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Meeting MS&T21: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Development of a Transferable Inter-atomic Potential for Boroaluminosilicate Glasses
Author(s) Rajesh Kumar, N M Anoop Krishnan
On-Site Speaker (Planned) Rajesh Kumar
Abstract Scope 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.
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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