# 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,
}
```
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"description": "# WarrenCowleyParameters\n\n \n\n\nOVITO Python modifier to compute the Warren-Cowley parameters, defined as:\n\n$$\\alpha_{ij}^m = 1-\\frac{p_{ij}^m}{c_j},$$ \n\nwhere $m$ 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. \nA 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.\n\n## Utilisation \n\nHere 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``. \n\n```python\nfrom ovito.io import import_file\nimport WarrenCowleyParameters as wc\n\npipeline = import_file(\"fcc.dump\")\nmod = wc.WarrenCowleyParameters(nneigh=[0, 12, 18], only_selected=False)\npipeline.modifiers.append(mod)\ndata = pipeline.compute()\n\nwc_for_shells = data.attributes[\"Warren-Cowley parameters\"]\nprint(f\"1NN Warren-Cowley parameters: \\n {wc_for_shells[0]}\")\nprint(f\"2NN Warren-Cowley parameters: \\n {wc_for_shells[1]}\")\n\n\n# Alternatively, can see it as a dictionarry\n# print(data.attributes[\"Warren-Cowley parameters by particle name\"])\n\n```\nExample scripts can be found in the ``examples/`` folder.\n\n\n\n## Installation\nFor a standalone Python package or Conda environment, please use:\n```bash\npip install --user WarrenCowleyParameters\n```\n\nFor *OVITO PRO* built-in Python interpreter, please use:\n```bash\novitos -m pip install --user WarrenCowleyParameters\n```\n\nIf you want to install the lastest git commit, please replace ``WarrenCowleyParameters`` by ``git+https://github.com/killiansheriff/WarrenCowleyParameters.git``.\n\n## Contact\nIf any questions, feel free to contact me (ksheriff at mit dot edu).\n\n## References & Citing \nIf you use this repository in your work, please cite:\n\n```\n@article{sheriffquantifying2024,\n\ttitle = {Quantifying chemical short-range order in metallic alloys},\n\tdoi = {10.1073/pnas.2322962121},\n\tjournaltitle = {Proceedings of the National Academy of Sciences},\n\tauthor = {Sheriff, Killian and Cao, Yifan and Smidt, Tess and Freitas, Rodrigo},\n\tdate = {2024-06-18},\n}\n```\n\nand \n\n```\n@article{sheriff2024chemicalmotif,\n title = {Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks},\n DOI = {10.1038/s41524-024-01393-5},\n journal = {npj Computational Materials},\n author = {Sheriff, Killian and Cao, Yifan and Freitas, Rodrigo},\n year = {2024},\n month = sep,\n}\n```\n",
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