Chemical Motif Identifier
Project description
ChemicalMotifIdentifier
[!WARNING] To reviewers: As we are keeping these codes private untill acceptance of the work, the PyPi installation of the package will not work. Please follow the instructions provided in the jupyter notebook that can be found in the examples/ folder.
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 chemicalmotifidentifier
# To install the latest git commit
pip install htpps://github.com/killiansheriff/ChemicalMotifIdentifier.git
Example of usage
A jupyter notebook presenting a few test cases can be found in the examples/ folder.
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