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轻质难熔高熵合金由轻质的Al、Ti以及难熔Nb、Mo、Hf等元素构成，具有较低的合金密度，同时继承了传统MoNbTaWHf基难熔高熵合金高强度、高硬度、耐腐蚀等特性，在高温结构材料领域具有重要的应用价值。但高熵合金成分空间大、元素间相互作用复杂，五元系合金中不同成分的组合就高达数十万种。新型高性能轻质难熔高熵合金的设计难度大。同时，由于加入了轻质的Al、Zr、Ti等化合物易形成元素，合金中会出现Laves等复杂的金属间化合物相，这些硬质相的出现会提升合金的硬度，但却加剧了基体相与第二相之间的点蚀，显著降低合金耐腐蚀性能，导致合金硬度和耐腐蚀性能之间存在着强烈的制约关系。因此，如何突破合金性能的限制，在高维成分空间内快速设计出高综合性能的轻质难熔高熵合金成分是亟需解决的难题。
图1 数据驱动的超硬超耐蚀轻质难熔高熵合金逐层多目标设计流程图
来自中南大学粉末冶金国家重点实验室的张利军教授团队，提出了一种结合特征分析与多目标优化设计方法的机器学习驱动合金设计策略，可用少量已报道的实验数据来训练模型，进而实现轻质难熔高熵合金相结构与力学性能的准确预测。基于该方法，他们建立了AlNbTiVZrCrMoHf系轻质难熔高熵合金“成分-组织-性能”定量化关系模型，通过对合金相结构、密度、熔点、硬度和腐蚀性能进行逐步地预测和筛选，成功突破合金硬度与耐腐蚀性能的制约关系，实现了三种超硬超耐蚀新型合金成分的高效开发设计。
图2 数据驱动携手逐层多目标设计的高性能轻质难熔高熵合金性能与文献数据的对比
此外，该研究还利用机器学习模型可解释性分析技术发现：高的价电子浓度（VEC）和低的混合焓（ΔHmix）有利于获得高硬度的轻质难熔高熵合金。提高Al元素含量会增强固溶强化效果，而增大Ti元素含量会提升抗氯离子点蚀性能。
图3 机器学习模型的可解释性分析
Data-driven design of novel lightweight refractory high-entropy alloys with superb hardness and corrosion resistance
Tianchuang Gao, Jianbao Gao, Shenglan Yang & Lijun Zhang
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, a 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 VSCE, which far exceeds all the literature reports. The successful application in this paper clearly demonstrates that the present design strategy driven by the ML technique should be generally applicable to other RHEA systems.