Coupling high-throughput experiment and machine learning to optimize elemental composition in nickel-based superalloys

Abstract

Establishing relationship of elemental composition and mechanical property is a tremendous amount of work in superalloys. Here, machine learning coupled with high-throughput experiment is adopted to construct “composition-hardness” model in nickel-based superalloys. The hardness estimated from experiment agrees well with the predicted value. Furthermore, optimal composition of high-hardness superalloys is accurately predicted by simulated annealing algorithm. Subsequently, optimal composition is validated by Thermo-Calc software, further demonstrating the effectiveness of current approach. Here, a design strategy combined with high-throughput experiment and machine learning is proposed, which may be believed for accelerating the design of advanced materials with excellent performance.

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Data Availability

Data will be made available on request.

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Acknowledgments

The authors would like to deeply appreciate the supports from the National Key Research and Development Program of China (2016YFB0700300), the National Natural Science Foundation of China (51871092, and 12072109), and Leading Fundation of National Defense Technology of China (Grant No. 18-163-00-TS-004-117-01).

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Correspondence to Feng Liu or Jia Li.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Wang, Z., Xie, B., Fang, Q. et al. Coupling high-throughput experiment and machine learning to optimize elemental composition in nickel-based superalloys. MRS Communications (2021). https://doi.org/10.1557/s43579-021-00045-9

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Keywords

  • Alloy
  • Machine learning
  • Chemical composition
  • Hardness