Machine learning assisted design of γ′-strengthened Co-base superalloys with multi-performance optimization

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, gamma ' solvus temperature, gamma ' 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 gamma ' solvus temperature of 1266.5 degrees C without the precipitation of any deleterious phases, a gamma ' volume fraction of 74.5% after aging for 1000 h at 1000 degrees C, a density of 8.68 g cm(-3) and good high-temperature oxidation resistance at 1000 degrees 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.