Machine Learning for Phase Behavior in Active Matter

This package is used to predict the phase of individual particles in suspensions of active colloids.

Dependencies

These scipts have been tested using Python 3.7.8, with the following packages (and their dependencies):

• pandas==1.1.2
• scipy==1.5.2
• scikit-learn==0.23.2
• tensorflow-gpu==2.2.0
• glob2==0.7
• pytorch==1.3.1
• xgboost==1.1.0
• hyperopt==0.2.4

Packages for simulation inputs

• HOOMD==2.3.4
• gsd==2.2.0

Additionally, CUDA 10.2 and cuDNN 7 have been used.

Reference

If you make use of these models or methods in your research, please cite the following in your manuscript:

@article{
    author ="Dulaney, Austin R. and Brady, John F.",
    title  ="Machine learning for phase behavior in active matter systems",
    journal  ="Soft Matter",
    year  ="2021",
    pages  ="-",
    publisher  ="The Royal Society of Chemistry",
    doi  ="10.1039/D1SM00266J",
    url  ="http://dx.doi.org/10.1039/D1SM00266J",
}

License

MIT