REFERENCES
1. Callister WD, Rethwisch DG. Materials science and engineering : an introduction. New York: Wiley; 2018.
2. Rajan K. Materials informatics: the materials “Gene” and big data. Annu Rev Mater Res 2015;45:153-69.
3. Williams JC, Starke EA. Progress in structural materials for aerospace systems11The Golden Jubilee Issue - selected topics in Materials Science and Engineering: past, present and future, edited by S. Suresh. Acta Materialia 2003;51:5775-99.
4. Rajan K. Combinatorial materials sciences: experimental strategies for accelerated knowledge discovery. Annu Rev Mater Res 2008;38:299-322.
5. Miracle DB, Li M, Zhang Z, Mishra R, Flores KM. Emerging capabilities for the high-throughput characterization of structural materials. Annu Rev Mater Res 2021;51:131-64.
6. Horstemeyer MF. Integrated Computational Materials Engineering (ICME) for metals: using multiscale modeling to invigorate engineering design with science. John Wiley & Sons; 2012
7. Van Der Giessen E, Schultz PA, Bertin N, et al. Roadmap on multiscale materials modeling. Model Simul Mat Sci Eng 2020;28:043001.
8. Su Y, Fu H, BAI Y, et al. Progress in materials genome engineering in China. Acta Metall Sin 2020;56:1313-23.
9. Kalidindi SR, De Graef M. Materials data science: current status and future outlook. Annu Rev Mater Res 2015;45:171-93.
10. Morgan D, Jacobs R. Opportunities and challenges for machine learning in materials science. Annu Rev Mater Res 2020;50:71-103.
11. Sparks TD, Kauwe SK, Parry ME, Tehrani AM, Brgoch J. Machine learning for structural materials. Annu Rev Mater Res 2020;50:27-48.
12. Suh C, Fare C, Warren JA, Pyzer-knapp EO. Evolving the materials genome: how machine learning is fueling the next generation of materials discovery. Annu Rev Mater Res 2020;50:1-25.
13. Hart GLW, Mueller T, Toher C, Curtarolo S. Machine learning for alloys. Nat Rev Mater 2021;6:730-55.
15. Saal JE, Oliynyk AO, Meredig B. Machine learning in materials discovery: confirmed predictions and their underlying approaches. Annu Rev Mater Res 2020;50:49-69.
16. 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. Materials & Design 2020;195:108996.
17. Ruan J, Xu W, Yang T, et al. Accelerated design of novel W-free high-strength Co-base superalloys with extremely wide γ/γʹ region by machine learning and CALPHAD methods. Acta Materialia 2020;186:425-33.
18. Pan S, Wang Y, Yu J, et al. Accelerated discovery of high-performance Cu-Ni-Co-Si alloys through machine learning. Materials & Design 2021;209:109929.
19. Yang F, Li Z, Wang Q, et al. Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulus. npj Comput Mater 2020;6:1-11.
20. Xiong S, Li X, Wu X, et al. A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus. Computational Materials Science 2020;184:109830.
21. Zhang H, Fu H, Zhu S, Yong W, Xie J. Machine learning assisted composition effective design for precipitation strengthened copper alloys. Acta Materialia 2021;215:117118.
22. 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;6:1-9.
23. Wu C, Chang H, Wu C, et al. Machine learning recommends affordable new Ti alloy with bone-like modulus. Materials Today 2020;34:41-50.
24. Tamura R, Osada T, Minagawa K, et al. Machine learning-driven optimization in powder manufacturing of Ni-Co based superalloy. Materials & Design 2021;198:109290.
25. Qin Z, Wang Z, Wang Y, et al. Phase prediction of Ni-base superalloys via high-throughput experiments and machine learning. Materials Research Letters 2021;9:32-40.
26. Guo S, Yu J, Liu X, Wang C, Jiang Q. A predicting model for properties of steel using the industrial big data based on machine learning. Computational Materials Science 2019;160:95-104.
27. Wang C, Fu H, Jiang L, Xue D, Xie J. A property-oriented design strategy for high performance copper alloys via machine learning. npj Comput Mater 2019;5:1-8.
28. 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.
29. Yi J, Jia Y, Zhao Y, et al. Precipitation behavior of Cu-3.0Ni-0.72Si alloy. Acta Materialia 2019;166:261-70.
30. Cheng J, Tang B, Yu F, Shen B. Evaluation of nanoscaled precipitates in a Cu-Ni-Si-Cr alloy during aging. J Alloys Compd 2014;614:189-95.
31. Hu T, Chen J, Liu J, Liu Z, Wu C. The crystallographic and morphological evolution of the strengthening precipitates in Cu-Ni-Si alloys. Acta Materialia 2013;61:1210-9.
32. Wei H, Chen Y, Zhao Y, Yu W, Su L, Tang D. Correlation mechanism of grain orientation/microstructure and mechanical properties of Cu-Ni-Si-Co alloy. Mater Sci Eng A Struct Mater 2021;814:141239.
33. Li J, Huang G, Mi X, Peng L, Xie H, Kang Y. Microstructure evolution and properties of a quaternary Cu-Ni-Co-Si alloy with high strength and conductivity. Mater Sci Eng A Struct Mater 2019;766:138390.
34. Pollock TM, Tin S. Nickel-based superalloys for advanced turbine engines: chemistry, microstructure and properties. J Propuls Power 2006;22:361-74.
37. Smith TM, Esser BD, Antolin N, et al. Phase transformation strengthening of high-temperature superalloys. Nat Commun 2016;7:13434.
38. Li Y, Ye X, Li J, Zhang Y, Koizumi Y, Chiba A. Influence of cobalt addition on microstructure and hot workability of IN713C superalloy. Materials & Design 2017;122:340-6.
39. Cloots M, Kunze K, Uggowitzer PJ, Wegener K. Microstructural characteristics of the nickel-based alloy IN738LC and the cobalt-based alloy Mar-M509 produced by selective laser melting. Mater Sci Eng A Struct Mater 2016;658:68-76.
40. Huron ES, Bain KR, Mourer DP, et al. The influence of grain boundary elements on properties and microstructures of P/M nickel base superalloys. Superalloys 2004;2004:73-81.
41. Horst OM, Schmitz D, Schreuer J, et al. Thermoelastic properties and γ’-solvus temperatures of single-crystal Ni-base superalloys. J Mater Sci 2021;56:7637-58.
42. Chen J, Huo Q, Chen J, et al. Tailoring the creep properties of second-generation Ni-based single crystal superalloys by composition optimization of Mo, W and Ti. Mater Sci Eng A Struct Mater 2021;799:140163.
43. Cormier J. Thermal cycling creep resistance of Ni-based single crystal superalloys. Proceedings of the 13th International Symposium of Superalloys, Superalloys. 2016. p. 385-94.
44. Rame J, Utada S, Bortoluci Ormastroni LM, et al. Platinum-containing new generation nickel-based superalloy for single crystalline applications. 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. p. 71-81.
45. Kim H, Park S, Seo S, Yoo Y, Jeong H, Jang H. Regression analysis of high-temperature oxidation of Ni-based superalloys using artificial neural network. Corrosion Science 2021;180:109207.
46. Suzuki A, Inui H, Pollock TM. L12-strengthened cobalt-base superalloys. Annu Rev Mater Res 2015;45:345-68.
47. Sato J, Omori T, Oikawa K, Ohnuma I, Kainuma R, Ishida K. Cobalt-base high-temperature alloys. Science 2006;312:90-1.
48. Makineni SK, Singh MP, Chattopadhyay K. Low-density, high-temperature Co base superalloys. Annu Rev Mater Res 2021;51:187-208.
49. Niinomi M, Liu Y, Nakai M, Liu H, Li H. Biomedical titanium alloys with Young's moduli close to that of cortical bone. Regen Biomater 2016;3:173-85.
50. Wang Q, Dong C, Liaw PK. Structural Stabilities of β-Ti alloys studied using a new Mo equivalent derived from [β/(α + β)] phase-boundary slopes. Metall and Mat Trans A 2015;46:3440-7.
51. Sumitomo N, Noritake K, Hattori T, et al. Experiment study on fracture fixation with low rigidity titanium alloy: plate fixation of tibia fracture model in rabbit. J Mater Sci Mater Med 2008;19:1581-6.
52. Abdel-Hady Gepreel M, Niinomi M. Biocompatibility of Ti-alloys for long-term implantation. J Mech Behav Biomed Mater 2013;20:407-15.
53. Eisenbarth E, Velten D, Müller M, Thull R, Breme J. Biocompatibility of beta-stabilizing elements of titanium alloys. Biomaterials 2004;25:5705-13.
54. Peters H, Ebel A. Havkmann J, et al. Industrial data mining in steel industry. 30th Journees Siderurgiques Internationales (JSI); 2012 Dec; Paris. Stahl und Eisen 2012;132.
55. Ordieres-Meré J, González-Marcos A, Castejón-Limas M, Martínez-de-Pisón FJ. Chapter 20: Data mining experiences in steel industry. Handbook of research on machine learning applications and trends: algorithms, methods and techniques. Capítulo de Libro; 2010. p. 427-39.
56. Agrawal A, Deshpande PD, Cecen A, Basavarsu GP, Choudhary AN, Kalidindi SR. Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. Integr Mater Manuf Innov 2014;3:90-108.
57. Agrawal A, Choudhary A. A fatigue strength predictor for steels using ensemble data mining: steel fatigue strength predictor. Proceedings of the 25th ACM International on Conference on information and knowledge management; 2016 Oct 24. 2016; p. 2497-500.
58. Agrawal A, Choudhary A. An online tool for predicting fatigue strength of steel alloys based on ensemble data mining. Int J Fatigue 2018;113:389-400.
59. Poon AIF, Sung JJY. Opening the black box of AI-Medicine. J Gastroenterol Hepatol 2021;36:581-4.
60. Hakkoum H, Idri A, Abnane I. Assessing and Comparing Interpretability Techniques for Artificial Neural Networks Breast Cancer Classification. Comput Methods Biomech Biomed Eng Imaging Vis 2021;9:587-99.
61. Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: a review of machine learning interpretability methods. Entropy (Basel) 2020;23:18.
62. Elshawi R, Al-Mallah MH, Sakr S. On the interpretability of machine learning-based model for predicting hypertension. BMC Med Inform Decis Mak 2019;19:146.
63. Yuan R, Liu Z, Balachandran PV, et al. Accelerated discovery of large electrostrains in BaTiO3-based piezoelectrics using active learning. Adv Mater 2018;30:1702884.
64. Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater 2019;5:1-17.
65. Lookman T, Balachandran PV, Xue D, Hogden J, Theiler J. Statistical inference and adaptive design for materials discovery. Curr Opin Solid State Mater Sci 2017;21:121-8.
66. Kim C, Chandrasekaran A, Jha A, Ramprasad R. Active-learning and materials design: the example of high glass transition temperature polymers. MRS Communications 2019;9:860-6.
67. Peng GCY, Alber M, Tepole AB, et al. Multiscale modeling meets machine learning: what can we learn? Arch Comput Methods Eng 2021;28:1017-37.