This tutorial contains an introduction to the use of the Materials Simulation Toolkit for Machine Learning (MAST-ML), a python package designed to broaden and accelerate the use of machine learning and data science methods for materials property prediction. Through hands-on activities, we will use MAST-ML to:

1. import materials datasets from online databases and clean and examine our input data,
2. conduct feature engineering analysis, including generation, preprocessing, and selection of features,
3. construct, evaluate and compare the performance of different model types and data splitting techniques, and
4. conduct a preliminary assessment of model error analysis and uncertainty quantification (UQ).

MAST-ML resources:

MAST-ML on nanoHUB: https://nanohub.org/tools/mastmltutorial

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

Publication: https://doi.org/10.1016/j.commatsci.2020.109544

MAST-ML tutorials: https://github.com/uw-cmg/MAST-ML/tree/master/examples

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Ryan Jacobs (2021), "The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction," https://nanohub.org/resources/35142.

  BibTex | EndNote

1. data science
2. diffusion
3. hands on
4. machine learning
5. MAST-ML
6. material properties
7. materials science
8. NCN Group - Machine Learning Hands-on
9. python
10. supervised learning
11. tutorial