Co-presented by MRS and ACerS, the American Ceramic Society

Ceramics and glasses are at the forefront of advanced materials and will continue to bring new solutions to global challenges in energy, the environment, healthcare, and information/communication technology. To meet the accelerated pace of modern technology delivery, a more sophisticated approach to the design of advanced ceramics and glass materials must be developed to enable faster, cheaper, and better research and development of new materials compositions for future applications.

In this Webinar, we will discuss application of data science tools toward the design, understanding, and optimization of ceramic and glass materials.

Talk presentations:

Effects of data and Regression techniques on design of robust models for glass properties prediction
Adama Tandia, Corning, Inc.
Talk begins at 6:02
Extrapolation and the role of domain knowledge in machine learning for materials
Bryce Meredig, Citrine Informatics
Talk begins at 43:40
Extrapolating Glass Properties by Topology-Informed Machine Learning
Mathieu Bauchy, University of California, Los Angeles (UCLA)
Talk begins at 1:10:14