This hands-on tutorial will introduce participants to modern tools to manage, organize, and visualize data as well as machine learning techniques to extract information from it. Participants will use APIs to query online repositories, organize and process the resulting data, and use it to build predictive. The activities will include building artificial neural networks and random forests, training them with the data acquired and using these models to make decisions. We will exemplify how active learning can be used to reduce the number of experiments required to arrive at a desired design goal. All simulations will be performed using Jupyter notebooks via nanoHUB and will make use of several online data repositories.

Researchers should cite this work as follows:

• Alejandro Strachan (2020), "Machine Learning in Materials - Center for Advanced Energy Studies and Idaho National Laboratory," https://nanohub.org/resources/34246.

  BibTex | EndNote

1. data science
2. density functional theory (DFT)
3. linear regression
4. machine learning
5. materials science
6. molecular dynamics (MD)
7. NCN Group - Machine Learning
8. neural networks
9. simulation and modeling