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Meeting MS&T24: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Synthetic 3D Microstructure Generation of Solid Oxide Cell Electrodes using Denoising Diffusion Models
Author(s) Rochan Bajpai, William Kent, William Epting, Harry Abernathy, Paul Salvador, Rachel Kurchin
On-Site Speaker (Planned) Rochan Bajpai
Abstract Scope Detailed representations of 3D microstructures are crucial for materials simulation and design, but high-fidelity experimental measurement is costly. To address this, a denoising diffusion generative model was developed and trained on solid oxide cell anode microstructures acquired through plasma focused ion beam milling and SEM. The model matches, and often exceeds, performance of existing methods such as generative adversarial networks (GAN’s) or DREAM.3D in generating realistic microstructures, as assessed by distributions of functionally relevant properties such as phase fraction, tortuosity, and triple phase boundary density. Diffusion models also offer compelling advantages over GAN’s in data efficiency and stability/ease of training, as well as several intriguing possibilities for extension, building on algorithmic developments in related 2D models. One example that we will explore is conditional generation (e.g. targeting particular properties) using an existing pre-trained model by introducing targeted biases in the sampling process.


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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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