Instructors: Austin McDannald, National Institute of Standards and Technology; Brian DeCost, National Institute of Standards and Technology; Gilad Kusne, National Institute of Standards and Technology

Machine Learning (ML) and Artificial Intelligence (AI) are powerful techniques that material scientists can use to help analyze their data, choose experiments, and discover new materials. This tutorial will introduce basic techniques for machine learning and AI, all from a material science perspective. Part of the purpose of this tutorial will be explaining how many of these techniques work, dispelling myths arising from the hype from popular culture. We will also show how these tools can be used for more rigorous material science studies, and how doing so differs from the prototypical ML and AI methods designed by computer science and social for use with largely unstructured. We show how to adapt the ML and AI methods to the particular data challenges in materials science with the goals of answering scientific inquiries.  

 After the tutorial, attendees will be familiar with and have the resources to:

• Apply the basics of both supervised and unsupervised ML and AI techniques.
• Apply Gaussian Processes and Active Learning to material science problems.
• Use Deep Learning techniques to analyze large data sets.