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Meeting MS&T21: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Open-source Hyper-dimensional Materials Analytics Using Hyperspy
Author(s) Joshua Taillon
On-Site Speaker (Planned) Joshua Taillon
Abstract Scope With modern advances in computer technology, materials characterization techniques such as electron microscopy (EM) are generating vastly increasing amounts of digital experimental data, requiring novel processing strategies and providing challenges for data analysis. Prominent among these challenges is being able to easily and reproducibly develop these new strategies, due to the limitations of existing proprietary software solutions available in the EM community. The open source HyperSpy project address this issue by providing researchers with easy access to data in proprietary formats,reproducible analysis through scripting and "notebook computing", and access to an ever-growing collection of high-quality scientific data processing libraries in the scientific Python ecosystem, including state of the art machine learning strategies. This talk will introduce the HyperSpy project, demonstrate the capabilities of the software, and provide a number of published examples of how HyperSpy has been used for the processing of large multi-dimensional EM imaging and spectroscopy datasets.


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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