SYMPOSIUM 32: Modeling and Simulations of Glass Structures and Properties

Modeling and simulation are integral to advancing materials research, especially in the realm of glass, ceramics, amorphous and nanostructured materials, which possess intricate characteristics and challenges to characterize experimentally. This session aims to delve into sophisticated computer simulations and modeling approaches employed to unravel the structures, properties, and behaviors of glasses and glass-forming liquids. We particularly seek to explore the latest advancements and applications of first-principles, classical, and mesoscale methods, with a focus on their integration to expand the spatial and temporal scales traditionally explored by conventional modeling techniques. Additionally, we encourage numerical investigations that facilitate the interpretation of experimental data and structural validation, utilizing techniques such as X-ray and neutron diffraction, solid-state NMR, and various spectroscopic methods. As an emerging technology, we will also highlight applications of machine-learning interatomic potential in this domain. 

Data-driven methods and artificial intelligence-based models have attracted much attention in recent years to solve complex problems in the field of glass science. Machine learning has been successfully applied to solve long-standing problems, such as predicting composition–property relationships, developing optimized glass compositions, accelerating glass modeling, and even understanding some fundamental aspects of the glass transition. This session will focus on recent advances in the use of machine learning and artificial intelligence in glass science, technology, and modeling. Topics of interest include, but are not limited to, the application of machine learning and artificial intelligence to develop and interpret composition–property relationships, design optimized glass compositions, 3D printing and additive manufacturing of glasses, advanced computational modeling by developing machine-learned interatomic potentials and accelerating glass simulations, image processing, predicting the structure of glasses, identifying key structural patterns/descriptors that govern glass properties, and understanding the fundamentals of the glassy state. 

Proposed Sessions/Topics 

• Atomistic simulation and predictive modeling of glasses 

• Data-driven modeling and machine learning for glass science 

Symposium Organizer(s) 

• Jincheng Du, University of North Texas, USA  

• Xiaonan Lu, Pacific Northwest National Laboratory, USA 

Point(s) of Contact 

• Jincheng Du; jincheng.du@unt.edu  

Symposium Sponsor(s) 

• Glass and Optical Materials Division