Integrating NSGA-II and TOPSIS for Stacking Model Optimization in Pursuit of Halide Double Perovskite Screening.

With the increase in training samples, both the Train-MSE and Cross-validation MSE show a decreasing trend and converge to their minimum values when the training size is approximate to 80%, and the gap between them narrows to near zero, as shown by the MSE_difference curve, confirming the proposed models’ strong generalization ability and high predictive accuracy without overfitting.

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