GOMD S1 session: Data-based Modeling and Machine Learning for Glass Science

Data-driven modeling and artificial intelligence have attracted much attention in recent years to solve complex problems in the field of glass science. Specifically, machine learning has been successfully applied to outstanding problems such as predicting composition–property relationships, developing optimized glass compositions, accelerating glass modeling, and understanding fundamentals of glass transition.

This session focuses on recent advances achieved using machine learning in the areas of glass science, technology, and modeling. Topics of interest include, but are not restricted to, the application of machine learning and artificial intelligence to develop composition-property relationships; design-optimized glass compositions; 3D printing and additive manufacturing of glass; advance computational modeling by developing machine-learned interatomic potentials and accelerating glass simulations; image processing; predicting the structure of glass; identifying key structural patterns/descriptors that govern glass properties; and understanding the fundamentals of the glassy state.

Organizers

• Adama Tandia, Corning Inc., USA, TandiaA@Corning.com
• Mathieu Bauchy, University of California Los Angeles, USA, bauchy@ucla.edu
• M. Anoop Krishan, Indian Institute of Technology Delhi, India, krishnan@iitd.ac.in