| Name | AtomisticReverseMonteCarlo JSON |
| Version |
0.0.3
JSON |
| download |
| home_page | |
| Summary | OVITO Python modifier to generate bulk crystal structures with target Warren-Cowley parameters. |
| upload_time | 2023-08-10 15:34:45 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | MIT License |
| keywords |
ovito
python-modifier
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
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| coveralls test coverage |
No coveralls.
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# Atomistic Reverse Monte-Carlo
 
OVITO Python modifier to generate bulk crystal structures with target Warren-Cowley parameters.
## Usage
Here's an example on how to use the code to create the ``fcc_wc.dump`` file which has Warren-Cowley parameters that falls within a 1% difference of the targeted ones:
```python
from ovito.io import export_file, import_file
from AtomisticReverseMonteCarlo import AtomisticReverseMonteCarlo
mod = AtomisticReverseMonteCarlo(
nneigh=12, # number of neighbors to compute WC parameters (12 1NN in fcc)
T=1e-9, # rMC temperature
target_wc=[ # wc target 1-pij/cj
[0.32719603, -0.19925471, -0.12794131],
[-0.19925471, 0.06350427, 0.13575045],
[-0.12794131, 0.13575045, -0.00762235],
],
tol_percent_diff=[ # max percent tolerence allowed before stopping
[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
],
save_rate=1000, # save rate
seed=123,
max_iter=None, # infinity number of iterations
)
# Load the intial snapshot
pipeline = import_file("fcc_random.dump")
pipeline.modifiers.append(mod)
data = pipeline.compute()
# Load data of the last trajectory
data = pipeline.compute(-1)
print(f'Target Warren-Cowley parameters: \n {data.attributes["Target Warren-Cowley parameters"]}')
print(f'Warren-Cowley parameters: \n {data.attributes["Warren-Cowley parameters"]}')
print(f'Warren-Cowley Percent error: \n {data.attributes["Warren-Cowley percent error"]}')
export_file(
data,
"fcc_wc.dump",
"lammps/dump",
columns=[
"Particle Identifier",
"Particle Type",
"Position.X",
"Position.Y",
"Position.Z",
],
)
```
The script can be found in the ``examples`` directory.
## Installation
For a standalone Python package or Conda environment, please use:
```bash
pip install --user AtomisticReverseMonteCarlo
```
For *OVITO PRO* built-in Python interpreter, please use:
```bash
ovitos -m pip install --user AtomisticReverseMonteCarlo
```
If you want to install the lastest git commit, please replace ``AtomisticReverseMonteCarlo`` with ``git+https://github.com/killiansheriff/AtomisticReverseMonteCarlo``.
## 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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"description": "# Atomistic Reverse Monte-Carlo \n \n\nOVITO Python modifier to generate bulk crystal structures with target Warren-Cowley parameters. \n\n## Usage \nHere's an example on how to use the code to create the ``fcc_wc.dump`` file which has Warren-Cowley parameters that falls within a 1% difference of the targeted ones:\n\n```python \nfrom ovito.io import export_file, import_file\n\nfrom AtomisticReverseMonteCarlo import AtomisticReverseMonteCarlo\n\nmod = AtomisticReverseMonteCarlo(\n nneigh=12, # number of neighbors to compute WC parameters (12 1NN in fcc)\n T=1e-9, # rMC temperature\n target_wc=[ # wc target 1-pij/cj\n [0.32719603, -0.19925471, -0.12794131],\n [-0.19925471, 0.06350427, 0.13575045],\n [-0.12794131, 0.13575045, -0.00762235],\n ],\n tol_percent_diff=[ # max percent tolerence allowed before stopping\n [1, 1, 1],\n [1, 1, 1],\n [1, 1, 1],\n ], \n save_rate=1000, # save rate\n seed=123,\n max_iter=None, # infinity number of iterations\n)\n\n# Load the intial snapshot \npipeline = import_file(\"fcc_random.dump\")\npipeline.modifiers.append(mod)\ndata = pipeline.compute()\n\n# Load data of the last trajectory\ndata = pipeline.compute(-1)\nprint(f'Target Warren-Cowley parameters: \\n {data.attributes[\"Target Warren-Cowley parameters\"]}')\nprint(f'Warren-Cowley parameters: \\n {data.attributes[\"Warren-Cowley parameters\"]}')\nprint(f'Warren-Cowley Percent error: \\n {data.attributes[\"Warren-Cowley percent error\"]}')\n\nexport_file(\n data,\n \"fcc_wc.dump\",\n \"lammps/dump\",\n columns=[\n \"Particle Identifier\",\n \"Particle Type\",\n \"Position.X\",\n \"Position.Y\",\n \"Position.Z\",\n ],\n)\n```\nThe script can be found in the ``examples`` directory.\n\n## Installation\nFor a standalone Python package or Conda environment, please use:\n```bash\npip install --user AtomisticReverseMonteCarlo\n```\n\nFor *OVITO PRO* built-in Python interpreter, please use:\n```bash\novitos -m pip install --user AtomisticReverseMonteCarlo\n```\n\nIf you want to install the lastest git commit, please replace ``AtomisticReverseMonteCarlo`` with ``git+https://github.com/killiansheriff/AtomisticReverseMonteCarlo``.\n\n\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 ``bibtex entry to follow``.\n",
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