Co-Presented by MRS, the Materials Research Society and ACerS, the American Ceramic Society

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Data-Driven Ceramics and Glass Research

Date: July 15, 2020

Time: 09:00AM - 10:30AM

You must be registered to participate!

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
       
Hosts: Speakers:

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:

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