Materials Simulation Toolkit for Machine Learning (MAST-ML) tutorial

Tutorial showing the many use cases for the MAST-ML package to build, evaluate and analyze machine learning models for materials applications.

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Version 3.0.1 - published on 07 May 2021

doi:10.21981/N0GQ-TT26 cite this

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Abstract

Welcome to the Tutorial series for using the 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 methods in materials science

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

Paper Citation: https://doi.org/10.1016/j.commatsci.2020.109544

Tool Contents

Tutorial 1: Getting Started with MAST-ML

Tutorial 2: Data Import and Cleaning with MAST-ML

Tutorial 3: Feature Engineering with MAST-ML

Tutorial 4: Models and Data Splitting Tests with MAST-ML

Tutorial 5: Left out data, nested cross validation, and optimized models with MAST-ML

Tutorial 6: Model error analysis and uncertainty quantification with MAST-ML

Credits

University of Wisconsin-Madison Computational Materials Group

Publications

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).

Cite this work

Researchers should cite this work as follows:

  • Ryan Jacobs, BENJAMIN AFFLERBACH (2021), "Materials Simulation Toolkit for Machine Learning (MAST-ML) tutorial," https://nanohub.org/resources/mastmltutorial. (DOI: 10.21981/N0GQ-TT26).

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