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])
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 bibtex entry to follow
.
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