This module introduces active learning in the context of materials discovery with hands-on online simulations. Active learning is a subset of machine learning where the information available at a given time is used to decide what areas of space to explore next. In this module, we will explore active learning to reduce the number of experiments required to find a material with desired properties.

This end-to-end module is designed to be self-contained and easy to incorporate in existing courses or used for self-study. The module consists of three components:

• Pre-recoded lecture: introduction to active learning in science and engineering YouTube | Video Download (MP4) | Slides (PDF)
• Hands-on tutorial using nanoHUB: high Li+ conductivity oxides Download (PDF)
• Homework Assignment Download (PDF)

This module is part of a series on data science and machine learning for engineering and physical sciences. Users will be able to run interactive code online using nanoHUB, no need to download or install any software.

Learning objectives. After completing this module, you will:

• Be able to use and modify active learning workflows
• Evaluate different information acquisition functions
• Use active learning to reduce the number of experiments in materials discovery or design

Pre-requisites

• Basic Python programming (see https://nanohub.org/resources/33266)
• Querying materials repositories (optional, see material in this series)
• Random forests including uncertainty quantification (optional, see material in this series)

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Alejandro Strachan, Juan Carlos Verduzco Gastelum (2020), "Module 7: Active Learning for Design of Experiments," https://nanohub.org/resources/34272.

  BibTex | EndNote

1. batteries
2. design of experiments
3. machine learning
4. materials discovery
5. materials science
6. NCN Group - Machine Learning Modules
7. ramdom forrests