Machine-learning design of ductile FeNiCoAlTa alloys with high strength

• 成果类型: Article
• 署名作者: Sohail, Yasir; Zhang, Chongle; Xue, Dezhen; Zhang, Jinyu; Zhang, Dongdong; Gao, Shaohua; Yang, Yang; Fan, Xiaoxuan; Zhang, Hang; Liu, Gang; Sun, Jun; Ma, En
• 署名单位: Xi'an Jiaotong University
• 刊物名称: Nature
• ISSN/ISSBN: 0028-2529
• DOI: 10.1038/s41586-025-09160-2
• 发表日期: 2025-07-03
• 关键词: high-entropy alloy; angle scattering data; precipitation; steel; dislocation

摘要

The pursuit of strong yet ductile alloys has been ongoing for centuries. However, for all alloys developed thus far, including recent high-entropy alloys, those possessing good tensile ductility rarely approach 2-GPa yield strength at room temperature. The few that do are mostly ultra-strong steels1, 2-3; however, their stress-strain curves exhibit plateaus and serrations because their tensile flow suffers from plastic instability (such as L & uuml;ders strains)1, 2, 3-4, and the elongation is pseudo-uniform at best. Here we report that a group of carefully engineered multi-principal-element alloys, with a composition of Fe35Ni29Co21Al12Ta3 designed by means of domain knowledge-informed machine learning, can be processed to reach an unprecedented range of simultaneously high strength and ductility. An example of this synergy delivers 1.8-GPa yield strength combined with 25% truly uniform elongation. We achieved strengthening by pushing microstructural heterogeneities to the extreme through unusually large volume fractions of not only coherent L12 nanoprecipitates but also incoherent B2 microparticles. The latter, being multicomponent with a reduced chemical ordering energy, is a deformable phase that accumulates dislocations inside to help sustain a high strain hardening rate that prolongs uniform elongation.