Npj Comput. Mater.: 通用材料多目标性能协同优化主动学习方法

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北京科技大学、北京材料基因工程高精尖创新中心的宿彦京教授团队提出了一种基于多元高斯密度分布函数的多目标优化算法（ED-MCI），具有优化效率高、通用性强的特点，并可拓展到更多个相互关联和此消彼长材料目标性能的协同优化设计，在小数据样本条件下，通过比较少的实验迭代次数，制备出了综合性能优异的新材料。本文以具有高承温能力和长蠕变寿命的高温合金为例，用多个小数据集训练机器学习模型，通过模型推荐和实验验证的反馈迭代，利用ED-MCI对高温合金γ′相体积分数、尺寸和形状进行协同优化，制备出具有高γ′相体积分数、尺寸小和立方度比例高的CoNiAlCr基高温合金。ED-MCI算法在数学推导阶段不受材料种类的束缚，具有可扩展性，为材料多目标协同优化提供了通用的新算法。

The γ/γ′ microstructure in CoNiAlCr-based superalloys using triple-objective optimization 

Pei Liu, Haiyou Huang, Cheng Wen, Turab Lookman & Yanjing Su

Optimizing several properties simultaneously based on small data-driven machine learning in complex black-box scenarios can present difficulties and challenges. Here we employ a triple-objective optimization algorithm deduced from probability density functions of multivariate Gaussian distributions to optimize the γ′ volume fraction, size and morphology in CoNiAlCr-based superalloys. The effectiveness of the algorithm is demonstrated by synthesizing alloys with desired γ/γ′ microstructure and optimizing γ′ microstructural parameters. In addition, the method leads to incorporating refractory elements to improve γ/γ′ microstructure in superalloys. After four iterations of experiments guided by the algorithm, we synthesize sixteen alloys of relatively high creep strength from ~120,000 candidates of which three possess high γ′ volume fraction (> 54%), small γ′ size (< 480 nm) and high cuboidal γ′ fraction (> 77%).