Reverse design of nickel-based superalloys based on thermodynamic calculation and machine learning

The combination of thermodynamic calculation and machine learning was used to reverse design nickel-based superalloys for thermodynamic performance requirements. The results show that the thermodynamic calculation dataset of nickel-based superalloys is successfully constructed by high-throughput thermodynamic calculations， which provides the data basis for the reverse design of nickel-based superalloys with thermodynamic performance requirements by using machine learning methods. Several C2P models are established for the thermodynamic target performance， and the accuracy of the models is higher than 99%. MLDS method is used to reverse design the alloy composition， and the eight alloys are recommended to meet the performance requirements （Vγ'，1100 ℃≥60%，Vγ，1100 ℃+Vγ'，1100 ℃≥99% and Tγ′≥1300 ℃）. The experimental verification of the three alloys with the smallest prediction error of thermomechanical properties shows that the Vγ'，1100 ℃ are greater than 80%， Vγ，1100 ℃+Vγ'，1100 ℃≥99% in the microstructure after aging， and Tγ′≥1300 ℃， which meet the design requirements.