Predicting the Thermal Conductivity of Structural Materials Under Lead-Bismuth Corrosion Based on Machine Learning

316L stainless steel and T91 heat-resistant steel are key structural materials for lead-cooled fast reactors (LFRs). Lead-bismuth eutectic (LBE) corrosion induces oxide layer formation and remarkably degrades thermal conductivity, endangering reactor safety and efficiency. Systematic experimental studies on and predictive tools for the thermal conductivity of stainless steels after LBE corrosion are currently scarce. To address the lack of experimental data and predictive capabilities regarding changes in thermal conductivity following LBE corrosion, this study experimentally obtained thermal conductivity data from stainless steels after lead-bismuth corrosion and developed machine learning models to predict thermal conductivity under multi-parameter coupled LBE corrosion conditions. Three machine learning models were established using material composition and corrosion parameters as inputs. Overall, the hyperparameter-optimized Gradient Boosting Regression model showed competitive predictive performance with low overall prediction error. The model therefore provides a preliminary data-driven tool for estimating the thermal conductivity of corroded 316L stainless steel and T91 heat-resistant steel, thereby providing technical support for material selection, thermal design, and safety assessment of LFRs, with further specimen-level validation required for broader engineering application.