发表机构
Key Laboratory for Anisotropy and Texture of Materials, Ministry of Education, School of Materials Science and Engineering, Northeastern University; Shenyang Dalu Laser Technology Co. Ltd.; The Department of Mechanical Engineering, National University of Singapore（东北大学材料科学与工程学院各向异性和纹理材料教育部重点实验室; 沈阳大路激光科技有限公司; 新加坡国立大学机械工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用多种机器学习策略，基于实验数据和软磁材料数据库，在无限成分空间中加速设计用于电磁干扰屏蔽的多功能FeCo基合金，实现快速自动发现具有优化屏蔽效能及增强磁电性能的合金。
AI 中文摘要
电磁屏蔽材料在航空航天应用和日常生活中都扮演着关键角色。然而，其设计与制造仍面临持续挑战。机器学习在加速材料开发和成分优化方面展现出巨大潜力。此外，激光增材制造为制造具有定制性能的多组分、多功能电磁屏蔽材料提供了强大的技术支持。在本研究中，基于实验推导和软磁材料数据库，提出了多种机器学习策略，以在几乎无限的成分空间中加速设计用于电磁干扰（EMI）屏蔽的多功能FeCo基合金。这项工作提出了一种新颖的方法，用于快速、自动地发现具有优化EMI屏蔽效能以及增强磁性能和电性能的多功能合金。
英文摘要
Electromagnetic shielding materials play a pivotal role in both aerospace applications and daily life. However, their design and manufacturing still face persistent challenges. Machine learning demonstrates significant potential in accelerating material development and compositions optimization. Furthermore, laser additive manufacturing provides powerful technical support for fabricating multi-component, multifunctional electromagnetic shielding materials with tailored properties. In this study, the multiple machine learning strategies have been proposed, based on experimental derivation and soft magnetic material databases, to accelerate the design of multifunctional FeCo-based alloys for electromagnetic interference (EMI) shielding within an almost infinite compositional space. This work presents a novel approach for the rapid and automated discovery of multifunctional alloys with optimized EMI shielding effectiveness, as well as enhanced magnetic and electrical properties.
