Methods rooted in data science, machine learning, and artificial intelligence have become necessary components of materials design endeavors, being frequently applied in conjunction with both computational and experimental data. The now thriving field of materials informatics has seen the accelerated discovery of new battery materials, solar cell absorbers, thermoelectrics, and routes for autonomous synthesis and characterization. The need to educate and train the materials science workforce on the essential elements of machine learning has never been greater.

A series of tutorials on machine learning and artificial intelligence were presented at the MRS Spring 2022, MRS Fall 2022 and MRS Spring 2023 meetings. This webinar will overview the material presented in these tutorials and provide a sampling of the content. The complete tutorial recordings will be made available to attendees following this live event.

Methods rooted in data science, machine learning, and artificial intelligence have become necessary components of materials design endeavors, being frequently applied in conjunction with both computational and experimental data. The now thriving field of materials informatics has seen the accelerated discovery of new battery materials, solar cell absorbers, thermoelectrics, and routes for autonomous synthesis and characterization. The need to educate and train the materials science workforce on the essential elements of machine learning has never been greater.

A series of tutorials on machine learning and artificial intelligence were presented at the MRS Spring 2022, MRS Fall 2022 and MRS Spring 2023 meetings. This webinar will overview the material presented in these tutorials and provide a sampling of the content.

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

Part 1: Introduction to Machine Learning

Part 2: Gaussian Process Regression

Part 3: Neural Networks

Part 4: Battery Informaties video 1

Part 5: Battery Informaties video 2