Conference Tools for 2020 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2020 TMS Annual Meeting & Exhibition
Symposium	Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
Presentation Title	Characterizing the Energetics and Structural Configurations of Silicon Carbide Grain Boundaries Using High-throughput Atomistic Techniques
Author(s)	Matthew Guziewski, Dennis Trujillo, Srikanth Patala, Shawn P Coleman
On-Site Speaker (Planned)	Matthew Guziewski
Abstract Scope	High throughput atomistic models are used to probe the uncertainty in the energetics and structure of silicon carbide at grain boundaries. In this work, a Metropolis style Monte Carlo algorithm is applied to a variety of tilt, twist and mixed character grain boundaries. This approach seeks to capture the variance in structure and energy beyond simply the five macroscopic degrees of freedom that describe the boundary geometry, though the consideration of factors such as metastable states, non-stoichiometric interfaces, and chemical environment near the interface. This allows for the analysis of not just pristine interfaces, but also interfaces that more closely resemble those which are seen experimentally. The observed distributions of energy are used to construct simple relations that relate the energetics to both misorientation/disorientation and to the expected metastable states, and allow for the construction of higher fidelity mesoscale models.
Proceedings Inclusion?	Planned: Supplemental Proceedings volume

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100 Years of Scherrer Modifications: Demystifying Diffractogram Width Analyses for Nanocrystalline Materials

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A New Crystallographic Defect Quantification Workflow via Advanced-microscopy-based Deep Learning

Advancement of Data Intensive Approaches in Materials Discovery and Design

Adversarial Networks for Microstructure Generation and Modeling Phase Transformation Kinetics

Application of Machine Learning to Microstructure Quantification and Understanding

Artificial Intelligence Approaches to Microstructural Science

Automated Anomaly Detection in Unlabeled Computed Tomography Images

Basis Functions for Quantifying Grain Boundary Texture in Polycrystalline Microstructures

Characterizing GB Atomic Structures at Multiple Scales

Characterizing the Energetics and Structural Configurations of Silicon Carbide Grain Boundaries Using High-throughput Atomistic Techniques

Deep Convolutional Networks for Image Reconstruction from 3D Coherent X-ray Diffraction Imaging Data

Determination of Representative Volume Elements for Small Cracks in Heterogeneous Domains via Convolutional Neural Networks

Feature Engineering of Material Structure for Extracting Process-structure-property Linkages

GB Property Localization: Inference and Uncertainty Quantification of Grain Boundary Structure-property Models

Higher Order Spectral Terms in Grain Boundary Networks

Indexing of Electron Back-Scatter Diffraction Patterns Using a Convolutional Neural Network

Integrated Structural Methods Addressing Aviation Challenges in Composites

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Investigations of Microstructural Effects on Porosity Evolution

Large-scale Defect Contrast Simulations for Scanning and Transmission Electron Microscopy

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