Machine learning-assisted prediction and interpretation of electrochemical corrosion behavior in high-entropy alloys

In this study, machine learning (ML) models were successfully employed to predict the short-term electrochemical corrosion behavior of high-entropy alloys (HEAs) based on their chemical compositions. Considering the vast compositional space of HEAs, which restricts the development of corrosion-resistant HEAs, and the lack of non-destructive methods to qualitatively assess their corrosion resistance, this work represents a significant advancement in the field. The "three-step" method was applied to select the optimal feature set from 38 features, and six ML regression models were trained and compared. The eXtreme Gradient Boosting (XGBoost) and Gradient Boosting Decision Tree (GBDT) algorithms demonstrated the highest predictive accuracy (R2 = 81.02 % and 84.64 %, respectively) among the six algorithms. The model's robust generalization capabilities were confirmed through validation on an additional dataset. Moreover, the interpretability of the model was enhanced by employing two analysis methods, which revealed that pH as an environmental factor, electronegativity difference and average electronegativity as empirical parameters, and the concentrations of Cr and Cu as compositional parameters have the most significant impact on the corrosion resistance of HEAs. The proposed methodology and framework have the potential to optimize alloy composition, facilitating the design and development of new corrosion-resistant HEAs.