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: 4 days 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 (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-07T15:21:21.000Z (about 2 months ago)
• Last Synced: 2024-12-09T05:42:44.801Z (14 days ago)
• Topics: high-entropy-alloys, materials-science, order-parameters, ovito, python-modifiers, warren-cowley
• Language: Python
• Homepage:
• Size: 1.25 MB
• Stars: 23
• Watchers: 2
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml

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README

        
# WarrenCowleyParameters

![PyPI Version](https://img.shields.io/pypi/v/WarrenCowleyParameters.svg) ![PyPI Downloads](https://static.pepy.tech/badge/WarrenCowleyParameters)

![tests](https://github.com/killiansheriff/WarrenCowleyParameters/actions/workflows/python-tests.yml/badge.svg)

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.

![](media/wc_bar_plot.png)

## 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,

}

```