REFERENCES
2. Mandal S, Sivaprasad PV, Venugopal S, Murthy KPN. Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion. Appl Soft Comput 2009;9:237-44.
3. Guo Z, Sha W. Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network. Comput Mater Sci 2004;29:12-28.
4. Gavard L, Bhadeshia HKDH, MacKay DJC, Suzuki S. Bayesian neural network model for austenite formation in steels. Mater Sci Technol 1996;12:453-63.
5. Bailer-jones C, Bhadeshia H, Mackay D. Gaussian process modelling of austenite formation in steel. Mater Sci Technol 1999;15:287-94.
6. 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.
7. Shen C, Wang C, Huang M, Xu N, van der Zwaag S, Xu W. A generic high-throughput microstructure classification and quantification method for regular SEM images of complex steel microstructures combining EBSD labeling and deep learning. J Mater Sci Technol 2021;93:191-204.
8. Zhang Z, Wen G, Chen S. Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding. J Manuf Process 2019;45:208-16.
9. Ma J, Luo D, Liao X, Zhang Z, Huang Y, Lu J. Tool wear mechanism and prediction in milling TC18 titanium alloy using deep learning. Measurement 2021;173:108554.
10. Su Y, Fu H, Bai Y, Jiang X, Xie J. Progress in materials genome engineering in China. Acta Metall Sin 2020;56:1313-23. (in Chinese).
11. Himanen L, Geurts A, Foster AS, Rinke P. Data-driven materials science: status, challenges, and perspectives. Adv Sci 2019;6:1900808.
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. Raccuglia P, Elbert KC, Adler PDF, et al. Machine-learning-assisted materials discovery using failed experiments. Nature 2016;533:73-6.
14. Medasani B, Gamst A, Ding H, et al. Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning. npj Comput Mater 2016;2:1.
15. Takahashi A, Seko A, Tanaka I. Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: application to elemental titanium. Phys Rev Materials 2017;1:063801.
16. 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.
17. Wang C, Shen C, Cui Q, Zhang C, Xu W. Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels. J Nucl Mater 2020;529:151823.
18. Niu B, Wang Z, Wang Q, et al. Dual-phase synergetic precipitation in Nb/Ta/Zr co-modified Fe-Cr-Al-Mo alloy. Intermetallics 2020;124:106848.
19. Domínguez LA, Goodall R, Todd I. Prediction and validation of quaternary high entropy alloys using statistical approaches. Mater Sci Technol 2015;31:1201-6.
20. 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.
21. Chang YJ, Jui CY, Lee WJ, Yeh AC. Prediction of the composition and hardness of high-entropy alloys by machine learning. JOM 2019;71:3433-42.
22. He L, Wang Z, Akebono H, Sugeta A. Machine learning-based predictions of fatigue life and fatigue limit for steels. J Mater Sci Technol 2021;90:9-19.
23. 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.
24. Zhang J, Xu B, Xiong Y, et al. Design high-entropy carbide ceramics from machine learning. npj Comput Mater 2022;8:678.
25. Qiao L, Zhu J, Wan Y, Cui C, Zhang G. Finite element-based machine learning approach for optimization of process parameters to produce silicon carbide ceramic complex parts. Ceram Int 2022;48:17400-11.
26. 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.
27. Xia K, Gao H, Liu C, et al. A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search. Sci Bull 2018;63:817-24.
28. Yu J, Xi S, Pan S, et al. Machine learning-guided design and development of metallic structural materials. J Mater Inf 2021;1:9.
29. Matsugi K, Murata Y, Morinaga M, Yukawa N. An electronic approach to alloy design and its application to Ni-based single-crystal superalloys. Mater Sci Eng A 1993;172:101-10.
30. Mehjabeen A, Xu W, Qiu D, Qian M. Redefining the β-phase stability in Ti-Nb-Zr alloys for alloy design and microstructural prediction. JOM 2018;70:2254-9.
32. Hume-Rothery W, Raynor GV. The equilibrium and lattice-spacing relations in the system magnesium-cadmium. Proc R Soc Lond A 1940;174:471-86.
33. Zhang YM, Yang S, Evans JRG. Revisiting Hume-Rothery’s Rules with artificial neural networks. Acta Mater 2008;56:1094-105.
34. Kattner UR. The calphad method and its role in material and process development. Tecnol Metal Mater Min 2016;13:3-15.
35. Tancret F, Toda-Caraballo I, Menou E, Rivera Díaz-del-castillo PEJ. Designing high entropy alloys employing thermodynamics and Gaussian process statistical analysis. Mater Design 2017;115:486-97.
36. Wu W, Sun Q. Applying machine learning to accelerate new materials development. Sci Sin Phys Mech Astron 2018;48:107001.
37. Wang Y, Wagner N, Rondinelli JM. Symbolic regression in materials science. MRS Commun 2019;9:793-805.
38. Cui C, Cao G, Cao Y, et al. Physical metallurgy guided deep learning for yield strength of hot-rolled steel based on the small labeled dataset. Mater Design 2022;223:111269.
39. Jiang L, Fu H, Zhang H, Xie J. Physical mechanism interpretation of polycrystalline metals’ yield strength via a data-driven method: a novel Hall-Petch relationship. Acta Mater 2022;231:117868.
40. Zhang Y, Ling C. A strategy to apply machine learning to small datasets in materials science. npj Comput Mater 2018;4:81.
41. Xue D, Balachandran PV, Hogden J, Theiler J, Xue D, Lookman T. Accelerated search for materials with targeted properties by adaptive design. Nat Commun 2016;7:BFncomms11241.
42. Dai D, Xu T, Wei X, et al. Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys. Comput Mater Sci 2020;175:109618.
43. Rickman JM, Chan HM, Harmer MP, et al. Materials informatics for the screening of multi-principal elements and high-entropy alloys. Nat Commun 2019;10:10533.
44. Childs CM, Washburn NR. Embedding domain knowledge for machine learning of complex material systems. MRS Commun 2019;9:806-20.
45. Murdock RJ, Kauwe SK, Wang AYT, Sparks TD. Is domain knowledge necessary for machine learning materials properties? Integr Mater Manuf Innov 2020;9:221-7.
46. Jain A, Ong SP, Hautier G, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater 2013;1:011002.
47. Choudhary K, Garrity KF, Reid ACE, et al. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design. npj Comput Mater 2020;6:440.
48. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res 2011;12:2825-30. Available from: https://jmlr.org/papers/v12/pedregosa11a.html. [Last accessed on 25 Sep 2023]
49. Chen T, Guestrin C. Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: Association for Computing Machinery; 2016. pp. 785-94.
50. Abadi M. TensorFlow: learning functions at scale. In: Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming. New York, NY, USA: Association for Computing Machinery; 2016. p. 1.
51. Imambi S, Prakash KB, Kanagachidambaresan GR. PyTorch. In: Prakash KB, Kanagachidambaresan GR, editors. Programming with TensorFlow. EAI/Springer innovations in communication and computing. Cham: Springer; 2021. pp. 87-104.
52. Erickson N, Mueller J, Shirkov A, et al. Autogluon-tabular: robust and accurate automl for structured data. arXiv. [Preprint.] Mar 13, 2020 [accessed 2024 Sep 25]. Available from: https://arxiv.org/abs/2003.06505.
53. Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett 2018;120:145301.
54. Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater 2019;31:3564-72.
55. Wang H, Zhang L, Han J, Weinan E. DeePMD-kit: a deep learning package for many-body potential energy representation and molecular dynamics. Comput Phys Commun 2018;228:178-84.
56. Wang Z, Han Y, Cai J, Chen A, Li J. Vision for energy material design: a roadmap for integrated data-driven modeling. J Energy Chem 2022;71:56-62.
57. Naser MZ, Alavi AH. Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences. Archit Struct Constr 2023;3:499-517.
59. Ye Y, Wang Q, Lu J, Liu C, Yang Y. High-entropy alloy: challenges and prospects. Mater Today 2016;19:349-62.
60. Liu Y, Wang J, Xiao B, Shu J. Accelerated development of hard high-entropy alloys with data-driven high-throughput experiments. J Mater Inf 2022;2:3.
61. Rao Z, Tung P, Xie R, et al. Machine learning-enabled high-entropy alloy discovery. Science 2022;378:78-85.
62. 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;5:227.
63. 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.
64. Ozaki T, Matsumoto H, Watanabe S, Hanada S. Beta Ti alloys with low young’s modulus. Mater Trans 2004;45:2776-9.
65. Pang C, Jiang B, Shi Y, Wang Q, Dong C. Cluster-plus-glue-atom model and universal composition formulas [cluster](glue atom)x for BCC solid solution alloys. J Alloys Compd 2015;652:63-9.
66. Wang Q, Ji C, Wang Y, Qiang J, Dong C. β-Ti alloys with low young’s moduli interpreted by cluster-plus-glue-atom model. Metall Mater Trans A 2013;44:1872-9.
67. Jiang B, Wang Q, Wen D, et al. Effects of Nb and Zr on structural stabilities of Ti-Mo-Sn-based alloys with low modulus. Mater Sci Eng A 2017;687:1-7.
68. 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;6:372.
69. Liu X, Tan X. Giant strains in non-textured (Bi1/2Na1/2)TiO3-based lead-free ceramics. Adv Mater 2016;28:574-8.
70. Yan B, Cheng L, Li B, et al. Bi-directional prediction of structural characteristics and effective thermal conductivities of composite fuels through learning from finite element simulation results. Mater Design 2020;189:108483.
71. Clark SJ, Segall MD, Pickard CJ, et al. First principles methods using CASTEP. Z Krist Cryst Mater 2005;220:567-70.
72. Hafner J. Ab-initio simulations of materials using VASP: density-functional theory and beyond. J Comput Chem 2008;29:2044-78.
73. Jinnouchi R, Karsai F, Kresse G. On-the-fly machine learning force field generation: application to melting points. Phys Rev B 2019;100:014105.
74. Jinnouchi R, Lahnsteiner J, Karsai F, Kresse G, Bokdam M. Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on the fly with bayesian inference. Phys Rev Lett 2019;122:225701.
75. Noé F, Tkatchenko A, Müller K, Clementi C. Machine learning for molecular simulation. Annu Rev Phys Chem 2020;71:361-90.
76. Zong H, Pilania G, Ding X, Ackland GJ, Lookman T. Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning. npj Comput Mater 2018;4:103.
77. Sun S, Ouyang R, Zhang B, Zhang T. Data-driven discovery of formulas by symbolic regression. MRS Bull 2019;44:559-64.
78. Wei Q, Cao B, Deng L, Sun A, Dong Z, Zhang T. Discovering a formula for the high temperature oxidation behavior of FeCrAlCoNi based high entropy alloys by domain knowledge-guided machine learning. J Mater Sci Technol 2023;149:237-46.
79. Melhem HG, Nagaraja S. Machine learning and its application to civil engineering systems. Civil Eng Syst 1996;13:259-79.
80. Yuan J, Wang Q, Li Z, Dong C, Zhang P, Ding X. Domain-knowledge-oriented data pre-processing and machine learning of corrosion-resistant γ-U alloys with a small database. Comput Mater Sci 2021;194:110472.
81. Zou C, Li J, Wang WY, et al. Integrating data mining and machine learning to discover high-strength ductile titanium alloys. Acta Mater 2021;202:211-21.
82. Tian F, Lin D, Gao X, Zhao Y, Song H. A structural modeling approach to solid solutions based on the similar atomic environment. J Chem Phys 2020;153:034101.
83. Liu G, Sun B. Concrete compressive strength prediction using an explainable boosting machine model. Case Stud Constr Mater 2023;18:e01845.
