Fe-based metallic glasses (MGs) are a class of promising soft magnetic materials that have received great attention in transformer industries. However, it is challenging to achieve a balance between saturation magnetization (B_s), glass-forming ability and plasticity due to their contradictory correlations in Fe-based MGs, which severely hinders the development of new Fe-based MGs with advanced performances. Inspired by the significant development in machine learning technology, we herein propose a multi-objective optimization strategy to search for Fe-based MGs with optimal combinations of critical casting size (D_max), B_s, and plasticity. The objective functions are built in combination with neural network models for predicting D_max and B_s, as well as empirical formula for plasticity. The effect of number of hidden layers is investigated and the dropout regularization method employed to improve the prediction performance. Our results show that the predictions of B_s and D_max by using alloy composition as the sole input perform well, as evidenced by their r² values of 0.963 and 0.874, respectively. Multi-objective optimization based on the genetic algorithm is executed to obtain the Pareto front and Pareto-optimal solutions. The Pareto-optimal alloys predicted for the Fe₈₃C₁B_xSi_yP_16-x-y and Fe_xCo_yNi_72-x-yB_19.2Si_4.8Nb₄ systems are in good agreement with those reported in experiments. This work thus showcases potential applications for the design of high-performance Fe-MGs against conflicting objectives.

铁基金属玻璃（MGs）是一类很有前途的软磁材料，在变压器行业受到了极大的关注。然而，由于饱和磁化强度（ B _s ）、玻璃形成能力和可塑性之间的平衡在铁基 MGs 中具有挑战性，这严重阻碍了具有先进性能的新型铁基 MGs 的开发。受机器学习技术重大发展的启发，我们在此提出了一种多目标优化策略，以搜索具有临界铸件尺寸 ( D max ) 、_B s最佳_{组合的铁基 MG}, 和可塑性。目标函数结合用于预测D _max和B _s的神经网络模型以及可塑性的经验公式构建。研究了隐藏层数的影响，并采用了 dropout 正则化方法来提高预测性能。我们的结果表明，使用合金成分作为唯一输入的B _s和D _max的预测表现良好，如它们的r ^{2所证明的那样}值分别为 0.963 和 0.874。执行基于遗传算法的多目标优化，得到Pareto前沿和Pareto最优解。_{Fe 83} C ₁ B _x Si _y P _{16- xy}和Fe _x Co _y Ni _{72 -xy} B _19.2 Si _4.8 Nb ₄系统预测的帕累托最优合金与实验中报道的非常一致。因此，这项工作展示了针对相互冲突的目标设计高性能 Fe-MG 的潜在应用。