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About this Symposium
Meeting MS&T22: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Sponsorship ACerS Basic Science Division
ACerS Electronics Division
Organizer(s) Amanda Krause, Carnegie Mellon University
Alp Sehirlioglu, Case Western Reserve University
Daniel Ruscitto, GE Research
Scope Big data techniques are being adopted in materials science to sort and analyze large volumes of disparate data for scientific discovery. This informatics approach is particularly attractive for analyzing micrographs, which traditionally rely on qualitative observations. This symposium focuses on analyzing images or multi-dimensional data with data methods, including computer visualization, advanced analytics, machine learning, and digital image correlation, to identify physical descriptors and higher order relationships. A special emphasis will be on applying these techniques to improve our understanding of structure-property relationships.

Session topics include:
-Data mining and machine learning applied to atomic/mesoscale images and spectroscopic data to identify defects
-Informing processing methods like additive manufacturing
-Transfer learning from experimental data to models
-Correlating mechanical, electrical, and thermal properties with microstructures

Abstracts Due 05/15/2022
Proceedings Plan Undecided
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

Automated Defect Identification for Tristructural Isotropic Fuels
B-3: 3D Computer Vision and Machine Learning for Porosity Analysis in Additive Manufacturing
Combining Limited Image and Tabular Data to Understand Failure Modes in Metals
Computer Vision Applications in Materials Science and Engineering
Establishing PSP Relationships with Microstructure Features Quantified Using Machine Learning
FeCrAl Alloy Design Utilizing Literature, Experiments, High Throughput Characterization, and Machine Learning
Machine Learning Enabled Reproducible Data Analysis for Electron Microscopy
Materials Data Science for Reliability: Data Handling
Multimodal Data of Fatigue Fracture Surfaces for Analysis in a CNN
Neighborhood Maps for Discovery of Novel Materials in Reduced Dimensions Using Machine Learning
Polycrystal Graph Neural Network
Process-Structure-Property Relationships from Variational Autoencoders


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