Thermophysical properties of spark plasma sintered UCo: a comparison with machine learning predictions

TL;DR

This study experimentally measures the thermophysical properties of UCo and validates machine learning predictions, supporting the use of AI models in discovering promising uranium compounds for advanced nuclear fuels.

Contribution

It provides the first experimental data on UCo's thermophysical properties and confirms the reliability of machine learning predictions for uranium compounds.

Findings

Machine learning predictions of UCo's thermal conductivity are accurate.

SHAP analysis aligns model decisions with physical trends.

Experimental data fills a gap in UCo thermophysical properties.

Abstract

Uranium dioxide has been widely used as a nuclear fuel in commercial light water reactors due to its high uranium density and chemical stability. However, its relatively low thermal conductivity is not optimal from the viewpoints of fuel integrity and safety margins, particularly during loss-of-coolant accidents. Although the development of accident-tolerant fuels with higher thermal conductivity is strongly desired, many potential uranium compounds remain unexplored due to constraints associated with handling radioactive materials. To efficiently screen promising uranium compounds with high thermal conductivity, past studies have leveraged machine-learning models to accelerate the discovery process. In this study, we experimentally examine the model's predictions by fabricating UCo and measuring its high-temperature thermophysical properties. Our results show that the thermal conductivity of UCo predicted by machine learning is in good agreement with the experimental measurements. Despite slight discrepancies, additional SHAP analysis suggests that the model's decision logic is consistent with known physical trends. Overall, this study fills a gap in reported thermophysical properties of UCo and provides experimental support for machine-learning-assisted screening of uranium compounds relevant to advanced fuel development.