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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Development of Boron Oxide Potentials for Computer Simulations of Multi-component Oxide Glasses
Author(s) Lu Deng, Jincheng Du
On-Site Speaker (Planned) Lu Deng
Abstract Scope Molecular dynamics and related atomistic computer simulations are effective ways in studying the structures and structure–property relations of glass materials. However, challenges exist in simulating B2O3–containing glasses due to the lack of reliable empirical potentials. In this talk, a set of partial charge pairwise composition–dependent potentials with boron–related interactions for multi-component oxide glasses is presented. This set of potentials is tested in sodium borate glasses and sodium borosilicate glasses, and it is capable to describe boron coordination change with glass composition in wide composition ranges. Structure features and mechanical properties of the simulated glass structures are calculated and agree well with available experimental data as well as theoretical predicted values. In addition, this potential set shows ability to simulate B2O3-containing multi-component glasses with both industrial and technological interests.


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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