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R. Sainju, W. Y. Chen, S. Schaefer, G. Roberts, M. B. Toloczko, R. J. Kurtz, C. H. Henager, D. J. Edwards, M. Li, Y. Zhu*, Microscopy and Microanalysis, 2021, 27 (S1), 1464-1465.
Highlights:
- Advanced in situ TEM equipped with high-energy ion sources are beginning to produce an ‘avalanche’ of big data, e.g. gigabytes of in situ TEM videos produced in a single irradiation experiment, making it increasingly difficult to extract and quantify the temporal information of radiation defect dynamics
- Our computer vision model DefectSegNet and associated MATLAB algorithms perform irradiation defect quantification in HT-9 in a more reproducible and reliable manner in just a few seconds
- Our new multiple object tracking (MOT) computer vision model tracks and quantifies individual defect clusters in Ni irradiated with 1 MeV Kr ions at ANL IVEM in real-time, advancing our understanding of the dynamic evolution of cascade-induced defect clusters
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R. Sainju, S. Suib, C. Ding, Y. Zhu, Microscopy and Microanalysis, 2021, 27 (S1), 2216-2217.
Highlights:
- Unlike conventional sintering studies that rely mostly on measuring averaged nanoparticle size or the overall surface area, the nature of in situ ETEM offers direct real-time visualization of the nanoparticles’ evolution at the nanoscale in response to the different reactive gaseous environments
- Whether a nanoparticle was sintered or regenerated depends on the intricate interplays among the nanoparticle size, its surrounding nanoparticles, and the reaction conditions
- In-situ ETEM combined with deep learning-based computer vision holds the potential to register and scale up single-particle level analysis that is critical to the understanding of nanocatalyst regeneration
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K. Pazdernik, N. L. LaHaye, C. M. Artman, Y. Zhu, Computational Materials Science, 181, 2020, 109728
Highlights:
- A full understanding of unirradiated LiAlO2 microstructure and how it evolves as a result of neutron irradiation is necessary to produce an integrated performance model to predict in-reactor behavior as well as to target strategic experiments
- We tested a collection of Deep Convolutional Neural Network (DCNN) architectures that have been optimized for image segmentation and selected the best performer to obtain pixel-level classification of the main microstructural features in unirradiated LiAlO2 pellets, including grains, grain boundaries, voids, precipitates, and zirconia impurities
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Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels. Highlighted on the DOE Fusion Energy Sciences (FES) website: AI Helps Scientists Quantify Irradiation Effects
G. Roberts, S. Y. Haile, R. Sainju, D. J. Edwards, B. Hutchinson, Y. Zhu*, Scientific Reports, 9, 2019, 1-12
Highlights:
- We demonstrate the feasibility of automated identification of common crystallographic defects (including extended radiation defects) in HT-9 martensitic steel using deep learning semantic segmentation, based on high-quality microscopy data
- DefectSegNet – a new hybrid CNN architecture with skip connections within and across the encoder and decoder was developed and has proved to be effective at perceptual defect identification with high pixel-wise accuracy
- Deep-learning semantic segmentation established on advanced microscopy and on optimized CNN architecture offers a path forward to the high-throughput defects quantification needed for rational reactor alloy design
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R. Sainju, C. Ophus, M.B. Toloczko, D. J. Edwards, Y Zhu*, Microscopy and Microanalysis, 2019
Highlights:
- A set of MATLAB algorithms were developed for automated identification and quantification of extended irradiation defects like dislocation lines, voids, and precipitates
- An optimized circular-grid intersection method was employed for dislocation density measurement a reduced systematic error
- For quantitative analysis of voids and precipitates, we resolved features overlapping with the application of modified the popular circular Hough transformation