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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Predicting Nuclear Magnetic Resonance Parameters in Ceramics Using Density Functional Theory
Author(s) Ryan J. McCarty, Arthur Pyuskulyan, Jingda Zhang, Kieron Burke
On-Site Speaker (Planned) Ryan J. McCarty
Abstract Scope Nuclear magnetic resonance is a useful tool for characterizing the atomic structures and arrangements of ceramic materials. In settings where the resulting observations are difficult to understand, a density functional theory (DFT) calculation using the GIPAW approach can be used as a predictive and interpretive tool. Using a variety of oxide and ionic solids, we have predicted nuclear magnetic properties and compared these to a database of experimental values. We explored both homogenous materials as well as materials containing dopants. During our work, we identified a smaller representative set of materials that may prove ideal for future benchmarking purposes. We will comment on the agreement between predictions and experimental results, as well as best practices and techniques to produce the most accurate predictions.
Proceedings Inclusion? Definite: At-meeting proceedings

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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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