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Meeting MS&T24: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Extraction of Local Scalar 3D Microstructural Properties of SOFC Electrodes from 2D Micrographs using Convolutional Neural Networks.
Author(s) William Flaherty Kent, Rochan Bajpai, Rachel Kurchin, William Epting, Harry Abernathy, Paul Salvador
On-Site Speaker (Planned) William Flaherty Kent
Abstract Scope Local microstructural properties control the electrochemical performance of SOFC electrodes. Common methods for obtaining fine-resolution, 3D reconstructions of microstructures are expensive, time-intensive, and limited in their ability to sample significant volumes, such as those relevant to heterogeneities in commercial SOFCs. Stereological methods can predict, albeit with significant uncertainties, average scalar properties from large field-of-view 2D images, but fail to capture heterogeneities relevant to local properties. These limitations are barriers to accurately studying microstructural changes that occur during cell operation, such as nickel coarsening. We discuss an approach to solve these problems using convolutional neural networks (CNNs) trained from 2D images from large volumes of reconstructed 3D microstructures. After training on 2D images labeled with their corresponding 3D scalar microstructural properties, the CNNs can extract from 2D images all relevant local scalar parameters with sufficient accuracy to study heterogeneity and degradation in performance.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advancing AI-Driven Analysis of Synchrotron Data via FAIR Practices, Ontology and Knowledge Graphs
Advancing Sustainable Agriculture through Multiscale Spatiotemporal Data Integration and High-Performance Computing
Aligning Grains in Time-Series Laboratory Diffraction Contrast Tomography (LabDCT) Data for Machine Learning of Microstructure Evolution
Autonomous approaches for determining structure-processing-property relationships in materials
Categorization of Fracture Surfaces using Deep Learning-enabled 2D Image Analysis
Deep Learning Accelerated Lab-Scale X-Ray Computed Tomography of Low-Melting-Point Solder Alloys Used in Heterogeneously Integrated Semiconductor Packages
Enhancing Rietveld Refinement Analyses with Machine Learning Techniques
Extraction of Local Scalar 3D Microstructural Properties of SOFC Electrodes from 2D Micrographs using Convolutional Neural Networks.
Feature Extraction from SEM Images of Fatigue Fracture Surfaces
Foundation models for multimodal data mining with applications in materials science.
Hierarchical Bayesian Models for Automating Structural Materials Characterization
Machine Learning Enhanced Data Analytics for Transmission Electron Microscopy
Synthetic 3D Microstructure Generation of Solid Oxide Cell Electrodes using Denoising Diffusion Models

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