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Meeting MS&T23: 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 05/08/2023

Efficient Void Shape Optimization Using Deep Generative Convolutional Neural Networks
Informing Autonomous Processing via STEM-EELS Using Variational Autoencoders for Classification and Decision
Machine Learning Segmentation Methods for Fatigue Fracture Surface Defect Analyses
Microstructure Statistics for Property Prediction in Multifunctional Electrode Composites Using Random Forests
Multi-modal Image Registration for Materials Characterization
Nanoscale Metrology of Materials Studied by Advanced Electron Microscopy Imaging and Spectroscopy.
Out-of-Domain Prediction of Material Property Using Deep Learning
Phase Segmentation of Steel Microstructures via Semi Supervised Deep Learning
Predicting the Occurrence and Mechanism of Liquid Metal Embrittlement Using Machine Learning
Rapid Grain Segmentation From Grayscale Micrograph Through Computer Vision Method
Semi-automated Hierarchical Clustering Model for 4D-STEM Datasets
Structure-property Relationships Derived From Electron Microscope to Atomistic Simulations
The Conundrum of Ambiguous Feature Sets in Materials Informatics for Images
Topic Modelling Framework for Rapid Digestion of Additive Manufacturing Literature
Using Computer Vision to Cluster Fatigue Life Based on Small Crack Characteristics

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