Acknowledgements

Materials Simulation Toolkit for Machine Learning (MAST-ML)

MAST-ML is an open-source Python package designed to broaden and accelerate the use of machine learning in materials science research

As of MAST-ML version 3.x, much of the original code and workflow have been rewritten. The use of an input file in version 2.x and older has been removed in favor of a more modular Jupyter notebook computing environment. Please see the examples and tutorials under the mastml/examples folder for a guide in using MAST-ML

Contributors

University of Wisconsin-Madison Computational Materials Group:

  • Prof. Dane Morgan

  • Dr. Ryan Jacobs

  • Dr. Tam Mayeshiba

  • Ben Afflerbach

  • Dr. Henry Wu

University of Kentucky contributors:

  • Luke Harold Miles

  • Robert Max Williams

  • Prof. Raphael Finkel

University of Wisconsin-Madison Undergraduate Skunkworks members (Spring 2021):

  • Avery Chan

  • Min Yi Lin

  • Hock Lye Lee

MAST-ML documentation:

An overview of code documentation and guides for installing MAST-ML can be found here

A number of Jupyter notebook tutorials detailing different MAST-ML use cases can be found here

Funding

This work was and is funded by the National Science Foundation (NSF) SI2 award No. 1148011 and DMREF award number DMR-1332851

Citing MAST-ML

If you find MAST-ML useful, please cite the following publication:

Jacobs, R., Mayeshiba, T., Afflerbach, B., Miles, L., Williams, M., Turner, M., Finkel, R., Morgan, D., “The Materials Simulation Toolkit for Machine Learning (MAST-ML): An automated open source toolkit to accelerate data- driven materials research”, Computational Materials Science 175 (2020), 109544. https://doi.org/10.1016/j.commatsci.2020.109544

Code Repository

MAST-ML is available via PyPi: pip install mastml

MAST-ML is available via Github

git clone –single-branch master https://github.com/uw-cmg/MAST-ML