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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Organizer(s) Mathieu Bauchy, University of California, Los Angeles
Peter Kroll, University of Texas
Efrain Hernandez, Army Research Laboratory
Scope This symposium will provide a forum to identify current achievements and existing challenges in advanced and accelerated materials discovery and characterization of ceramic and glassy materials enabled by simulation and machine learning approaches. Computational techniques ranging from atomistic simulations to mesoscopic and continuum modeling, and innovative approaches in experimental data analysis for the study of ceramic materials, crystalline or amorphous, of all compositions will be considered. Contributions involving informatics approaches, machine learning, data­mining, and design and optimization for materials discovery are also encouraged.

Topics include, but are not limited to:
• Informatics, machine learning, data­ mining approaches,
• First-principles studies of the structure and properties of ceramic materials,
• Molecular dynamics and Monte Carlo studies,
• Upscaling techniques and mesoscale modeling,
• Continuum modeling of ceramic materials,
• Breakthroughs in computational methods for ceramic materials.

This symposium is sponsored by the ACerS Glass & Optical Materials Division.
Abstracts Due 04/05/2019
Proceedings Plan Definite: At-meeting proceedings

A ReaxFF Approach to Study Phonon-Chemistry Coupling in Ceramic Materials
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
Finite Element Analysis of Heat Transfer through the Laser Scan on Alumina (Al2O3) during Additive Manufacturing Process
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
Leveraging Machine Learning to Predict Microstructural and Macroscopic Properties of Alumina
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
Utilization of Artificial Neural Network to Explore the Compositional Space of Hollandite-structured Materials for Cs Incorporation

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