fig6

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 6. Performance of 25 different ML models for physical features as input. (A) EC; (B) UTS. ML: Machine learning; EC: electrical conductivity; UTS: ultimate tensile strength; SVM: support vector machine; GPR: Gaussian process regression; RMSE: root mean square error.

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