Session 5: Computer simulation and predictive modeling of glasses
Modeling and simulation plays an increasingly important role in materials research. This is in particular the case for glasses, amorphous, and nanostructured materials due to their complex nature. This session will focus on computer simulations and modeling approaches to gain insight into the structures and properties of glasses and glass-forming liquids. Furthermore, machine learning, deep learning, big data and their applications in predictive modeling of glass properties will be covered. Recent development of classical and first principles methods including empirical potentials, efficient first principles algorithms, as well as applications in simulations in complex interfaces and interfacial behaviors such water/glass interactions are also of particular interest. Also welcome are numerical studies that help the interpretation of experimental data and structural validation using methods such as X-ray and neutron diffraction, solid-state NMR, and other spectroscopic techniques. Finally, integrated computational material design of glass compositions using physics-based modeling and simulation methods will also be covered.
Walter Kob, Universite Montpellier II
Carlo Massobrio, Institut de Physique et Chimie des Matériaux de Strasbourg
Tandia Adama, Corning Incorporated
Jessica Rimsza, Sandia National Laboratory
Hiroyuki Inoue, University of Tokyo
Jincheng Du, University of North Texas