REFERENCES
1. Feng, Q.; Nandy, T.; Tin, S.; Pollock, T. Solidification of high-refractory ruthenium-containing superalloys. Acta. Mater. 2003, 51, 269-84.
2. Suzuki, A.; Inui, H.; Pollock, T. M. L12-Strengthened cobalt-base superalloys. Annu. Rev. Mater. Res. 2015, 45, 345-68.
3. Sato, J.; Omori, T.; Oikawa, K.; Ohnuma, I.; Kainuma, R.; Ishida, K. Cobalt-base high-temperature alloys. Science 2006, 312, 90-1.
4. Pollock, T. M.; Dibbern, J.; Tsunekane, M.; Zhu, J.; Suzuki, A. New Co-based γ-γ′ high-temperature alloys. JOM 2010, 62, 58-63.
5. Makineni, S. K.; Singh, M. P.; Chattopadhyay, K. Low-density, high-temperature Co base superalloys. Annu. Rev. Mater. Res. 2021, 51, 187-208.
6. Suzuki, A.; Denolf, G. C.; Pollock, T. M. Flow stress anomalies in γ/γ′ two-phase Co–Al–W-base alloys. Scr. Mater. 2007, 56, 385-8.
7. Hart, G. L. W.; Mueller, T.; Toher, C.; Curtarolo, S. Machine learning for alloys. Nat. Rev. Mater. 2021, 6, 730-55.
8. Jordan, M. I.; Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 2015, 349, 255-60.
9. Curtarolo, S.; Hart, G. L.; Nardelli, M. B.; Mingo, N.; Sanvito, S.; Levy, O. The high-throughput highway to computational materials design. Nat. Mater. 2013, 12, 191-201.
10. Kalidindi, S. R.; Medford, A. J.; Mcdowell, D. L. Vision for data and informatics in the future materials innovation ecosystem. JOM 2016, 68, 2126-37.
11. Probert, M. Electronic Structure: Basic Theory and Practical Methods, by Richard M. Martin: Scope: graduate level textbook. Level: theoretical materials scientists/condensed matter physicists/computational chemists. Contemp. Phys. 2011, 52, 77.
12. Agrawal, A.; Choudhary, A. Perspective: Materials informatics and big data: realization of the “fourth paradigm” of science in materials science. APL. Mater. 2016, 4, 053208.
13. Zhang, H.; Fu, H.; Zhu, S.; Yong, W.; Xie, J. Machine learning assisted composition effective design for precipitation strengthened copper alloys. Acta. Mater. 2021, 215, 117118.
14. Tabor, D. P.; Roch, L. M.; Saikin, S. K.; et al. Accelerating the discovery of materials for clean energy in the era of smart automation. Nat. Rev. Mater. 2018, 3, 5-20.
15. Chen, G.; Shen, Z.; Iyer, A.; et al. Machine-learning-assisted de novo design of organic molecules and polymers: opportunities and challenges. Polymers 2020, 12, 163.
16. Zhou, T.; Song, Z.; Sundmacher, K. Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering 2019, 5, 1017-26.
17. Decost, B. L.; Francis, T.; Holm, E. A. Exploring the microstructure manifold: image texture representations applied to ultrahigh carbon steel microstructures. Acta. Mater. 2017, 133, 30-40.
18. Zou, C.; Li, J.; Wang, W. Y.; et al. Integrating data mining and machine learning to discover high-strength ductile titanium alloys. Acta. Mater. 2021, 202, 211-21.
19. Xue, D.; Balachandran, P. V.; Hogden, J.; Theiler, J.; Xue, D.; Lookman, T. Accelerated search for materials with targeted properties by adaptive design. Nat. Commun. 2016, 7, 11241.
20. Xue, D.; Xue, D.; Yuan, R.; et al. An informatics approach to transformation temperatures of NiTi-based shape memory alloys. Acta. Mater. 2017, 125, 532-41.
21. Gopakumar, A. M.; Balachandran, P. V.; Xue, D.; Gubernatis, J. E.; Lookman, T. Multi-objective optimization for materials discovery via adaptive design. Sci. Rep. 2018, 8, 3738.
22. Menou, E.; Ramstein, G.; Bertrand, E.; Tancret, F. Multi-objective constrained design of nickel-base superalloys using data mining- and thermodynamics-driven genetic algorithms. Modelling. Simul. Mater. Sci. Eng. 2016, 24, 055001.
23. Tancret, F. Computational thermodynamics, Gaussian processes and genetic algorithms: combined tools to design new alloys. Modelling. Simul. Mater. Sci. Eng. 2013, 21, 045013.
24. Hu, X.; Wang, J.; Wang, Y.; et al. Two-way design of alloys for advanced ultra supercritical plants based on machine learning. Comput. Mater. Sci. 2018, 155, 331-9.
25. Chandran, M.; Lee, S. C.; Shim, J. Machine learning assisted first-principles calculation of multicomponent solid solutions: estimation of interface energy in Ni-based superalloys. Modelling. Simul. Mater. Sci. Eng. 2018, 26, 025010.
26. Liu, X.; Zhang, F.; Hou, Z.; et al. Self-supervised learning: generative or contrastive. IEEE. Trans. Knowl. Data. Eng. 2023, 35, 857-76.
27. Huang, L.; Zhang, C.; Zhang, H. Self-adaptive training: bridging supervised and self-supervised learning. IEEE. Trans. Pattern. Anal. Mach. Intell. 2024, 46, 1362-77.
28. Bröker, F.; Holt, L. L.; Roads, B. D.; Dayan, P.; Love, B. C. Demystifying unsupervised learning: how it helps and hurts. Trends. Cogn. Sci. 2024, 28, 974-86.
29. Pagan, D. C.; Phan, T. Q.; Weaver, J. S.; Benson, A. R.; Beaudoin, A. J. Unsupervised learning of dislocation motion. Acta. Mater. 2019, 181, 510-8.
32. van Buuren, S.; Groothuis-Oudshoorn, K. mice: Multivariate imputation by chained equations in R. J. Stat. Softw. 2011, 45, 1-67.
33. Hu, M.; Tan, Q.; Knibbe, R.; et al. Recent applications of machine learning in alloy design: a review. Mater. Sci. Eng. R. Rep. 2023, 155, 100746.
34. Benarioua, M.; Amroune, S.; Alshahrani, H.; et al. Statistical normalization of mechanical properties of natural date palm biofibers using the Box-Cox transformation. J. Nat. Fibers. 2025, 22, 2544176.
35. Probst, P.; Boulesteix, A. L.; Bischl, B. Tunability: importance of hyperparameters of machine learning algorithms. J. Mach. Learn. Res. 2019, 20, 1934-65.
36. Bergstra, J.; Bengio, Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 2012, 13, 281-305.
37. Zhang, C.; Bengio, S.; Hardt, M.; Recht, B.; Vinyals, O. Understanding deep learning (still) requires rethinking generalization. Commun. ACM. 2021, 64, 107-15.
38. Khoshvaght, H.; Permala, R. R.; Razmjou, A.; Khiadani, M. A critical review on selecting performance evaluation metrics for supervised machine learning models in wastewater quality prediction. J. Environ. Chem. Eng. 2025, 13, 119675.
39. Lundberg, S. M.; Lee, S. I. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA. Curran Associates Inc.; 2017. pp. 4768-77.
40. Zhang, H.; Fu, H.; He, X.; et al. Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening. Acta. Mater. 2020, 200, 803-10.
41. Wen, C.; Zhang, Y.; Wang, C.; et al. Machine learning assisted design of high entropy alloys with desired property. Acta. Mater. 2019, 170, 109-17.
42. Xiong, J.; Zhang, T.; Shi, S. Machine learning of mechanical properties of steels. Sci. China. Technol. Sci. 2020, 63, 1247-55.
43. Guan, Z.; Tian, H.; Li, N.; Long, J.; Zhang, W.; Du, Y. High-accuracy reliability evaluation for the WC–Co-based cemented carbides assisted by machine learning. Ceram. Int. 2023, 49, 613-24.
44. Sun, L.; Cao, B.; Ma, Q.; et al. Machine learning-assisted composition design of W-free Co-based superalloys with high γ′-solvus temperature and low density. J. Mater. Res. Technol. 2024, 29, 656-67.
45. Wang, C.; Chen, X.; Chen, Y.; et al. Accelerated design of high γ′ solvus temperature and yield strength cobalt-based superalloy based on machine learning and phase diagram. Front. Mater. 2022, 9, 882955.
46. Chen, Y.; Wang, C.; Ruan, J.; et al. Development of low-density γ/γ′ Co–Al–Ta-based superalloys with high solvus temperature. Acta. Mater. 2020, 188, 652-64.
47. Chung, D.; Toinin, J. P.; Lass, E. A.; Seidman, D. N.; Dunand, D. C. Effects of Cr on the properties of multicomponent cobalt-based superalloys with ultra high γ’ volume fraction. J. Alloys. Compd. 2020, 832, 154790.
48. Li, W.; Li, L.; Wei, C.; Zhao, J.; Feng, Q. Effects of Ni, Cr and W on the microstructural stability of multicomponent CoNi-base superalloys studied using CALPHAD and diffusion-multiple approaches. J. Mater. Sci. Technol. 2021, 80, 139-49.
49. Llewelyn, S.; Christofidou, K.; Araullo-Peters, V.; et al. The effect of Ni:Co ratio on the elemental phase partitioning in γ-γ′ Ni-Co-Al-Ti-Cr alloys. Acta. Mater. 2017, 131, 296-304.
50. Yu, J.; Guo, S.; Chen, Y.; et al. A two-stage predicting model for γ′ solvus temperature of L12-strengthened Co-base superalloys based on machine learning. Intermetallics 2019, 110, 106466.
51. 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.
52. Li, W.; Li, L.; Antonov, S.; Feng, Q. Effective design of a Co-Ni-Al-W-Ta-Ti alloy with high γ′ solvus temperature and microstructural stability using combined CALPHAD and experimental approaches. Mater. Design. 2019, 180, 107912.
53. Titus, M. S.; Suzuki, A.; Pollock, T. M. Creep and directional coarsening in single crystals of new γ–γ′ cobalt-base alloys. Scr. Mater. 2012, 66, 574-7.
54. Xue, C.; Yu, H.; Liu, C.; Peng, T.; Ji, P.; Fang, W. Effect of Co content on precipitation behavior of γ′ phase strengthened CoNi-based alloys. Mater. Today. Commun. 2024, 40, 109877.
55. Fan, Z.; Wang, X.; Yang, Y.; Chen, H.; Yang, Z.; Zhang, C. Plastic deformation behaviors and mechanical properties of advanced single crystalline CoNi-base superalloys. Mater. Sci. Eng. A. 2019, 748, 267-74.
56. Yu, J.; Wang, C.; Chen, Y.; Wang, C.; Liu, X. Accelerated design of L12-strengthened Co-base superalloys based on machine learning of experimental data. Mater. Design. 2020, 195, 108996.
57. Liu, X.; Cai, W.; Chen, Z.; et al. Effects of alloying additions on the microstructure, lattice misfit, and solvus temperature of a novel Co–Ni-based superalloy. Intermetallics 2022, 141, 107431.
58. Cao, B.; Kong, H.; Ding, Z.; et al. A novel L12-strengthened multicomponent Co-rich high-entropy alloy with both high γ′-solvus temperature and superior high-temperature strength. Scr. Mater. 2021, 199, 113826.
59. Ohl, B.; Dunand, D. C. Effects of Ni and Cr additions on γ + γ’ microstructure and mechanical properties of W-free Co–Al–V–Nb–Ta-based superalloys. Mater. Sci. Eng. A. 2022, 849, 143401.
60. Pandey, P.; Mukhopadhyay, S.; Srivastava, C.; Makineni, S. K.; Chattopadhyay, K. Development of new γ′-strengthened Co-based superalloys with low mass density, high solvus temperature and high temperature strength. Mater. Sci. Eng. A. 2020, 790, 139578.
61. Li, L.; Wang, C.; Chen, Y.; et al. Effect of Re on microstructure and mechanical properties of γ/γ′ Co-Ti-based superalloys. Intermetallics 2019, 115, 106612.
62. Pandey, P.; Shankar Prasad, A.; Baler, N.; Chattopadhyay, K. On the effect of Ti addition on microstructural evolution, precipitate coarsening kinetics and mechanical properties in a Co–30Ni–10Al–5Mo–2Nb alloy. Materialia 2021, 16, 101072.
63. Klein, L.; Bauer, A.; Neumeier, S.; Göken, M.; Virtanen, S. High temperature oxidation of γ/γ′-strengthened Co-base superalloys. Corros. Sci. 2011, 53, 2027-34.
64. Pei, C.; Ma, Q.; Gao, Q.; et al. A critical review on oxidation behavior of Co-based superalloys. Chin. J. Aeronaut. 2025, 38, 103380.
65. Berthod, P.; Gomis, J. Addition of Co in Ni(Cr)-based cast superalloys for tantalum carbide stabilisation: consequences on the behaviour in oxidation at elevated temperatures. Can. Metall. Q. 2021, 60, 172-82.
66. Kubacka, D.; Weiser, M.; Spiecker, E. Early stages of high-temperature oxidation of Ni- and Co-base model superalloys: a comparative study using rapid thermal annealing and advanced electron microscopy. Corros. Sci. 2021, 191, 109744.
67. Li, W.; Li, L.; Antonov, S.; Lu, F.; Feng, Q. Effects of Cr and Al/W ratio on the microstructural stability, oxidation property and γ′ phase nano-hardness of multi-component Co–Ni-base superalloys. J. Alloys. Compd. 2020, 826, 154182.
68. Chen, Z.; Okamoto, N. L.; Chikugo, K.; Inui, H. On the possibility of simultaneously achieving sufficient oxidation resistance and creep property at high temperatures exceeding 1000 °C in Co-based superalloys. J. Alloys. Compd. 2021, 858, 157724.
69. Bhattacharya, S. K.; Sahara, R.; Narushima, T. Predicting the parabolic rate constants of high-temperature oxidation of Ti alloys using machine learning. Oxid. Met. 2020, 94, 205-18.
70. Pillai, R.; Romedenne, M.; Peng, J.; et al. Lessons learned in employing data analytics to predict oxidation kinetics and spallation behavior of high-temperature NiCr-based alloys. Oxid. Met. 2022, 97, 51-76.
71. Pei, C.; Ma, Q.; Zhang, J.; et al. A novel model to predict oxidation behavior of superalloys based on machine learning. J. Mater. Sci. Technol. 2025, 235, 232-43.
72. Sato, A.; Chiu, Y.; Reed, R. Oxidation of nickel-based single-crystal superalloys for industrial gas turbine applications. Acta. Mater. 2011, 59, 225-40.
73. Wang, Y.; Tan, Y.; Liu, L.; Li, P.; Li, X. Oxidation behavior and mechanism of GH4975 superalloy prepared electron beam smelting between 900 °C and 1100 °C in air. Vacuum 2024, 219, 112752.
74. Neumeier, S.; Freund, L.; Göken, M. Novel wrought γ/γ′ cobalt base superalloys with high strength and improved oxidation resistance. Scr. Mater. 2015, 109, 104-7.
75. Shinagawa, K.; Omori, T.; Sato, J.; et al. Phase equilibria and microstructure on γ′ phase in Co-Ni-Al-W system. Mater. Trans. 2008, 49, 1474-9.
76. Forsik, S. A. J.; Polar Rosas, A. O.; Wang, T.; et al. High-temperature oxidation behavior of a novel Co-base superalloy. Metall. Mater. Trans. A. 2018, 49, 4058-69.
77. Ruan, J.; Xu, W.; Yang, T.; et al. Accelerated design of novel W-free high-strength Co-base superalloys with extremely wide γ/γ′ region by machine learning and CALPHAD methods. Acta. Mater. 2020, 186, 425-33.
78. Zhu, J.; Titus, M. S.; Pollock, T. M. Experimental investigation and thermodynamic modeling of the Co-rich region in the Co-Al-Ni-W quaternary system. J. Phase. Equilib. Diffus. 2014, 35, 595-611.
79. Zhuang, X.; Lu, S.; Li, L.; Feng, Q. Microstructures and properties of a novel γ′-strengthened multi-component CoNi-based wrought superalloy designed by CALPHAD method. Mater. Sci. Eng. A. 2020, 780, 139219.
80. Liang, Z.; Neumeier, S.; Rao, Z.; Göken, M.; Pyczak, F. CALPHAD informed design of multicomponent CoNiCr-based superalloys exhibiting large lattice misfit and high yield stress. Mater. Sci. Eng. A. 2022, 854, 143798.
81. Hillert, M. Diffusion and interface control of reactions in alloys. Metall. Trans. A. 1975, 6, 5-19.
82. Ruan, J.; Ueshima, N.; Oikawa, K. Growth behavior of the δ-Ni3Nb phase in superalloy 718 and modified KJMA modeling for the transformation-time-temperature diagram. J. Alloys. Compd. 2020, 814, 152289.
83. Andersson, J.; Helander, T.; Höglund, L.; Shi, P.; Sundman, B. Thermo-Calc & DICTRA, computational tools for materials science. Calphad 2002, 26, 273-312.
84. Chen, R.; Li, E.; Zou, Y. A survey of energies from pure metals to multi-principal element alloys. J. Mater. Inf. 2024, 4, 26.
85. Khatavkar, N.; Swetlana, S.; Singh, A. K. Accelerated prediction of Vickers hardness of Co- and Ni-based superalloys from microstructure and composition using advanced image processing techniques and machine learning. Acta. Mater. 2020, 196, 295-303.
86. Niezgoda, S. R.; Yabansu, Y. C.; Kalidindi, S. R. Understanding and visualizing microstructure and microstructure variance as a stochastic process. Acta. Mater. 2011, 59, 6387-400.
87. Latypov, M. I.; Toth, L. S.; Kalidindi, S. R. Materials knowledge system for nonlinear composites. Comput. Methods. Appl. Mech. Eng. 2019, 346, 180-96.
88. Taylor, P. L.; Conduit, G. Machine learning predictions of superalloy microstructure. Comput. Mater. Sci. 2022, 201, 110916.
89. Xi, S.; Yu, J.; Bao, L.; et al. Predicting atomic structure and mechanical properties in quinary L12-Strengthened cobalt-based superalloys using machine learning-driven first-principles calculations. Mater. Today. Commun. 2024, 38, 107774.
90. Zhao, Y.; Zhang, Y.; Zhang, Y.; et al. Deformation behavior and creep properties of Co-Al-W-based superalloys: a review. Prog. Nat. Sci. Mater. Int. 2021, 31, 641-8.
91. Weller, L.; M’saoubi, R.; Giuliani, F.; Humphry-Baker, S.; Marquardt, K. Void formation driven by plastic strain partitioning during creep deformation of WC-Co. Int. J. Refract. Met. Hard. Mater. 2025, 126, 106950.
92. Lu, S.; Luo, Z.; Li, L.; Feng, Q. Comparison of creep mechanisms between Co-Al-W- and CoNi-based single crystal superalloys at low temperature and high stresses. Metall. Mater. Trans. A. 2023, 54, 1597-607.
93. Rae, C.; Reed, R. Primary creep in single crystal superalloys: origins, mechanisms and effects. Acta. Mater. 2007, 55, 1067-81.
94. Wu, R.; Sandfeld, S. Insights from a minimal model of dislocation-assisted rafting in single crystal Nickel-based superalloys. Scr. Mater. 2016, 123, 42-5.
95. Wu, R.; Zaiser, M.; Sandfeld, S. A continuum approach to combined γ/γ′ evolution and dislocation plasticity in Nickel-based superalloys. Int. J. Plast. 2017, 95, 142-62.
96. Wu, X.; Dlouhy, A.; Eggeler, Y.; et al. On the nucleation of planar faults during low temperature and high stress creep of single crystal Ni-base superalloys. Acta. Mater. 2018, 144, 642-55.
97. Wang, C.; Wei, X.; Ren, D.; Wang, X.; Xu, W. High-throughput map design of creep life in low-alloy steels by integrating machine learning with a genetic algorithm. Mater. Design. 2022, 213, 110326.
98. Wang, J.; Fa, Y.; Tian, Y.; Yu, X. A machine-learning approach to predict creep properties of Cr–Mo steel with time-temperature parameters. J. Mater. Res. Technol. 2021, 13, 635-50.
99. Zhang, X.; Gong, J.; Xuan, F. A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions. Int. J. Fatigue. 2021, 148, 106236.
100. Dang, Y. Y.; Zhao, X. B.; Yuan, Y.; et al. Predicting long-term creep-rupture property of Inconel 740 and 740H. Mater. High. Temp. 2016, 33, 1-5.
101. Bolton, J. Reliable analysis and extrapolation of creep rupture data. Int. J. Press. Vessel. Pip. 2017, 157, 1-19.
102. Maclachlan, D.; Knowles, D. Modelling and prediction of the stress rupture behaviour of single crystal superalloys. Mater. Sci. Eng. A. 2001, 302, 275-85.
103. Feng, L.; Zhang, K.; Zhang, G.; Yu, H. Anisotropic damage model under continuum slip crystal plasticity theory for single crystals. Int. J. Solids. Struct. 2002, 39, 5279-93.
104. Prasad, S.; Rajagopal, K.; Rao, I. A continuum model for the anisotropic creep of single crystal nickel-based superalloys. Acta. Mater. 2006, 54, 1487-500.
105. Fedelich, B.; Epishin, A.; Link, T.; Klingelhöffer, H.; Künecke, G.; Portella, P. D. Experimental characterization and mechanical modeling of creep induced rafting in superalloys. Comput. Mater. Sci. 2012, 64, 2-6.
106. Kim, Y.; Kim, D.; Kim, H.; Oh, C.; Lee, B. An intermediate temperature creep model for Ni-based superalloys. Int. J. Plast. 2016, 79, 153-75.
107. Venkatesh, V.; Rack, H. J. A neural network approach to elevated temperature creep-fatigue life prediction. Int. J. Fatigue. 1999, 21, 225-34.
108. Tian, N.; Tian, S.; Yan, H.; Shu, D.; Zhang, S.; Zhao, G. Deformation mechanisms and analysis of a single crystal nickel-based superalloy during tensile at room temperature. Mater. Sci. Eng. A. 2019, 744, 154-62.
109. Eggeler, Y.; Müller, J.; Titus, M.; Suzuki, A.; Pollock, T.; Spiecker, E. Planar defect formation in the γ′ phase during high temperature creep in single crystal CoNi-base superalloys. Acta. Mater. 2016, 113, 335-49.
110. Titus, M. S.; Eggeler, Y. M.; Suzuki, A.; Pollock, T. M. Creep-induced planar defects in L12-containing Co- and CoNi-base single-crystal superalloys. Acta. Mater. 2015, 82, 530-9.
111. Zhou, H.; Li, L.; Antonov, S.; Feng, Q. Sub/micro-structural evolution of a Co–Al–W–Ta–Ti single crystal superalloy during creep at 900 °C and 420 MPa. Mater. Sci. Eng. A. 2020, 772, 138791.
112. Lu, S.; Antonov, S.; Li, L.; et al. Atomic structure and elemental segregation behavior of creep defects in a Co-Al-W-based single crystal superalloys under high temperature and low stress. Acta. Mater. 2020, 190, 16-28.
113. 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.
114. Royer, A.; Bastie, P.; Veron, M. In situ determination of γ′ phase volume fraction and of relations between lattice parameters and precipitate morphology in Ni-based single crystal superalloy. Acta. Mater. 1998, 46, 5357-68.
115. Kassner, M.; Pérez-Prado, M. Five-power-law creep in single phase metals and alloys. Prog. Mater. Sci. 2000, 45, 1-102.
116. Wu, R.; Zeng, L.; Fan, J.; Peng, Z.; Zhao, Y. Composition, heat treatment, microstructure and loading condition based machine learning prediction of creep life of superalloys. Mech. Mater. 2023, 187, 104819.
117. 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.
118. Ohl, B.; Campbell, C.; Dunand, D. C. Machine-learning prediction of creep strain rate in γ/γ′ cobalt-based superalloys. Mater. Sci. Eng. A. 2025, 934, 148304.
119. Liu, Y.; Guo, B.; Zou, X.; Li, Y.; Shi, S. Machine learning assisted materials design and discovery for rechargeable batteries. Energy. Stor. Mater. 2020, 31, 434-50.
120. Liu, Y.; Wu, J.; Avdeev, M.; Shi, S. Multi-layer feature selection incorporating weighted score-based expert knowledge toward modeling materials with targeted properties. Adv. Theory. Simul. 2020, 3, 1900215.
121. Huang, Y.; Liu, J.; Zhu, C.; et al. An explainable machine learning model for superalloys creep life prediction coupling with physical metallurgy models and CALPHAD. Comput. Mater. Sci. 2023, 227, 112283.
122. Zhang, S.; Wang, L.; Zhu, S.; et al. Physics-informed neural network for creep-fatigue life prediction of Inconel 617 and interpretation of influencing factors. Mater. Design. 2024, 245, 113267.
123. Raissi, M.; Perdikaris, P.; Karniadakis, G. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 2019, 378, 686-707.
124. Zhu, S.; Xu, W.; Hong, D.; et al. Rare-earth metal complexes supported by 1,3-functionalized indolyl-based ligands for efficient hydrosilylation of alkenes. Inorg. Chem. 2023, 62, 381-91.
125. Wang, L.; Chen, Y.; Mei, X.; Qin, W.; Qin, X. Lightweight sign transformer framework. SIViP 2023, 17, 381-7.
126. Luo, J. H.; Li, B. Q.; Sun, L. L.; et al. Effect of Ti/Cr ratio on coarsening behavior of γ′ precipitates in novel Co-based superalloys. Chin. J. Nonferrous. Met. 2025, 35, 2673-89. (in Chinese).
127. Philippe, T.; Voorhees, P. Ostwald ripening in multicomponent alloys. Acta. Mater. 2013, 61, 4237-44.
128. Pandey, P.; Kashyap, S.; Palanisamy, D.; Sharma, A.; Chattopadhyay, K. On the high temperature coarsening kinetics of γ′ precipitates in a high strength Co37.6Ni35.4Al9.9Mo4.9Cr5.9Ta2.8Ti3.5 fcc-based high entropy alloy. Acta. Mater. 2019, 177, 82-95.
129. Ardell, A. J.; Ozolins, V. Trans-interface diffusion-controlled coarsening. Nat. Mater. 2005, 4, 309-16.
130. Sun, L.; Ma, Q.; Zhang, J.; et al. Identifying determinants of γ’ phase coarsening behavior in Co/CoNi-based superalloys with explainable artificial intelligence (XAI). J. Mater. Inf. 2024, 4, 30.
131. Gan, W.; Gao, H.; Wen, Z. Based on damage caused by microstructure evolution during long-term thermal exposure to analyze and predict creep behavior of Ni-based single crystal superalloy. AIP. Adv. 2020, 10, 085301.
132. Duan, X.; Xu, H.; Wang, E.; et al. Design of novel Ni-based superalloys with better oxidation resistance with the aid of machine learning. J. Mater. Sci. 2023, 58, 11100-14.
133. Liu, P.; Huang, H.; Wen, C.; Lookman, T.; Su, Y. The γ/γ′ microstructure in CoNiAlCr-based superalloys using triple-objective optimization. npj. Comput. Mater. 2023, 9, 1090.
134. 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.
135. Marshal, A.; Pradeep, K.; Music, D.; Zaefferer, S.; De, P.; Schneider, J. Combinatorial synthesis of high entropy alloys: introduction of a novel, single phase, body-centered-cubic FeMnCoCrAl solid solution. J. Alloys. Compd. 2017, 691, 683-9.
136. Hofmann, D. C.; Kolodziejska, J.; Roberts, S.; et al. Compositionally graded metals: a new frontier of additive manufacturing. J. Mater. Res. 2014, 29, 1899-910.
137. Li, X.; Hou, Y.; Cai, W.; et al. Study on crack behavior of GH3230 superalloy fabricated via high-throughput additive manufacturing. Materials 2024, 17, 4225.
138. Ludwig, A. Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods. npj. Comput. Mater. 2019, 5, 205.
139. Li, M. X.; Sun, Y. T.; Wang, C.; et al. Data-driven discovery of a universal indicator for metallic glass forming ability. Nat. Mater. 2022, 21, 165-72.
140. Zhuang, X.; Antonov, S.; Li, W.; Lu, S.; Li, L.; Feng, Q. Alloying effects and effective alloy design of high-Cr CoNi-based superalloys via a high-throughput experiments and machine learning framework. Acta. Mater. 2023, 243, 118525.
