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