Machine learning-based predictions of yield strength for neutron-irradiated ferritic/martensitic steels

Abstract

Due to their superior resistance to radiation-induced damage, Ferritic/Martensitic (F/M) steels are promising structural materials for advanced nuclear reactors. This study investigates machine learning (ML) methodologies to predict the yield strength of neutron-irradiated F/M steels. Popular ML algorithms, such as Random Forest (RF), Extreme Gradient Boosting (XGBOOST), Gradient Boosting (GBOOST), and Support Vector Regression (SVR), were trained on a experimental dataset to understand the relationship between the input variables (e.g., irradiation dose, irradiation temperature, tensile test condition, heat treatment conditions and steels composition) and the output variable (yield strength). Further, the SHapley Additive exPlanations (SHAP) algorithm was employed to obtain the importance hierarchy of the input variables for their selection. Post-training and testing ML algorithms, their performance was evaluated by assessing their ability to predict the unseen/validation dataset. Among the algorithms tested, XGBOOST demonstrated the highest performance in predicting the validation dataset, followed by RF, GBOOST, and SVR. Synthetic experiments show that the trained ML algorithms can capture the trends between the irradiation input variables and the yield strength. Overall, the trained ML algorithms overcame challenges such as data uncertainties, smaller and sparser datasets, and other complexities and predicted almost 70% to 75% of the datapoints in the unseen/validation dataset within one standard deviation.