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

Pei Liu, Haiyou Huang, Stoichko Antonov, Cheng Wen, Dezhen Xue, Houwen Chen, Longfei Li, Qiang Feng, Toshihiro Omori, Yanjing Su

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)


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.

Original languageEnglish
Article number62
Journalnpj Computational Materials
Issue number1
Publication statusPublished - 2020 Dec 1

ASJC Scopus subject areas

  • Modelling and Simulation
  • Materials Science(all)
  • Mechanics of Materials
  • Computer Science Applications


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