Identifying chemically similar multiphase nanoprecipitates in compositionally complex non-equilibrium oxides via machine learning

Mao, Keyou S.; Gerczak, Tyler J.; Harp, Jason M.; McKinney, Casey S.; Lach, Timothy G.; Karakoc, Omer; Nelson, Andrew T.; Terrani, Kurt A.; Parish, Chad M.; Edmondson, Philip D.

doi:10.1038/s43246-022-00244-4

Title: Identifying chemically similar multiphase nanoprecipitates in compositionally complex non-equilibrium oxides via machine learning

Journal Article · 19 April 2022 · Communications Materials

DOI: https://doi.org/10.1038/s43246-022-00244-4 · OSTI ID:1863623

; Gerczak, Tyler J.;

; McKinney, Casey S.;

; Karakoc, Omer; Nelson, Andrew T.; Terrani, Kurt A.; Parish, Chad M.; Edmondson, Philip D.

Abstract Characterizing oxide nuclear fuels is difficult due to complex fission products, which result from time-evolving system chemistry and extreme operating environments. Here, we report a machine learning-enhanced approach that accelerates the characterization of spent nuclear fuels and improves the accuracy of identifying nanophase fission products and bubbles. We apply this approach to commercial, high-burnup, irradiated light-water reactor fuels, demonstrating relationships between fission product precipitates and gases. We also gain understanding of the fission versus decay pathways of precipitates across the radius of a fuel pellet. An algorithm is provided for quantifying the chemical segregation of the fission products with respect to the high-burnup structure, which enhances our ability to process large amounts of microscopy data, including approaching the atomistic-scale. This may provide a faster route for achieving physics-based fuel performance modeling.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)

Sponsoring Organization:: USDOE; USDOE Office of Nuclear Energy (NE), Nuclear Fuel Cycle and Supply Chain. Fuel Cycle Research and Development Program; USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1863623

Journal Information:: Communications Materials, Journal Name: Communications Materials Journal Issue: 1 Vol. 3; ISSN 2662-4443

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Title: Identifying chemically similar multiphase nanoprecipitates in compositionally complex non-equilibrium oxides via machine learning

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