fig8

Exploring the Pareto front of strength-conductivity trade-off: interpretable machine learning for property prediction and composition design in high-strength high-conductivity Cu alloys

Figure 8. Performance of the three selected ML models on training and test sets. (A and B) EC; (C and D) UTS. ML: Machine learning; EC: electrical conductivity; UTS: ultimate tensile strength; RMSE: root mean square error; MAPE: mean absolute percentage error; comp.: composition-property; feat.: physical features-property.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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