This tutorial will introduce core concepts of machine learning through the lens of a basic workflow to predict material bandgaps from material compositions. As we progress through this workflow, we’ll highlight key steps, challenges that can come up with materials data, and potential solutions to these challenges. The core workflow we’ll introduce includes: Data Cleaning, Feature Generation, Feature Engineering, Establishing Model Assessment, Training a Default Model, Hyperparameter Optimization, and Making Predictions. By the end of the workshop I hope that you’ll have a better understanding of these core concepts, and how they can all fit together.

To preview the materials ahead of time you can find them on the nanoHUB tool Machine Learning Lab Module.

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Benjamin Afflerbach (2021), "An Introduction to Machine Learning for Materials Science: A Basic Workflow for Predicting Materials Properties," https://nanohub.org/resources/35141.

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1. decision trees
2. hands on
3. machine learning
4. Machine Learning Lab
5. material properties
6. materials science
7. NCN Group - Machine Learning Hands-on
8. regression
9. supervised learning
10. tutorial