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Meeting
MS&T22: Materials Science & Technology
Symposium
Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title
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On-Site Speaker (Planned)
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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 Y
2
Si
2
O
7
and Yb
2
Si
2
O
7
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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