CGCNN2

Release PyPI Downloads

As the original Crystal Graph Convolutional Neural Networks (CGCNN) repository is no longer actively maintained, this repository is a reproduction of CGCNN by Xie et al. It includes necessary updates for deprecated components and a few additional functions to ensure smooth operation. Despite its age, CGCNN remains a straightforward and fast deep learning framework that is easy to learn and use.

The package provides the following major functions:

  • Training a CGCNN model using a custom dataset.
  • Predicting material properties with a pre-trained CGCNN model.
  • Fine-tuning a pre-trained CGCNN model on a new dataset.
  • Extracting structural features as descriptors for downstream tasks.

Installation

Make sure you have a Python interpreter, version 3.11 to 3.14. Then, you can simply install cgcnn2 from PyPI using pip:

pip install cgcnn2

or, if you manage your project with uv:

uv add cgcnn2

If you'd like to use the latest unreleased version on the main branch, you can install it directly from GitHub:

pip install git+https://github.com/jcwang587/cgcnn2@main

Get Started

There are entry points for training, predicting, and fine-tuning CGCNN models. For example, to explore the usage of the provided training script cgcnn-tr, you can use the --help option of the command:

cgcnn-tr --help

Similarly, you can access the predicting and fine-tuning functionalities through cgcnn-pr and cgcnn-ft commands. A detailed user guide documentation is available at: https://jcwang.dev/cgcnn2/

References

The original paper describes the CGCNN framework in detail:

@article{PhysRevLett2018,
  title = {Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties},
  author = {Xie, Tian and Grossman, Jeffrey C.},
  journal = {Phys. Rev. Lett.},
  volume = {120},
  issue = {14},
  pages = {145301},
  numpages = {6},
  year = {2018},
  month = {Apr},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.120.145301},
  url = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.145301}
}

Project links

Data verified by PyPI on Jul 7, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for jcwang587 from gravatar.com jcwang587

Credits

Author/Maintainer: Jiacheng Wang

GitHub Statistics

Data verified by PyPI on Jul 7, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python <3.15, >=3.11

Tags

computational-materials fine-tuning graph-model python

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cgcnn2-0.6.0.tar.gz (5.9 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

cgcnn2-0.6.0-py3-none-any.whl (5.9 MB view details)

Uploaded Python 3

File details

Details for the file cgcnn2-0.6.0.tar.gz.

File metadata

  • Download URL: cgcnn2-0.6.0.tar.gz
  • Upload date:
  • Size: 5.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for cgcnn2-0.6.0.tar.gz
Algorithm Hash digest
SHA256 1875c3aacf30f47d4d7d26790320547248f31ed373f3a55082ea7bb03eb7a3e0
MD5 08f360917a72e7ddc91bea41a8687202
BLAKE2b-256 b29cabd14614f694f38e93d0e94099cff769a78085a55ceb379c2fabbb605667

See more details on using hashes here.

Provenance

The following attestation bundles were made for cgcnn2-0.6.0.tar.gz:

Publisher: publish-pypi.yml on jcwang587/cgcnn2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file cgcnn2-0.6.0-py3-none-any.whl.

File metadata

  • Download URL: cgcnn2-0.6.0-py3-none-any.whl
  • Upload date:
  • Size: 5.9 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for cgcnn2-0.6.0-py3-none-any.whl
Algorithm Hash digest
SHA256 8a45716f3220a5467d97843875fcf3acd08c3d70a1cabfff97d103f6ab7695b8
MD5 fe29c0d899eab3050c01a69170a7ea02
BLAKE2b-256 a4cc58a6067ed5135c1129bc809670fd291b4eea1fb74e02cf93537fb1bc5073

See more details on using hashes here.

Provenance

The following attestation bundles were made for cgcnn2-0.6.0-py3-none-any.whl:

Publisher: publish-pypi.yml on jcwang587/cgcnn2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page