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Meeting Materials Science & Technology 2020
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Sponsorship ACerS Basic Science Division
ACerS Electronics Division
Organizer(s) Amanda Krause, University of Florida
Kristen H. Brosnan, General Electric Research
Alp Sehirlioglu, Case Western Reserve University
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 05/31/2020
Proceedings Plan Undecided
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

Accelerate TEM and Tomography Imaging by Deep-learning Enabled Compressive Sensing and Information Inpainting in High-dimensional Manifold
Assessment of the Ability of Laboratory Accelerated Corrosion Tests to Accurately Predict On-road Corrosion of 6xxx Al Alloys
Automated Optical Microscopy for Rapid Defect Screening
Computer Vision and Machine Learning for Microstructural Image Data
Developing Granular Dielectrics Based on Reconstructed Micro-CT Images
FAIR Digital Object Framework and High Throughput Experiment
Feature Characterization of Electron Backscatter Patterns from Rotating Lattice Single Crystals Using Machine Learning
Identifying Crack Initiation Sites with CNNs
Incorporating Materials Physics into Imaging Algorithms for Microscope Image Interpretation
Introductory Comments: Materials Informatics for Images and Multi-dimensional Datasets
Keyhole Porosity Threshold in Laser Melting Revealed by High-Speed X-ray Imaging
Microstructure Representation for Physically Meaningful Descriptors
Neural Networks and Community Driven Software for Scanning Transmission Electron Microscopy
Towards Smart Categorization of Growth Morphology by Machine Learning


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