Skip to main content

Chemical Motif Identifier

Project description

ChemicalMotifIdentifier

PyPI Version PyPI Downloads

This repository contains the codes necessary to perform a chemical-motif characterization of short-range order, as described in our Quantifying chemical short-range order in metallic alloys paper and our Chemical-motif characterization of short-range order using E(3)-equivariant graph neural networks paper.

This framework allows for correlating any per-atom property to their local chemical motif. It also allows for the determination of predictive short-range chemical fluctuations length scale. It is based on E(3)-equivariant graph neural networks. Our framework has 100% accuracy in the identification of any motif that could ever be found in an fcc, bcc, or hcp solid solution with up to 5 chemical elements.

Instalation

# To install the latest PyPi release
pip install --upgrade chemicalmotifidentifier

# To install the latest git commit 
pip install --upgrade git+https://github.com/killiansheriff/ChemicalMotifIdentifier.git

You will also need to install torch, torch_scatter and torch_geometric.

Example of usage

A jupyter notebook presenting a few test cases can be found in the examples/ folder.

References & Citing

If you use this repository in your work, please cite:

@article{sheriff2023quantifying,
  title={Quantifying chemical short-range order in metallic alloys},
  author={Sheriff, Killian and Cao, Yifan and Smidt, Tess and Freitas, Rodrigo},
  journal={arXiv},
  year={2023},
  doi={10.48550/arXiv.2311.01545}
}

and

@article{TBD
}

Project details


Download files

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

Source Distribution

chemicalmotifidentifier-0.0.9.tar.gz (29.7 kB view details)

Uploaded Source

Built Distribution

chemicalmotifidentifier-0.0.9-py3-none-any.whl (31.8 kB view details)

Uploaded Python 3

File details

Details for the file chemicalmotifidentifier-0.0.9.tar.gz.

File metadata

File hashes

Hashes for chemicalmotifidentifier-0.0.9.tar.gz
Algorithm Hash digest
SHA256 1f7a35751e950b6613d9d71ef9702510ac556aa51f4b62f601593c1dd4aaed0f
MD5 5bdb3ca78e819329e5d5a61d9ec07de9
BLAKE2b-256 bafb1270ba910eff89f4790aa7de338fb9605451348af85671eb822d14d174a4

See more details on using hashes here.

File details

Details for the file chemicalmotifidentifier-0.0.9-py3-none-any.whl.

File metadata

File hashes

Hashes for chemicalmotifidentifier-0.0.9-py3-none-any.whl
Algorithm Hash digest
SHA256 eb604d286e8bbef97ab82cf0995918a15d9677fc928308b58277f73d935d12cb
MD5 1cac24f7a5ae9ff545ab056411fc4cbb
BLAKE2b-256 9166d27b94d2a8e7b6996b423b3987b64980888c6ee237405e4f0977ae06c3d0

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page