Lightweight refractory high-entropy alloys (LW-RHEAs) hold significant potential in the fields of aviation, aerospace, and nuclear energy due to their low density, high strength, high hardness, and corrosion resistance. However, the enormous composition space has severely hindered the development of novel LW-RHEAs with excellent comprehensive performance. In this paper, an machine learning (ML)-based alloy design strategy combined with a multi-objective optimization method was proposed and applied for a rational design of Al-Nb-Ti-V-Zr-Cr-Mo-Hf LW-RHEAs. The quantitative relation of “composition-structure-property” was first established by ML modeling. Then, feature analysis reveals that Cr content greater than 12 at.% is a key criterion for alloys with high corrosion resistance. The phase structure, density, melting point, hardness and corrosion resistance of the alloys were screened layer by layer, and finally, three LW-RHEAs with superb hard and corrosion resistance were successfully designed. Key experimental validation indicates that three target alloys have densities around 6.5 g/cm³, and all alloys are disordered bcc_A2 single-phase with the highest hardness of 593 HV and the largest pitting potential of 2.5 V_SCE, which far exceeds all the literature reports. The successful demonstration in this paper clearly demonstrates that the present design strategy driven by the ML technique should be generally applicable to other RHEA systems.

轻质耐火高熵合金 （LW-RHEAs） 因其低密度、高强度、高硬度和耐腐蚀性而在航空、航天和核能领域具有重要潜力。然而，巨大的组成空间严重阻碍了具有优异综合性能的新型 LW-RHEA 的开发。本文提出了一种基于机器学习（ML）的合金设计策略，结合多目标优化方法，并将其应用于Al-Nb-Ti-V-Zr-Cr-Mo-Hf LW-RHEAs的合理设计。“成分-结构-属性”的定量关系首先是通过 ML 建模建立的。然后，特征分析表明，Cr 含量大于 12 at.% 是高耐腐蚀性合金的关键标准。对合金的相结构、密度、熔点、硬度和耐腐蚀性进行逐层筛选，最后成功设计了三种具有极好硬度和耐腐蚀性的LW-RHEAs。关键实验验证表明，三种靶材合金的密度约为 6.5 g/cm³，并且所有合金都bcc_A2单相无序，硬度最高为 593 HV，点蚀电位最大，为 2.5 V_SCE，远超所有文献报道。本文的成功演示清楚地表明，目前由 ML 技术驱动的设计策略应该普遍适用于其他 RHEA 系统。