OVITO Python modifier to compute the Warren-Cowley parameters, defined as:
where
Here is an example of how to compute the 1st and 2nd nearest neighbor shell Warren-Cowley parameters of the fcc.dump
dump file. Note that in the fcc crystal structure, the 1st nearest neighbor shell has 12 atoms
, while the second one has 6 atoms
.
from ovito.io import import_file
import WarrenCowleyParameters as wc
pipeline = import_file("fcc.dump")
mod = wc.WarrenCowleyParameters(nneigh=[0, 12, 18], only_selected=False)
pipeline.modifiers.append(mod)
data = pipeline.compute()
wc_for_shells = data.attributes["Warren-Cowley parameters"]
print(f"1NN Warren-Cowley parameters: \n {wc_for_shells[0]}")
print(f"2NN Warren-Cowley parameters: \n {wc_for_shells[1]}")
Example scripts can be found in the examples/
folder.
For a standalone Python package or Conda environment, please use:
pip install --user WarrenCowleyParameters
For OVITO PRO built-in Python interpreter, please use:
ovitos -m pip install --user WarrenCowleyParameters
If you want to install the lastest git commit, please replace WarrenCowleyParameters
by git+https://github.com/killiansheriff/WarrenCowleyParameters.git
.
If any questions, feel free to contact me (ksheriff at mit dot edu).
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{sheriff2024chemicalmotif,
title={Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks},
author={Killian Sheriff and Yifan Cao and Rodrigo Freitas},
journal={arXiv},
year={2024},
doi={10.48550/arXiv.2405.08628}
}