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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Extracting Equations for Thermal Conductivity of Fuels Using Symbolic Regression

Author(s)

Abigail Hogue, Benjamin Rhoads, Samrat Choudhury

On-Site Speaker (Planned)

Benjamin Rhoads

Abstract Scope

Rapid modeling of thermal conductivity of nuclear fuels enables high throughput design and optimization, providing safer and more efficient energy sources. However, predicting thermal conductivity of fuels for arbitrary processing conditions and microstructures is still a challenge due to data sparsity and unreported differences in experimental conditions amongst different sources. In this study, we compiled thermal conductivity data as a function of temperature, grain size, and chemistry, and leverage data imputation methods coupled with machine learning tools such as symbolic regression to extract equations for thermal conductivity. Later, machine learning predicted thermal conductivity was verified using available experimental data. These results demonstrate the promising capability of data imputation and symbolic regression to provide physical insight to nuclear fuel systems.

Proceedings Inclusion?

Planned:

Keywords

Nuclear Materials, Machine Learning,

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