Designing a material with multiple desired properties is a great challenge, especially in a complex material system. Here, we propose a material design strategy to simultaneously optimize multiple targeted properties of multi-component Co-base superalloys via machine learning. The microstructural stability, γ′ solvus temperature, γ′ volume fraction, density, processing window, freezing range, and oxidation resistance were simultaneously optimized. A series of novel Co-base superalloys were successfully selected and experimentally synthesized from >210,000 candidates. The best performer, Co-36Ni-12Al-2Ti-4Ta-1W-2Cr, possesses the highest γ′ solvus temperature of 1266.5 °C without the precipitation of any deleterious phases, a γ′ volume fraction of 74.5% after aging for 1000 h at 1000 °C, a density of 8.68 g cm⁻³ and good high-temperature oxidation resistance at 1000 °C due to the formation of a protective alumina layer. Our approach paves a new way to rapidly design multi-component materials with desired multi-performance functionality.

设计具有多种所需特性的材料是一项巨大的挑战，尤其是在复杂的材料系统中。在这里，我们提出了一种材料设计策略，以通过机器学习同时优化多组分钴基高温合金的多个目标性能。同时优化了微观结构稳定性，γ'固溶温度，γ'体积分数，密度，加工范围，凝固范围和抗氧化性。从超过210,000个候选材料中成功选择了一系列新型的Co基超级合金，并进行了实验合成。性能最佳的Co-36Ni-12Al-2Ti-4Ta-1W-2Cr的γ'固溶温度最高，为1266.5°C，没有任何有害相的沉淀，老化1000 h后的γ'体积分数为74.5％。在1000°C下的密度为8.68 g cm ^-3由于形成了保护性氧化铝层，在1000°C时具有良好的高温抗氧化性。我们的方法为快速设计具有所需多功能功能的多组分材料铺平了道路。