Multi-task deep-learning optimization of trade-off properties for superior-performance Fe-based soft magnetic alloys

Abstract

Fe-based amorphous alloys are promising soft magnetic materials for developing next-generation devices with high frequency and efficiency. However, optimization of Fe-based alloys with ultra-high saturation magnetic flux density (B_s), ultra-low coercivity (H_c), and good glass-forming ability is a notorious problem, owing to the vast composition space and complex trade-offs of these properties. Thus, conventional design methods encounter great challenges. Here we develop a generative multi-task deep learning (GMTDL) to achieve simultaneous optimization of compositions and trade-off properties. The GMTDL can sufficiently exploit and share the knowledge of datasets across different tasks, despite the limitation and imbalance of these datasets. Therefore, it exhibits superior performance in prediction of alloys with multiple targeted properties, outperforming previous machine learning-based design strategies. Moreover, the GMTDL can also tailor compositions, providing an efficient way to regulate properties and generate desired candidates for further experimental processing. The validity and reliability of GMTDL are rigorously tested by benchmarking with Fe-based alloys reported very recently. Moreover, some new alloys with ultra-high B_s and ultra-low H_c are predicted. The optimal content windows of key elements and their synergistic effects are also unraveled for practical guidance. Thus, our study establishes an effective and reliable paradigm for simultaneous prediction and optimization of high-performance materials with multiple properties.