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

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
• Matthew Turner
• Prof. Raphael Finkel

MAST-ML documentation

• An overview of code documentation and tutorials for getting started with MAST-ML can be found here:

https://mastmldocs.readthedocs.io/en/latest/

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 freely available via Github:

https://github.com/uw-cmg/MAST-ML

git clone https://github.com/uw-cmg/MAST-ML

MAST-ML can also be installed via pip:

pip install mastml