Ceramics and Glasses Modeling by Simulations and Machine Learning: On-Demand Poster Presentations
Sponsored by: ACerS Glass & Optical Materials Division
Program Organizers: Mathieu Bauchy, University of California, Los Angeles; Peter Kroll, University of Texas at Arlington; N. M. Anoop Krishnan, Indian Institute of Technology Delhi
Friday 8:00 AM
October 22, 2021
Room: On-Demand Poster Hall
Location: MS&T On Demand
Effect of Polydispersity on the Fracture Properties of Calcium–Silicate–Hydrate Gel: Ashish Yadav1; N.M. Anoop Krishnan1; 1Indian Institute of Technology (IIT), Delhi
The calcium–silicate–hydrate (C–S–H) gel mainly governs the mechanical and durability properties of the concrete. However, the structural features of C–S–H which control its fracture properties are poorly understood. Here, we investigate the effect of grain polydispersity and packing density using the coarse-grained mesoscale simulation. The simulations show good agreement with the experimental fracture energy. We demonstrate that the fracture energy is largely independent of the packing density but mainly affected by the grain polydispersity beyond the initial stable configuration. These results highlight the crucial role of polydispersity in controlling the C–S–H gel's fracture properties.
Deciphering the Viscosity of Glass Materials with Machine Learning: Yu Song1; Mathieu Bauchy1; 1University of California, Los Angeles
Viscosity is one of the most investigated properties of glass materials in the past decades. Despite the large number of studies involved with glass viscosity, the huge design space of glass leaves the conventional experimental and simulation methods deficient to explore the new glass compositions yielding advanced viscosity performance. In this regard, machine learning methods provide promising solutions for mapping the oxide composition of unknown glasses to their viscosities. Here, based on a large glass dataset (>100,000 glasses), we study multiple machine learning models to predicting viscosity as a function of the glass composition and temperature, with a special focus on explaining the data pattern as learned by various machine learning approaches. These models allow us to decipher the influence of individual oxide on viscosity and to determine the range of feasible glass compositions satisfying a specific viscosity.
Development of a Transferable Inter-atomic Potential for Boroaluminosilicate Glasses: Rajesh Kumar1; N M Anoop Krishnan1; 1Indian Institute of Technology Delhi
Boroaluminosilicate glasses are used as a base glass for a variety practical applications ranging from smart phone protective screens to nuclear waste immobilization. Developing a transferable interatomic potential is crucial to study the structure and properties of boroaluminosilicate glasses. The development of such a potential is challenging due to the variable coordination states of boron atom depending on the presence of anions, also known as the boron anomaly. We developed a transferable potential which can be used to simulate the structure aluminoborosilicate and borosilicate glasses. Glass structures simulated exhibit close match with the experimental structure. The densities of a wide range of glasses exhibit excellent match with experimental values. Overall, the inter-atomic potential developed herein will be extremely useful to simulate the structure of a wide range of boroaluminosilicate glasses with applications in nuclear waste immobilization, and bullet-proof glass composites.