fig3

Figure 3. Feature engineering, ML surrogate model training and inverse design based on multi-objective optimization. (A) Feature-importance analysis of the processing and composition-related features for each output; (B) SHAP beeswarm plots showing the effects of the selected features on each microstructural output; (C) Prediction performance of the trained surrogate models for the microstructural features; (D) Parallel-coordinates plot of the Pareto-front solutions based on the optimization targets. The red line represents the solution selected by WcTRD; (E) Three-dimensional scatter plot of the Pareto-front solutions. The red star represents the E1 alloy selected by WcTRD. SHAP: SHapley Additive exPlanations; ML: machine learning; WcTRD: W-containing Ti-rich RHEAs designer; LSBoost: least squares boosting; GP: gaussian process; SVM: support vector machine; MAPE: mean absolute percentage error.