Fig. 2: Workflow. | npj Computational Materials

Fig. 2: Workflow.

From: Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty

Fig. 2

Framework of the proposed multi-objective Bayesian active learning with included experimental data uncertainty, for designing super high-strength and high-ductility lead-free solder alloys. a Dataset with experimental uncertainty. b Two developed Gaussian Process Regression (GPR) models, one for strength and one for elongation. c Multi-objective Bayesian sampling based on the acquisition-function-modified objective space. d Iteration results with experimental feedback. e Experimental characterization and mechanism exploration.

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