Data-driven methods for diffusivity prediction in nuclear fuels

• Published in: Computational materials science Vol. 230; p. 112442
• Main Authors: Craven, Galen T., Chen, Renai, Cooper, Michael W.D., Matthews, Christopher, Rizk, Jason, Malone, Walter, Johnson, Landon, Gibson, Tammie, Andersson, David A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 25-10-2023; Elsevier
• Subjects: Cluster dynamics; Diffusion; Diffusivity; Machine learning; NUCLEAR FUEL CYCLE AND FUEL MATERIALS; Nuclear fuels; Uranium nitride; Uranium oxide; Nuclear fuels; Cluster dynamics; Machine learning; Uranium nitride; Uranium oxide; Diffusion; Diffusivity
• ISSN: 0927-0256; 1879-0801
• DOI: 10.1016/j.commatsci.2023.112442

The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels—uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. These results suggest there is potential for ML to accelerate the design, qualification, and implementation of nuclear fuels.
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•Data-driven methods have been applied to optimize cluster dynamics models.•Developed machine learning models for diffusion in nuclear fuels.•Machine learning can be used to understand diffusivity in nuclear fuels.