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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Leveraging Machine Learning to Predict Microstructural and Macroscopic Properties of Alumina
Author(s) Russell Gleason, Branden Kappes, Geoff Brennecka, Aaron Stebner
On-Site Speaker (Planned) Russell Gleason
Abstract Scope Alumina is one of the most widely used advanced ceramic materials, yet abnormal grain growth during production remains problematic. The difficulty in controlling the microstructural development of alumina leads to the commensurate difficulty in controlling its macroscopic properties. Since Coble, many different approaches have been used to frame the problem, from doping, to surface energy and bulk defect chemistry, and more recently complexions, but a complete understanding is still elusive. This work aims to take a new approach to the subject, namely machine learning. This approach leverages the power of data informatics to search for previously unrecognized phenomenological relationships among powder properties, chemical composition, manufacturing processes, and the resulting microstructural and macroscopic properties of alumina. Using neural networks and regression analysis, the properties of alumina samples produced by widely varying methods are compared to each other in order to extract relationships and elucidate methods of better controlling these properties.
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
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

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