  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.
