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About this Symposium
Meeting MS&T21: 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, General Electric
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 04/15/2021
Proceedings Plan Undecided

Building a Database of Fatigue Fracture Images to train a CNN
Characterization of Additively Manufactured ZrB2-SiC Ultra High Temperature Ceramics via X-ray Microtomography
Graph Neural Networks for an Accurate and Interpretable Prediction of the Properties of Polycrystalline Materials
Machine Learning and Image Processing Techniques for Materials Evaluation
Machine Learning Ferroelectrics: Bayesianity, Parsimony, and Causality
Multivariate Statistical Analysis (MVSA) for Hyperspectral Images
Now On-Demand Only - Computational or Experimental? Interpreting X-ray Absorption and Diffraction Contrast for Massive Non-destructive 3D Grain Mapping of Metals in Laboratory CT
Open-source Hyper-dimensional Materials Analytics Using Hyperspy
Quantitative Comparisons of 2D Microstructures with the Wasserstein Metric
Spatial and Statistical Representation of Strain Localization as a Function of the 3D Microstructure Using Multi-modal and Multi-scale Data Merging
Training Deep-learning Models with 3D Microstructure Images to Predict Location-dependent Mechanical Properties in Additive Manufacturing
Understanding Degradation and Failure Mechanisms by Multiscale and Multiresolution Electron Microscopy

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