https://github.com/killiansheriff/warrencowleyparameters
OVITO Python modifier to compute the Warren-Cowley parameters.
https://github.com/killiansheriff/warrencowleyparameters
high-entropy-alloys materials-science order-parameters ovito python-modifiers warren-cowley
Last synced: 7 months ago
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OVITO Python modifier to compute the Warren-Cowley parameters.
- Host: GitHub
- URL: https://github.com/killiansheriff/warrencowleyparameters
- Owner: killiansheriff
- Created: 2023-08-07T15:21:03.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-07T15:21:21.000Z (12 months ago)
- Last Synced: 2025-03-05T07:45:23.919Z (8 months ago)
- Topics: high-entropy-alloys, materials-science, order-parameters, ovito, python-modifiers, warren-cowley
- Language: Python
- Homepage:
- Size: 1.25 MB
- Stars: 27
- Watchers: 2
- Forks: 8
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
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README
# 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``.
```python
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]}")
# Alternatively, can see it as a dictionarry
# print(data.attributes["Warren-Cowley parameters by particle name"])
```
Example scripts can be found in the ``examples/`` folder.
## Installation
For a standalone Python package or Conda environment, please use:
```bash
pip install --user WarrenCowleyParameters
```
For *OVITO PRO* built-in Python interpreter, please use:
```bash
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{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,
}
```