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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
Sponsorship TMS Materials Processing and Manufacturing Division
TMS Extraction and Processing Division
TMS: Computational Materials Science and Engineering Committee
TMS: Materials Characterization Committee
Organizer(s) Shawn P. Coleman, DEVCOM Army Research Laboratory
Tomoko Sano, U.S. Army Research Laboratory
James Hogan, University of Alberta
Srikanth Patala, SASA Institute
Oliver Johnson, Brigham Young University
Francesca M. Tavazza, National Institute of Standards and Technology
Scope This symposium focuses on the development and use of computational and data intensive characterization capabilities used by experimentalists and modelers to investigate materials structure and mechanisms at varying length and time scales. Advancements in computational processing power; instrument and detector capabilities; and multi-scale experimental and modeling techniques are generating increasingly large datasets that have facilitated the discovery of quantitative descriptors that link structure to processing parameters and material properties. For example, experimental techniques such as 3D x-ray and synchrotron tomography; atom probe tomography; multi-modal imaging; and high frame rate imaging are generating enormous characterization datasets used to understand and quantify material structure and behavior. Similarly, atomistic and mesoscale simulations are generating large datasets that provide insights for the genesis and evolution of various microstructural features, and provide links to higher order models and experiments. Throughout the materials community, scientific discoveries using these large characterization datasets are being accelerated through the advancement and automation of analysis techniques such as machine learning and artificial intelligence. As these computational and data intensive characterization approaches advance, there is a call for deeper study to quantify their inherent uncertainty of structural descriptors.

This symposium intends to bring together experimental and theoretical experts in computational and data intensive microstructure characterization from both academia and industry, with a focus on the methods and techniques to effectively manipulate, reconstruct, analyze, and apply these data to develop improved predictive capabilities for multi-scale materials design. Suggested areas of focus for this symposium include:

• Theoretical and computational development of novel structural descriptors to characterize microstructural features (e.g. grain boundary atomic and crystallographic structure, crystallographic texture, distributions of triple junction types), and their application to quantitatively characterize experimental and simulation data, and develop new predictive microstructure-property models.
• Methods and algorithms for collecting, reconstructing, analyzing, and quantifying large experimental microstructural datasets collected from tools such as: atom probe tomography, x-ray computed tomography, or high-speed measurements.
• Methods and algorithms for the detection, analysis, and quantification of microstructural features predicted through atomic and mesoscale simulation data. Validation approaches for computational and theoretical models using structural descriptors and advanced experimental mechanics techniques. Methods to bridge modeling and experiment through computed characterization (e.g. virtual X-ray and electron diffraction and simulated microscopy).
• Application of advanced analysis techniques, such as machine learning and artificial intelligence, to develop multi-scale microstructure descriptors and provide greater insights into materials characterization data.
• Methods for quantifying the uncertainty inherent in manipulation, reconstruction and analysis of large sets of characterization data.

Abstracts Due 07/15/2019
Proceedings Plan Planned: Supplemental Proceedings volume

100 Years of Scherrer Modifications: Demystifying Diffractogram Width Analyses for Nanocrystalline Materials
3D Morphological Characterization of Porous Cu by Vapor Phase Dealloying Zn-Cu Alloys
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
Investigating the Atomistic Nature of Grain Boundary Failure
Investigating the Effect of Solute Segregation to Grain Boundaries in Nanocrystalline Alloys Toward Stability and Strengthening
Investigations of Microstructural Effects on Porosity Evolution
Large-scale Defect Contrast Simulations for Scanning and Transmission Electron Microscopy
Large Scale Microstructure Synthesis Using LEGOMAT: Application to Additive Manufacturing
Machine Learning and Electron Backscatter Diffraction
Machine Learning Approach for On-the-fly Crystal System Classification from Powder X-ray Diffraction Pattern
Machine Learning Approaches to Image Segmentation of Large Materials Science Datasets
Machine Learning Reinforced Crystal Plasticity Modeling of Titanium-Aluminum Alloys under Uncertainty
Methods for the Correction of Epistemic Resolution Error through Data Collection Process Simulations
Microstructural Evolution Along Geodesics
Monte Carlo Studies of EBSPs Spectroscopy
Neural Networks for Real-time Processing of Scanning Transmission Electron Microscopy Data
Parametric Models for Crystallographic Texture: Estimation and Uncertainty Quantification
Predicting Compressive Strength of Consolidated Solids from Features Extracted from SEM Images
Predicting Crack Location Using a Radial Distribution Function as a Unique Descriptor of Pore Networks
Predicting Microstructure-sensitive Fatigue-crack Path in 3D Using a Machine Learning Framework
The Grain Boundary Octonion: Metrics, Paths, and Fundamental Zones
Uncertainty Propagation in a Multiscale CALPHAD-reinforced Elastochemical Phase-field Model
Uncertainty Quantification of Far-field HEDM Measurements
Uncertainty Quantification Techniques Applied to Ductile Damage Predictions in the 3rd Sandia Fracture Challenge
Utilizing Convolutional Neural Networks for Prediction of Process and Material Parameters from Microstructural Images
X-Ray Computed Tomography of 3D Crack Lattices in Advanced Ceramics and their Effect on Mechanical Response

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