REFERENCES

1. Tan, L.; Yang, X.; Shi, D.; Huang, W.; Lyu, S.; Fan, Y. Effect of microstructure rafting on deformation behaviour and crack mechanism during high-temperature low-cycle fatigue of a Ni-based single crystal superalloy. Int. J. Fatigue. 2025, 190, 108619.

2. Pollock, T. M. Alloy design for aircraft engines. Nat. Mater. 2016, 15, 809-15.

3. Ge, M.; Li, Y.; Wang, X.; et al. Effects of Ta on the high temperature creep behavior and deformation mechanism of a Ni-based single crystal superalloy. Mater. Sci. Eng. A. 2024, 916, 147335.

4. Ru, Y.; Zhang, H.; Pei, Y.; et al. Improved 1200 °C stress rupture property of single crystal superalloys by γ’-forming elements addition. Scr. Mater. 2018, 147, 21-6.

5. Xia, W.; Zhao, X.; Yue, L.; Zhang, Z. Microstructural evolution and creep mechanisms in Ni-based single crystal superalloys: a review. J. Alloys. Compd. 2020, 819, 152954.

6. Zhang, P.; Yuan, Y.; Niu, Q.; et al. Correlation microstructural evolution with creep-rupture properties of a novel directionally solidified Ni-based superalloy M4706. J. Mater. Sci. 2022, 57, 17812-27.

7. Murakumo, T.; Kobayashi, T.; Koizumi, Y.; Harada, H. Creep behaviour of Ni-base single-crystal superalloys with various γ’ volume fraction. Acta. Mater. 2004, 52, 3737-44.

8. Liu, Y.; Niu, C.; Wang, Z.; et al. Machine learning in materials genome initiative: a review. J. Mater. Sci. Technol. 2020, 57, 113-22.

9. Liu, P.; Huang, H.; Antonov, S.; et al. Machine learning assisted design of γ’-strengthened Co-base superalloys with multi-performance optimization. npj. Comput. Mater. 2020, 6, 334.

10. Zhao, Y. Understanding and design of metallic alloys guided by phase-field simulations. npj. Comput. Mater. 2023, 9, 1038.

11. Oommen, V.; Shukla, K.; Goswami, S.; Dingreville, R.; Karniadakis, G. E. Learning two-phase microstructure evolution using neural operators and autoencoder architectures. npj. Comput. Mater. 2022, 8, 876.

12. Yang, K.; Cao, Y.; Zhang, Y.; et al. Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks. Patterns 2021, 2, 100243.

13. Zhu, Y.; Xu, T.; Wei, Q.; et al. Linear-superelastic Ti-Nb nanocomposite alloys with ultralow modulus via high-throughput phase-field design and machine learning. npj. Comput. Mater. 2021, 7, 674.

14. Van Lich, L.; Nguyen, T.; Hong Hue, D. T.; et al. The design of compositionally modulated lead-free ferroelectrics with large electromechanical response via high-throughput phase-field simulations and machine learning. Mater. Res. Bull. 2023, 167, 112433.

15. Shen, Z. H.; Wang, J. J.; Jiang, J. Y.; et al. Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics. Nat. Commun. 2019, 10, 1843.

16. Li, W.; Yang, T.; Liu, C.; et al. Optimizing piezoelectric nanocomposites by high-throughput phase-field simulation and machine learning. Adv. Sci. 2022, 9, e2105550.

17. Tso, W.; Wu, W.; Seidman, D. N.; Heinonen, O. G. Active learning sensitivity analysis of γ’(L12) precipitate morphology of ternary co-based superalloys. Materialia 2023, 28, 101760.

18. Xu, D.; Zhang, Q.; Huo, X.; Wang, Y.; Yang, M. Advances in data-assisted high-throughput computations for material design. Mater. Genome. Eng. Adv. 2023, 1, e11.

19. Karniadakis, G. E.; Kevrekidis, I. G.; Lu, L.; Perdikaris, P.; Wang, S.; Yang, L. Physics-informed machine learning. Nat. Rev. Phys. 2021, 3, 422-40.

20. Chen, L.; Zhao, Y. From classical thermodynamics to phase-field method. Prog. Mater. Sci. 2022, 124, 100868.

21. Qin, Q.; Zhang, Z.; Long, H.; Zhuo, J.; Li, Y. Prediction of creep properties of Co–10Al–9W superalloys with machine learning. J. Mater. Sci. 2024, 59, 4571-85.

22. Zhao, Y. Integrated unified phase-field modeling (UPFM). Mater. Genome. Eng. Adv. 2024, 2, e44.

23. Shan, Y.; Zhuo, J.; Song, J.; Niu, K.; Li, Y. Precipitation kinetics and creep properties of multicomponent Ni-based superalloys. J. Mater. Sci. 2024, 59, 20715-34.

24. Chen, J.; Guo, M.; Yang, M.; Zhang, J. Temperature dependence of kinetics pathway of γ’ precipitation in Co-Al-W superalloys: a phase-field study. J. Alloys. Compd. 2022, 922, 166319.

25. Zhou, N.; Shen, C.; Mills, M.; Wang, Y. Large-scale three-dimensional phase field simulation of γ’-rafting and creep deformation. Philos. Mag. 2010, 90, 405-36.

26. Wang, D.; Li, Y.; Shi, S.; Tong, X.; Yan, Z. Phase-field simulation of γ’ precipitates rafting and creep property of Co-base superalloys. Mater. Design. 2020, 196, 109077.

27. Utada, S.; Despres, L.; Cormier, J. Ultra-high temperature creep of Ni-based SX superalloys at 1250 °C. Metals 2021, 11, 1610.

28. Liu, Y.; Wu, J.; Wang, Z.; et al. Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning. Acta. Mater. 2020, 195, 454-67.

29. Li, X.; Zhang, H.; Li, X.; et al. High-temperature creep behavior and damage mechanism of an advanced powder metallurgy Ni-based superalloy. Adv. Eng. Mater. 2024, 26, 2400230.

30. Omprakash, C. M.; Kumar, A.; Kamaraj, M.; Satyanarayana, D. V. V. Creep behaviour of directionally solidified nickel-base superalloy CM 247: a three-dimensional representation of creep curves. Trans. Indian. Inst. Met. 2021, 74, 1787-97.

31. Ronneberger, O.; Fischer, P.; Brox, T. . U-Net: convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention - MICCAI 2015. Cham: Springer International Publishing; 2015. pp. 234-41.

32. Qin, Z.; Li, W.; Wang, Z.; et al. High-throughput characterization methods for Ni-based superalloys and phase prediction via deep learning. J. Mater. Res. Technol. 2022, 21, 1984-97.

33. Zhou, Z.; Siddiquee, M. M. R.; Tajbakhsh, N.; Liang, J. UNet++: a nested U-Net architecture for medical image segmentation. Deep. Learning. in. Medical. Image. Analysis. and. Multimodal. Learning. for. Clinical. Decision. Support. , pp 3-11.

34. He, K.; Zhang, X.; Ren, S.; Sun, J. . Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA. Jun 27-30, 2016. IEEE, 2016; pp, 770-8.

35. Zhang, N.; Fu, H.; Liu, P.; et al. Machine learning-based quantitative analysis of metal ductile fracture surface. Materialia 2023, 32, 101904.

36. Wang, Y.; Banerjee, D.; Su, C.; Khachaturyan, A. Field kinetic model and computer simulation of precipitation of L12 ordered intermetallics from f.c.c. solid solution. Acta. Mater. 1998, 46, 2983-3001.

37. Kline, D. M.; Berardi, V. L. Revisiting squared-error and cross-entropy functions for training neural network classifiers. Neural. Comput. Appl. 2005, 14, 310-8.

38. Zhu, L.; Luo, Q.; Chen, Q.; et al. Prediction of ultimate tensile strength of Al-Si alloys based on multimodal fusion learning. Mater. Genome. Eng. Adv. 2024, 2, e26.

39. Fu, C.; Chen, Y.; Li, L.; Antonov, S.; Feng, Q. Evaluation of service conditions of high pressure turbine blades made of DS Ni-base superalloy by artificial neural networks. Mater. Today. Commun. 2020, 22, 100838.

40. Underwood, E. E. . The mathematical foundations of quantitative stereology. In: stereology and quantitative metallography. ASTM International, 1972; pp. 3-38.

41. Liu, P.; Huang, H.; Jiang, X.; et al. Evolution analysis of γ’ precipitate coarsening in Co-based superalloys using kinetic theory and machine learning. Acta. Mater. 2022, 235, 118101.

42. Murphy, K. P. . Machine learning: a probabilistic perspective. MIT Press, 2012. https://api.semanticscholar.org/CorpusID:17793133. (accessed 2025-03-26).

43. Kim, Y.; Kim, T.; Ergün, T. The instability of the Pearson correlation coefficient in the presence of coincidental outliers. Financ. Res. Lett. 2015, 13, 243-57.

44. Hao, J.; Ho, T. K. Machine learning made easy: a review of Scikit-learn Package in python programming language. J. Educ. Behav. Stat. 2019, 44, 348-61. https://www.researchgate.net/publication/331257851_Machine_Learning_Made_Easy_A_Review_of_Scikit-learn_Package_in_Python_Programming_Language. (accessed 2025-03-26).

45. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B. 1996, 58, 267-88.

46. Loh, W. Classification and regression trees. WIREs. Data. Min. Knowl. 2011, 1, 14-23.

47. Vijayakumar, V.; Case, M.; Shirinpour, S.; He, B. Quantifying and characterizing tonic thermal pain across subjects from EEG data using random forest models. IEEE. Trans. Biomed. Eng. 2017, 64, 2988-96.

48. Ibraheem, R.; Dechent, P.; dos, R. e. i. s. . G. Path signature-based life prognostics of Li-ion battery using pulse test data. Appl. Energy. 2025, 378, 124820.

49. Zhang, T.; Liu, X. Informatics is fueling new materials discovery. J. Mater. Inf. 2021, 1, 6.

50. Cecen, A.; Fast, T.; Kalidindi, S. R. Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure. Integr. Mater. Manuf. Innov. 2016, 5, 1-15.

51. Wold, S.; Esbensen, K.; Geladi, P. Principal component analysis. Chemom. Intell. Lab. Syst. 1987, 2, 37-52.

52. Xu, J.; Li, L.; Liu, X.; Li, H.; Feng, Q. Quantitative models of high temperature creep microstructure-property correlation of a nickel-based single crystal superalloy with physical and statistical features. J. Mater. Res. Technol. 2022, 19, 2301-13.

53. Sengodan G. Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data. Compos. Part. B. Eng. 2021, 225, 109282.

54. Brough, D. B.; Wheeler, D.; Kalidindi, S. R. Materials knowledge systems in python - a data science framework for accelerated development of hierarchical materials. Integr. Mater. Manuf. Innov. 2017, 6, 36-53.

55. Zhang, Y.; Fang, Y.; Li, L.; et al. Thermal stability prediction of copolymerized polyimides via an interpretable transfer learning model. J. Mater. Inf. 2024, 4, 8.

56. Yu, L.; Zhai, J.; Cao, W.; Ren, J. Prediction of temperature-dependent yield strength of refractory high entropy alloy based on stacking integrated framework. J. Mater. Inf. 2024, 4, 28.

57. Nabarro, F. R. N. Rafting in superalloys. Metall. Mater. Trans. A. 1996, 27, 513-30.

58. Touratier, F.; Andrieu, E.; Poquillon, D.; Viguier, B. Rafting microstructure during creep of the MC2 nickel-based superalloy at very high temperature. Mater. Sci. Eng. A. 2009, 510-511, 244-9.

59. Lu, S.; Luo, Z.; Lu, F.; Li, L.; Feng, Q. Creep performance in a CoNi-based single crystal superalloy with super-high γ’ volume fraction at 760 °C and equivalent high stress. J. Mater. Res. Technol. 2024, 29, 4870-80.

60. Ju, Y.; Long, H.; Qin, Q.; Wang, S.; Shan, Y.; Li, Y. Creep property and rafting kinetics of Co-based monocrystal superalloys with antiphase boundaries of γ’ phase. Mater. Sci. Eng. A. 2023, 880, 145283.

61. Antonov, S.; An, W.; Utada, S.; et al. . Evaluation and comparison of damage accumulation mechanisms during non-isothermal creep of cast Ni-based superalloys. In: Tin S, Hardy M, Clews J, Cormier J, Feng Q, Marcin J, O’brien C, Suzuki A, editors. Superalloys 2020. Cham: Springer International Publishing; 2020. pp. 228-39.

62. Wang, X.; Liu, J.; Jin, T.; et al. Effects of temperature and stress on microstructural evolution during creep deformation of Ru-free and Ru-containing single crystal superalloys. Adv. Eng. Mater. 2015, 17, 1034-44.

63. Coakley, J.; Ma, D.; Frost, M.; et al. Lattice strain evolution and load partitioning during creep of a Ni-based superalloy single crystal with rafted γ’ microstructure. Acta. Mater. 2017, 135, 77-87.

64. Liu, P.; Zhang, Z.; Liu, X.; et al. Study on the mechanism of γ’ phase rafting in a 4th generation nickel-based single crystal superalloy during thermal exposure at high temperatures. J. Alloys. Compd. 2024, 980, 173594.

65. Barrett, P. R.; Hassan, T. A unified constitutive model in simulating creep strains in addition to fatigue responses of Haynes 230. Int. J. Solids. Struct. 2020, 185-6, 394-409.

66. Xu, K.; Wang, G.; Liu, J.; et al. Creep behavior and a deformation mechanism based creep rate model under high temperature and low stress condition for single crystal superalloy DD5. Mater. Sci. Eng. A. 2020, 786, 139414.

67. Mohles, V.; Jiang, Y.; Steinbach, I.; Roslyakova, I.; Bürger, D.; Eggeler, G. Microstructure based model for creep of single crystal superalloys in the high temperature and low stress creep regime. Mater. Sci. Eng. A. 2024, 909, 146780.

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