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About this Symposium
Meeting 2021 TMS Annual Meeting & Exhibition
Symposium Data Science and Analytics for Materials Imaging and Quantification
Sponsorship TMS Structural Materials Division
TMS Materials Processing and Manufacturing Division
TMS: Advanced Characterization, Testing, and Simulation Committee
TMS: Integrated Computational Materials Engineering Committee
Organizer(s) Emine Begum Gulsoy, Northwestern University
Charudatta Phatak, Argonne National Laboratory
Stephan Wagner-Conrad, Carl Zeiss Microscopy
Marcus D. Hanwell, Brookhaven National Laboratory
David J. Rowenhorst, Naval Research Laboratory
Tiberiu Stan, Northwestern University
Scope Materials imaging and the analysis of the data play a central role in materials characterization. The combination provides a way to `see' a material and quantify its complexities leading to an understanding of its behavior under various conditions. Combining experiments with complementary techniques such as analytical spectroscopy allows one to gain a deeper insight into the relevant physical phenomena. Materials imaging has reached a critical mass of data generation partially due to faster and larger detectors, as well as advanced microscopes and state-of-the-art light source facilities. Modern mathematics and computer science tools are enabling the automation of data integration and analysis; as well as opening new possibilities for extraction of quantitative metrics from materials imaging.

This symposium solicits abstract submissions from researchers who are advancing the field of materials imaging using novel techniques and developing new methods that leverage high performance computational methods for analysis. Image simulation, uncertainty quantification, and imaging data curation are equally of interest. Session topics include, but are not limited to:
- Advances in materials imaging techniques, including in-operando conditions
- Fast imaging in support of high-throughput experimentation
- Automating experimentation: machine learning algorithms for image acquisition and instrument control
- Workflows for automated data curation of microscopy data
- Advances in infrastructure for materials imaging and microscopic data
- Advances in simulations for materials imaging
- Approaches for data mining, machine learning, image processing, and extracting useful insights from large imaging data sets of numerical and experimental results and reuse of microscopic data

Abstracts Due 07/20/2020
Proceedings Plan Planned:
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

Advancements in EBSD Using Machine Learning
Computer Vision and Machine Learning for Microstructural Characterization and Analysis
Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys
Deep Neural Network Facilitated Complex Imaging of Phase Domains
Dictionary Indexing of EBSD Patterns Assisted by Convolutional Neural Network
High Dimensional Analysis of Abnormal Grain Growth under Dynamic Annealing Conditions
Improved EBSD Indexing through Non-Local Pattern Averaging
Materials Characterization in 3D Using High Energy X-ray Diffraction Microscopy: Irradiated and Deformed Materials
Microstructure Image Segmentation with Deep Learning: from Supervised to Unsupervised Methods
Quantitative EBSD Image Analysis and Prediction via Deep Learning
Quantitative X-ray Fluorescence Nanotomography
Resolving Pseudosymmetry in Tetragonal ZrO2 Using EBSD with a Modified Dictionary Indexing Approach
Understanding Powder Morphology and Its Effect on Flowability Through Machine Learning in Additive Manufacturing
Understanding the Keyhole Dynamics in Laser Processing Using Time-resolved X-ray Imaging Coupled With Computer Vision and Data Analytics


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