ChemicalMotifIdentifier

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{sheriffquantifying2024,
	title = {Quantifying chemical short-range order in metallic alloys},
	doi = {10.1073/pnas.2322962121},
	journaltitle = {Proceedings of the National Academy of Sciences},
	author = {Sheriff, Killian and Cao, Yifan and Smidt, Tess and Freitas, Rodrigo},
	date = {2024-06-18},
}

and

@article{sheriff2024chemicalmotif,
  title = {Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks},
  DOI = {10.1038/s41524-024-01393-5},
  journal = {npj Computational Materials},
  author = {Sheriff,  Killian and Cao,  Yifan and Freitas,  Rodrigo},
  year = {2024},
  month = sep,
}