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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Force-Enhanced Refinement of the Atomic Structure of Silicate Glasses
Author(s) Qi Zhou, Mathieu Bauchy
On-Site Speaker (Planned) Mathieu Bauchy
Abstract Scope Accessing the structure of silicate glasses by atomistic simulations remains challenging. Indeed, traditional molecular dynamics simulations depending on the melt-quench method are limited to very high cooling rate and, hence, tend to overestimate the degree of disorder in glasses. Also, conventional reverse Monte Carlo simulations can yield unrealistic structures since a virtually infinite number of configurations can have the same pair distribution function. Here, to overcome these limitations, we report a new method that combines energy minimization with reverse Monte Carlo. Through studying the example of sodium silicate glasses, we show that this method provides some glass configurations that exhibit higher stability than those produced by melt-quench or reverse Monte Carlo. Also, we discuss how this increased degree of stability manifests itself in the atomic structure (with a focus on the medium-range order). This paves the way toward an increased ability to accurately model the structure of silicate glasses.
Proceedings Inclusion? Definite: At-meeting proceedings

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Atomistic Modeling of Fundamental Deformation Mechanisms in MAX Phases
Development of Boron Oxide Potentials for Computer Simulations of Multi-component Oxide Glasses
Embedding Machine Learning in the Physics of Disordered Solids
Exploring Molecular Dynamics Descriptors to Improve Machine Learning Predictions of Glass Forming Ability
Force-Enhanced Refinement of the Atomic Structure of Silicate Glasses
Genesis of “Free” Carbon in Silicon Oxycarbide Ceramics
Impact of Carbon Morphology on Mechanical Properties of SiCO Ceramics
Machine Learning-aided Development of Empirical Force-fields for Glassy Materials
Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons
Machine Learning Applied to Zeolite Synthesis Enabled by Automatic Literature Data Extraction
Machine Learning to Predict the Elastic Properties of Glasses
Peridynamics Modeling of Impact-induced Crack Patterns in Glass
Physics-Based Machine Learning Models for High Throughput Screening of Novel Scintillator Chemistries
Predicting Nuclear Magnetic Resonance Parameters in Ceramics Using Density Functional Theory
Prediction of Compressive Strength and Modulus of Elasticity of Concrete Using Machine Learning Models
Reactive MD Simulations of Polysiloxanes: Modeling the Polymer-to-Ceramic Route towards Silicon Oxycarbide Ceramics
Role of Multi-state Hydrogen during Mayenite Electride Formation by First-principles Calculation
The Stability, Structure and Properties of the Zeta Phase in the Transition Metal Carbides
The Thermophysical Properties of TcO2
Thermal Conductivity of a Glass Material by First-principles Molecular Dynamics: The Case of GeTe4
Tuning Electronic Properties in II-IV-V2 Semiconductors via Sub-lattice Configurational Disorder

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