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Meeting MS&T22: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Molecular Dynamics Simulation of Tellurite Glasses
Author(s) Amreen Jan, N M Anoop Krishnan
On-Site Speaker (Planned) Amreen Jan
Abstract Scope Tellurium oxide-based glasses are among the most promising candidates for integration in non-linear optical devices. However, these glasses are known to be prone to fast devitrification and hence, it is necessary to understand their formation and properties to be able to take full advantage of their peculiarity. Though a lot of experimental work has been carried out in the field of tellurite glasses but these glasses have not been explored much using atomistic modelling. In this work Tellurium oxide glass has been simulated, using the interatomic potentials based on the framework of Born model of ionic solids and further, core shell model. This work explores the effect of system size, quenching rate, and ensemble (NPT and NVT) in terms of connectivity, short-range and medium range order. The glass is estimated to be sensitive to the quenching rate and ensemble choice but not much to the system size.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Physics Informed Machine Learning Approach to Predict Glass Forming Ability
D-7: Development of Structural Descriptors to Predict Dissolution Rate of Volcanic Glasses: Molecular Dynamic Simulations
D-8: Molecular Dynamic Simulations of Polymer Derived Ceramics
Data-driven Prediction of Room Temperature Density of Multicomponent Silicate-based Glasses
Data Driven Design and Enhancement of Machinable Glass Ceramics
Developing ReaxFF for Simulation of Silicon Carbonitride Polymer-derived Ceramics
In-Silico Simulations of Polymer Pyrolysis
Machine Learning-Derived Atomistic Potentials for Y2Si2O7 and Yb2Si2O7
Machine Learning Defect Properties of Semiconductors
Machine Learning to Design and Discover Sustainable Cementitious Binders: Learning from Small Databases and Developing Closed-form Analytical Models
Molecular Dynamics Simulation of Tellurite Glasses
Molecular Dynamics Study of Domain Switching Dynamics in KNbO3 and BaTiO3
Natural Language Processing Aided Understanding of Material Science Literature
Pore-resolved Simulations of Chemical Vapor Infiltration in 3D Printed Preforms and the Kinetic Regimes
Predicting and Accessing Metastable Phases
Predicting the Dynamics of Atoms in Glass-Forming Liquids by a Surrogate Machine-Learned Simulator
Quantifying the Local Structure of Metallic Glass as a Function of Composition and Atomic Size
Using Machine Learning Empirical Potentials to Investigate Interdiffusion at Metal-Chalcogenide Alloy Interfaces
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