Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials

Journal Article · Tue Jul 01 00:00:00 EDT 2025 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2025.113943· OSTI ID:2569566

Oostrom, Marjolein T.; Hagen, Alexander R.; LaHaye, Nicole L.; Pazdernik, Karl TL

Understanding the relationship between the evolution of microstructures of irradiated LiAlO2pellets and tritium diffusion, retention and release could improve predictions of tritium performance. Given expert-labeled segmented images of irradiated and unirradiated pellets, we trained Deep Convolutional Neural Networks to segment images into defect, grain, and boundary classes. Qualitative microstructural information was calculated from these segmented images to facilitate the comparison of unirradiated and irradiated pellets. We tested modifications to improve the sensitivity of the model, including incorporating meta-data into the model and utilizing uncertainty quantification. The predicted segmentation was similar to the expert-labeled segmentation for most methods of microstructural qualification, including pixel proportion, defect area, and defect density. Overall, the high performance metrics for the best models for both irradiated and unirradiated images shows that utilizing neural network models is a viable alternative to expert-labeled images.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 2569566
• Report Number(s): PNNL-SA-199783
• Journal Information: Computational Materials Science, Journal Name: Computational Materials Science Vol. 257
• Country of Publication: United States
• Language: English