Machine learning-assisted composition design of W-free Co-based superalloys with high á-solvus temperature and low density

Developing materials with multiple desired characteristics is a tremendous challenge, particularly in an elaborate material system. Herein, a machine learning assisted material design strategy was applied to simultaneously optimize dual target attributes by considering gamma' solvus temperature and alloy density of multi-component Cobased superalloys. To verify the soundness of our strategy, four alloys were selected and experimentally synthesized from >510,000 candidates, each of them possessing gamma' solvus temperature exceeding 1200 C-degrees and alloy density below 8.3 g/cm(3). Of those, Co-35Ni-12Al-5Ti-3V-3Cr-2Ta-2Mo (at.%) possesses the highest gamma' solvus temperature of 1250 C-degrees and lower density of 8.2 g/cm(3). This article validates a straightforward strategy to guide rapid discovery and fabrication of multi-component materials with desired dual-performance characteristics.