • This example uses m3gnet as the surrogate model.
  • Provide the path to the database that stores structures with substituted elements.
  • Specify this in the ./opt_db/run_all.py file:
file_path = "path_2_db"
db_path = f"{file_path}/data.db"
cif_processor = CIFProcessor(file_path)
structure_processor = StructureProcessor(file_path, db_path)
cif_processor.process_files()
cif_processor.clean()
structure_processor.process_structures()

Contact Information

Acknowledgement

If you utilize the data or code from this repository, please reference our paper.