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Acknowledgements
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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