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https://github.com/killiansheriff/atomisticreversemontecarlo

OVITO Python modifier to generate bulk crystal structures with target Warren-Cowley parameters.

https://github.com/killiansheriff/atomisticreversemontecarlo

Science Score: 49.0%

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    CITATION.cff file
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    Found 1 DOI reference(s) in README
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    Academic publication links
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    1 of 2 committers (50.0%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (11.3%) to scientific vocabulary

Keywords

high-entropy-alloys materials-science monte-carlo monte-carlo-simulation order-parameters ovito python-modifiers reverse-monte-carlo warren-cowley-parameters
Last synced: 7 months ago · JSON representation

Repository

OVITO Python modifier to generate bulk crystal structures with target Warren-Cowley parameters.

Basic Info
  • Host: GitHub
  • Owner: killiansheriff
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 10.8 MB
Statistics
  • Stars: 25
  • Watchers: 2
  • Forks: 3
  • Open Issues: 0
  • Releases: 2
Topics
high-entropy-alloys materials-science monte-carlo monte-carlo-simulation order-parameters ovito python-modifiers reverse-monte-carlo warren-cowley-parameters
Created over 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme

README.md

Atomistic Reverse Monte-Carlo

PyPI Version PyPI Downloads

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 exportfile, importfile

from AtomisticReverseMonteCarlo import AtomisticReverseMonteCarlo

mod = AtomisticReverseMonteCarlo( nneigh=12, # number of neighbors to compute WC parameters (12 1NN in fcc) T=1e-9, # rMC temperature targetwc=[ # wc target 1-pij/cj [0.32719603, -0.19925471, -0.12794131], [-0.19925471, 0.06350427, 0.13575045], [-0.12794131, 0.13575045, -0.00762235], ], tolpercentdiff=[ # max percent tolerence allowed before stopping [1, 1, 1], [1, 1, 1], [1, 1, 1], ],
saverate=1000, # save rate seed=123, max_iter=None, # infinity number of iterations )

Load the intial snapshot

pipeline = importfile("fccrandom.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"]}')

exportfile( data, "fccwc.dump", "lammps/dump", columns=[ "Particle Identifier", "Particle Type", "Position.X", "Position.Y", "Position.Z", ], ) ` The script can be found in theexamples`` 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:

@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}, }

Owner

  • Name: Killian Sheriff
  • Login: killiansheriff
  • Kind: user
  • Location: Cambridge, MA
  • Company: MIT

@MIT DMSE Ph.D. Student | @McGillUPhysics graduate | Doing Materials Science + AI research to better understand high-entropy alloys.

GitHub Events

Total
  • Watch event: 5
  • Push event: 2
  • Fork event: 1
Last Year
  • Watch event: 5
  • Push event: 2
  • Fork event: 1

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 69
  • Total Committers: 2
  • Avg Commits per committer: 34.5
  • Development Distribution Score (DDS): 0.058
Past Year
  • Commits: 3
  • Committers: 1
  • Avg Commits per committer: 3.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Killian Sheriff k****f@m****u 65
Daniel Utt u****t@o****g 4
Committer Domains (Top 20 + Academic)
ovito.org: 1 mit.edu: 1

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 0
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 12 minutes
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
Pull Request Authors
  • nnn911 (1)
Top Labels
Issue Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 20 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 3
  • Total maintainers: 1
pypi.org: atomisticreversemontecarlo

OVITO Python modifier to generate bulk crystal structures with target Warren-Cowley parameters.

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 20 Last month
Rankings
Dependent packages count: 7.6%
Average: 38.5%
Dependent repos count: 69.5%
Maintainers (1)
KillianSheriff
Last synced: 8 months ago