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Meeting MS&T21: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Effect of Polydispersity on the Fracture Properties of Calcium–Silicate–Hydrate Gel
Author(s) Ashish Yadav, N.M. Anoop Krishnan
On-Site Speaker (Planned) Ashish Yadav
Abstract Scope 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.
Proceedings Inclusion? Undecided


A Machine-learning Based Hierarchical Framework to Discover Novel Scintillator Chemistries
Bayesian Optimization of Silicon Nitride Empirical Potentials
Ceramics from Polymers –– Results of Ab Initio Molecular Dynamic Simulations
Deciphering the Viscosity of Glass Materials with Machine Learning
Decomposing the Strength of Hydrated Cement Compositions by Machine Learning
Development of a Reactive Force Field (ReaxFF) for Simulation of Polymer-derived Ceramics
Development of a Transferable Inter-atomic Potential for Boroaluminosilicate Glasses
Effect of Polydispersity on the Fracture Properties of Calcium–Silicate–Hydrate Gel
Elucidating Compositional Governance of Optical Properties of Oxide Glasses Using Interpretable Machine Learning
Fusing Experimental and Simulation Datasets in Machine Learning for Predicting Glass Properties
Graph ODE for Learning Dynamic Systems
Impact of Irradiation on the Properties of Gel Layer Formed After Aqueous Corrosion of Borosilicate Glasses
Kinetic Monte Carlo Simulation of Glasses Aided by Machine Learning
Looking for Order in Disorder: Topological Data Analysis of Glass Structure
Machine Learning as a Tool to Accelerate the Design of Nuclear Waste Glasses with Enhanced Sulfur Loadings
Modeling Polaron Hopping in Ternary Spinel Oxides
Now On-Demand Only: Information Extraction Pipeline for Glasses: An NLP Based Approach
P1-3: Molecular Dynamic Characteristic Temperatures for Predicting Metallic Glass Forming Ability
The Energy Landscape Governs Ductility in Disordered Materials
Toward Revealing Full Atomic Picture of Nanoindentation Deformation Mechanisms in Li2O-2SiO2 Glass-ceramics

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