Glasses, Optical Materials, and their Functional Applications: Current Issues in Science & Technology: ACerS GOMD Alfred R. Cooper Award Session
Sponsored by: ACerS Glass & Optical Materials Division
Program Organizers: Jincheng Du, University of North Texas; S. K. Sundaram, Alfred University
Tuesday 2:00 PM
November 3, 2020
Room: Virtual Meeting Room 15
Location: MS&T Virtual
Session Chair: Steve Martin, Iowa State University
2:00 PM Invited
Cooper Distinguished Lecture: Exploring the Amorphous State of Matter by Roaming About the Network Building Blocks: John Kieffer1; 1University of Michigan
The network concept is widely applied for the description of amorphous materials, e.g., in the context of bonding topology, entanglement, gelation, jamming, etc., aspects that are all synonymous with mechanical stiffness. However, similar geometric criteria must characterize the migration pathways of mobile modifier cations, as these are delineated by the network structure. Hence, juxtaposing these antithetical glass properties can potentially reveal new insights into the amorphous state of matter. Indeed, we have shown how the adiabatic elastic modulus determined using Brillouin scattering, a technique that probes the nano-scale, serves to derive building block speciation in mixed network former glasses. Furthermore, the adiabatic bulk modulus and the activation energy for modifier cation migration are strongly anti-correlated, which led to an improved transition state theory model for cation hopping in glasses. By integrating MD simulations with this characterization approach, we endeavor to derive materials design criteria for amorphous solid-state electrolytes. Funding: NSF-DMR_1610742.
2:40 PM Invited
2020 Alfred R. Cooper Young Scholar Award Presentation: Beyond the Average: A Statistical Mechanical Exploration of Topological Fluctuations in Glass-Forming Systems: Katelyn Kirchner1; 1Pennsylvania State University
The macroscopic properties of any material system are dictated by its atomic structure; however, the presence of structural and topological fluctuations dramatically alters the properties and performance of the material for a given application. Glass is unique in that it has a disordered atomic arrangement, meaning that the properties of glasses are based on statistical distributions rather than precisely known values. Up until this point, there have been no rigorous theories established to predict these statistical distributions. Glass properties have traditionally been represented as mean values, which do not fully represent the complexity of glass structure. This paper introduces a rigorous approach for quantifying fluctuations in glass structure which will enable scientists to improve their understanding of fundamental glass physics and chemistry. The model is first shown for arbitrary glassy systems to clarify the physical understanding and outline the general approach for calculating distributions of properties in disordered networks. The established framework is then applied to real glass-forming systems, specifically phosphates and silicates, where the microscopic structure, ability for atomic rearrangement, and thermodynamic properties are predicted and validated against experimental data. Results reveal that statistical mechanical modeling is an effective, computationally efficient approach to investigate structure-property relationships in disordered networks.
3:20 PM Invited
Effect of Nanoscale Phase Separation on the Fracture Behavior of Glasses: Toward Tough, Yet Transparent Glasses: Jared Rivera1; 1UCLA Department of Civil and Environmental Engineering
Although oxide glasses have many unique properties, their range of applications remains limited by their brittleness. By mimicking the microstructure of composite materials, the presence of controlled nanoscale phase separation in glass could overcome this limitation. However, the nature of the toughening mechanism induced by such nanostructuring remains poorly understood. Here, based on peridynamic simulations, we investigate the effect of nanoscale phase separation on the crack propagation mechanism. We show that phase separation can significantly increase glass's toughness (with up to a 90% increase in the fracture energy for the range of conditions investigated herein). The extent of toughening is found to arise from a balance between the overall cohesion of the phase-separated glass and the propensity for crack deflection. This suggests that controlled nanoscale phase separation is a promising route toward the development of tough, yet optically transparent glasses.
3:50 PM Invited
Decoding Structure-Dynamics Correlations in SiO2 Supercooled Liquid by Machine Learning: Emily Li1; 1Materion Corporation
The dynamics of silicate liquids and supercooled liquids plays a key role in glass manufacturing and geology. However, the relationship between the dynamics of silicate glasses and their atomic structure remains unclear, as intuitive structural metrics are only weakly correlated to dynamics. Because of its ability to discover complex patterns within data, machine learning is considered as a powerful tool to map structure to dynamics by revealing complex structure characteristics. Based on molecular dynamic simulations of silica supercooled liquid, a classification machine learning model was developed to investigate the structure origin. By interpreting the results of the model, we extracted a non-intuitive structural metric (called “softness”) that exhibits a strong correlation with the displacement of the oxygen atoms. This non-intuitive structural fingerprint, based merely on the initial atomic positions, serves as an appropriate structural descriptor to predict silica’s dynamics. More generally, this approach offers a promising route to decipher the relationship between atomic structure and dynamics.