A Data-driven machine learning model for radiation-induced DBTT shifts in RAFM steels

• Built a stacking model (XGBoost, RF, GBDT, MLP) for DBTT prediction (R² = 0.96). • Identified dose, temperature, Ta, W, and Cr as key factors for DBTT evolution. • DBTT peaks from 150 to 300°C and >30 dpa, then drops above 350°C due to recovery. • Ta and W reduce DBTT by stabilising precipitates and limiting defect growth. • SHAP shows that W lowers DBTT via solid solution strengthening at 6–9% Cr and 0–0.04% Ta. This study develops a stacking ensemble machine learning model to predict ductile-to-brittle transition temperature (DBTT) in irradiated Reduced-Activation Ferritic-Martensitic (RAFM) steels. Using a dataset of 490 irradiation cases, the model integrates XGBoost, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Multi-Layer Perceptron (MLP), achieving a high predictive accuracy (R² = 0.96) and outperforming individual models. The results highlight the significant influence of irradiation dose and temperature on DBTT Beyond 30 dpa, defect accumulation causes a sharp DBTT increase, while irradiation temperature exhibits a nonlinear effect, peaking at 150-300°C due to radiation-enhanced precipitation and declining above 350°C as defect recovery improves ductility. Additionally, alloying elements play a crucial role: Ta and W help mitigate embrittlement, moderate Cr (4–6 wt.%) increases DBTT, and higher Cr levels (>6 wt.%) reduce it at elevated temperatures. SHAP analysis reveals that W is particularly effective in reducing embrittlement in alloy with moderate Cr (6–9 wt.%) and low Ta, while higher Cr concentrations (>6 wt.%) help stabilise DBTT at elevated temperatures. To enable practical alloy design, a genetic algorithm was combined with the model to optimise steel compositions under defined irradiation conditions (200–350 °C, 10 dpa). The approach successfully identified candidate alloys with predicted DBTT values below 50 °C. This study provides a robust predictive framework for understanding and optimising DBTT in irradiated RAFM steels, offering valuable insights into their performance in next-generation nuclear reactors.