In this study, based on the saturation magnetic flux density experimental values (Bs) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict Bs using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R2 score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict Bs above the threshold value of Bs (1.4 T). After obtaining two ANN models optimized for the R2 and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

The Zr-based bulk metallic glass matrix composites of a mixture of gas-atomized metallic glass powders and Fe-based nanostructured powders were fabricated by spark plasma sintering. The Fe-based nanostructured powders adopted for the enhancement of plasticity were well distributed in the matrix after consolidation, and the matrix remains as a fully amorphous phase. The successful consolidation of metallic glass matrix composite with high density was attributed to viscous flow in the supercooled liquid state during spark plasma sintering. Unlike other amorphous matrix composites, in which improved ductility could be obtained at the expense of their strength, the developed composite exhibited improvement both in strength and ductility. The ductility improvement in the composite was considered to be due to the formation of multiple shear bands under the presence of the Fe-based nanostructured particles.