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Tutorial MT00—Machine Learning in Materials Science—From Basic Concepts to Active Learning was presented at the 2024 MRS Spring Meeting. This webinar will overview the material presented in these tutorials and provide a sampling of the content. Attendees will have access to the full recorded tutorials after the webinar.

Machine Learning (ML) and Artificial Intelligence (AI) are powerful techniques that materials scientists can use to help analyze their data, choose experiments, and discover new materials. This tutorial will introduce basic techniques for ML and AI, all from a materials 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 materials 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 materials science problems.
Use Deep Learning techniques to analyze large data sets.

We invite you to view the complete recorded tutorial session, which is presented in three parts:

Introduction to Machine Learning and Artificial Intelligence

Arun Mannodi Kanakkithodi, Purdue University

Gaussian Process Regression and Active Learning

Austin McDannald, National Institute of Standards and Technology

Deep Learning With Neural Networks

Saaketh Desai, Sandia National Laboratories