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

• Tutorial 1: Getting Started with MAST-ML:

• Tutorial 2: Data Import and Cleaning:

• Tutorial 3: Feature Engineering:

• Tutorial 4: Models and Data Splitting Tests:

• Tutorial 5: Left out data, nested cross validation, and optimized models:

• Tutorial 6: Model error analysis and uncertainty quantification:

• Tutorial 7: Model predictions with guide rails:

• Run random forest materials property models with predictions, calibrated error bars, and domain of applicability guidance:

University of Wisconsin-Madison Computational Materials Group:

• Prof. Dane Morgan
• Dr. Ryan Jacobs
• Dr. Lane Schultz
• Dr. Tam Mayeshiba
• Dr. Ben Afflerbach
• Dr. Henry Wu

University of Kentucky contributors:

• Luke Harold Miles
• Robert Max Williams
• Matthew Turner
• Prof. Raphael Finkel

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

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

This work was funded by the National Science Foundation (NSF) SI2 award number 1148011

This work was funded by the National Science Foundation (NSF) DMREF award number DMR-1332851

This work was funded by the National Science Foundation (NSF) CSSI award number 1931298

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

If you find the uncertainty quantification (error bar) approaches useful, please cite the following publication:

Palmer, G., Du, S., Politowicz, A., Emory, J. P., Yang, X., Gautam, A., Gupta, G., Li, Z., Jacobs, R., Morgan, D., "Calibration after bootstrap for accurate uncertainty quantification in regression models", npj Computational Materials 8 115 (2022). https://doi.org/10.1038/s41524-022-00794-8

If you find the domain of applicability approaches useful, please cite the following publication:

Schultz, L. E., Wang, Y., Jacobs, R., Morgan, D., "A general approach for determining applicability domain of machine learning models", npj Computational Materials 11 95 (2025). https://doi.org/10.1038/s41524-025-01573-x

MAST-ML can be installed via pip:

pip install mastml

Clone from Github:

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

• Integration of domain of applicability approach using kernel density estimates based on MADML package: https://github.com/leschultz/materials_application_domain_machine_learning

• Refinement of tutorials, addition of new tutorial for domains of applicability

• Updates to plotting routines for error bar analysis

• Many small bug fixes and updates to conform to updated versions of package dependencies

• Refinement of tutorials, addition of Tutorial 7, Colab links as badges added for easier use.

• mastml_predictor module added to help streamline making predictions (with option to include error bars) on new test data.

• Basic parallelization added, which is especially useful for speeding up nested CV runs with many inner splits.

• EnsembleModel now handles ensembles of GPR and XGBoost models.

• Numerous improvements to plotting, including new plots (QQ plot), better axis handling and error bars (RvE plot), plotting and stats separated per group if groups are specified.

• Improvements to feature selection methods. EnsembleModelFeatureSelector includes dummy feature references, added SHAP-based selector

• Added assessment of baseline tests like comparing metrics to predicting the data average or permuted data test

• Many miscellaneous bug fixes.

• MAST-ML no longer uses an input file. The core functionality and workflow of MAST-ML has been rewritten to be more conducive to use in a Jupyter notebook environment. This major change has made the code more modular and transparent, and we believe more intuitive and easier to use in a research setting. The last version of MAST-ML to have input file support was version 2.0.20 on PyPi.

• Each component of MAST-ML can be run in a Jupyter notebook environment, either locally or through a cloud-based service like Google Colab. As a result, we have completely reworked our use-case tutorials and examples. All of these MAST-ML tutorials are in the form of Jupyter notebooks and can be found in the mastml/examples folder on Github.

• An active part of improving MAST-ML is to provide an automated, quantitative analysis of model domain assessement and model prediction uncertainty quantification (UQ). Version 3.x of MAST-ML includes more detailed implementation of model UQ using new and established techniques.