OVITO Python modifier to compute Warren-Cowley parameters.
Project description
WarrenCowleyParameters
OVITO Python modifier to compute the Warren-Cowley parameters, defined as:
$$\alpha_{ij}^m = 1-\frac{p_{ij}^m}{c_j},$$
where $m$ denotes the $m$-th nearest-neighbor shell, $p_{ij}^m$ is the average probability of finding a $j$-type atom around an $i$-type atom in the $m$-th shell, and $c_j$ is the average concentration of $j$-type atom in the system. A negative $\alpha_{ij}^m$ suggests the tendency of $j$-type clustering in the $m$-th shell of an $i$-type atom, while a positive value means repulsion.
Utilisation
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.
Installation
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
.
Contact
If any questions, feel free to contact me (ksheriff at mit dot edu).
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
Built Distribution
Hashes for warrencowleyparameters-1.0.0.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3ad92acf5d72fb883d106aa819d8ed1832f1feef9f2f6942a71d9e13bb5bef47 |
|
MD5 | 52fe3c1f7b5aeaa53600c0d8f0c3f53e |
|
BLAKE2b-256 | 09467ae2ec5306a7afeee45037c69dc3b594d7642a81cc4851159a9cd17096ee |
Hashes for WarrenCowleyParameters-1.0.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fbc0046c40e04ac0c891505e742f1a9dfa97d289478e53ea220af63a87b9b03b |
|
MD5 | 40745a2a6746355cd8fee6d671151925 |
|
BLAKE2b-256 | 9021fb320e52108a35481546cde4e13af5a0ffe9bca440d085f88cc89aea0634 |