Comparing Machine Learning Models for Strength and Ductility in High-Entropy Alloys

We compare several machine learning (ML) models that predict the yield strength and plasticity of high-entropy alloys (HEAs) for achieving high-accuracy with notably low root mean square errors (RMSE). Our models, developed using a comprehensive database of single-phase body-centered cubic (BCC) HEAs and BCC + B2 HEAs (where B2 is ordered BCC), integrate advanced feature engineering reflecting the current understanding of electronic factors, atomic ordering informed by mixing enthalpy, and the D parameter associated with stacking fault energy in HEAs. This approach enables systematic comparisons of different ML models, providing deep insights into the mechanical properties of BCC and related alloys. By leveraging genetic algorithms for feature selection and meticulous hyperparameter optimization, our ML framework excels in both predictive power and interpretability. The rigorous validation process includes repeated k-fold cross-validation and leave-one-out cross-validation (LOOCV), ensuring robust generalization. Moreover, our use of Shapley Additive Explanations (SHAP) revealed critical predictors such as the testing temperature-to-melting temperature ratio \(\left(\frac{{T}_{test}}{{T}_{melt}}\right)\) $\left(\frac{T_{\mathit{test}}}{T_{\mathit{melt}}}\right)$ , the mixing enthalpy \(\left(\Delta {\text{H}}_{\text{mix}}\right)\) $\left(\mathrm{\Delta },{\text{H}}_{\text{mix}}\right)$ and the D parameter, which play key roles in determining plasticity and yield strength. Our work represents an advancement in designing high-performance HEAs with optimized strength and ductility, offering a powerful tool for predicting mechanical properties and exploring new candidate alloys with exceptional mechanical behavior.