REFERENCES
1. Wei QH, Xiong J, Sun S, Zhang T-Y. Multi-objective machine learning of four mechanical properties of steels. Sci Sin -Tech 2021;51:722-36.
2. Xiong J, Zhang T-Y, Shi S. Machine learning of mechanical properties of steels. Sci China Technol Sci 2020;63:1247-55.
3. Leitherer A, Ziletti A, Ghiringhelli LM. Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning. Nat Commun 2021;12:6234.
4. Sun S, Ouyang R, Zhang B, Zhang T-Y. Data-driven discovery of formulas by symbolic regression. MRS Bull 2019;44:559-64.
5. Xiong J, Shi S, Zhang T-Y. Machine learning of phases and mechanical properties in complex concentrated alloys. Journal of Materials Science & Technology 2021;87:133-42.
6. Xie SR, Quan Y, Hire AC, et al. Machine learning of superconducting critical temperature from Eliashberg theory. npj Comput Mater 2022;8.
7. Levämäki H, Tasnádi F, Sangiovanni DG, Johnson LJS, Armiento R, Abrikosov IA. Predicting elastic properties of hard-coating alloys using ab-initio and machine learning methods. npj Comput Mater 2022;8.
8. Roy Chowdhury P, Ruan X. Unexpected thermal conductivity enhancement in aperiodic superlattices discovered using active machine learning. npj Comput Mater 2022;8.
9. Zhu YQ, 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.
10. Wang JH, Jia J, Sun S, Zhang T-Y. Statistical learning of small data with domain knowledge-sample size-and pre-notch length- dependent strength of concrete. Engineering Fracture Mechanics 2022;259:108160.
11. 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;7:11241.
12. Fung V, Hu G, Ganesh P, Sumpter BG. Machine learned features from density of states for accurate adsorption energy prediction. Nat Commun 2021;12:88.
13. Attia PM, Grover A, Jin N, et al. Machine learned features from density of states for accurate adsorption energy prediction. Nat Commun 2021;12:88.
14. Saito Y, Shin K, Terayama K, et al. Deep-learning-based quality filtering of mechanically exfoliated 2D crystals. npj Comput Mater 2019;5.
15. Li X, Zhao J, Cong J, et al. Machine learning guided automatic recognition of crystal boundaries in bainitic/martensitic alloy and relationship between boundary types and ductile-to-brittle transition behavior. Journal of Materials Science & Technology 2021;84:49-58.
16. Dai F, Wen B, Sun Y, Xiang H, Zhou Y. Theoretical prediction on thermal and mechanical properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by deep learning potential. Journal of Materials Science & Technology 2020;43:168-74.
18. 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.
19. Wen C, Zhang Y, Wang C, et al. Machine learning assisted design of high entropy alloys with desired property. Acta Materialia 2019;170:109-17.
20. Balachandran PV, Kowalski B, Sehirlioglu A, Lookman T. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nat Commun 2018;9:1668.
21. Yan L, Diao Y, Lang Z, Gao K. Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach. Sci Technol Adv Mater 2020;21:359-70.
22. Jablonka KM, Jothiappan GM, Wang S, Smit B, Yoo B. Bias free multiobjective active learning for materials design and discovery. Nat Commun 2021;12:2312.
23. Garrido Torres JA, Gharakhanyan V, Artrith N, Eegholm TH, Urban A. Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures. Nat Commun 2021;12:7012.
24. Lu L, Meng X, Mao Z, Karniadakis GE. DeepXDE: A deep learning library for solving differential equations. SIAM Rev 2021;63:208-28.
25. Lundberg SM, Lee SI. A unified approach to interpreting model predictions, Proceedings of the 31st international conference on neural information processing systems, 2017, pp. 4768-4777. Available from: https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html [Last accessed on 20 Apr 2022].
26. Schulenberg T, Leung LK, Oka Y. Review of R & D for supercritical water cooled reactors. Progress in Nuclear Energy 2014;77:282-99.
27. Zhong X, Wu X, Han E. Effects of exposure temperature and time on corrosion behavior of a ferritic-martensitic steel P92 in aerated supercritical water. Corrosion Science 2015;90:511-21.
28. Klueh R, Nelson A. Ferritic/martensitic steels for next-generation reactors. Journal of Nuclear Materials 2007;371:37-52.
29. Ampornrat P, Was GS. Oxidation of ferritic-martensitic alloys T91, HCM12A and HT-9 in supercritical water. Journal of Nuclear Materials 2007;371:1-17.
30. Li Y, Xu T, Wang S, et al. Modelling and Analysis of the Corrosion Characteristics of Ferritic-Martensitic Steels in Supercritical Water. Materials (Basel) 2019;12:409.
31. Tan L, Ren X, Allen T. Corrosion behavior of 9-12% Cr ferritic-martensitic steels in supercritical water. Corrosion Science 2010;52:1520-8.
32. Li H, Cao Q, Zhu Z. High temperature oxidation behavior of ferritic steel in supercritical water at 550-700 ℃. Materials at High Temperatures 2018;36:111-6.
33. Zhu Z, Xu H, Jiang D, Mao X, Zhang N. Influence of temperature on the oxidation behaviour of a ferritic-martensitic steel in supercritical water. Corrosion Science 2016;113:172-9.
34. Li Y, Wang S, Sun P, et al. Investigation on early formation and evolution of oxide scales on ferritic-martensitic steels in supercritical water. Corrosion Science 2018;135:136-46.
35. Liu Z. Corrosion behavior of designed ferritic-martensitic steels in supercritical water Canada: ProQuest Dissertations Publishing; 2013.
36. Dong Z, Li M, Behnamian Y, et al. Effects of Si, Mn on the corrosion behavior of ferritic-martensitic steels in supercritical water (SCW) environments. Corrosion Science 2020;166:108432.
37. Sun L, Yan W. Estimation of oxidation kinetics and oxide scale void position of ferritic-martensitic steels in supercritical water. Advances in Materials Science and Engineering 2017;2017:1-12.
38. Bischoff J, Motta AT. Oxidation behavior of ferritic-martensitic and ODS steels in supercritical water. Journal of Nuclear Materials 2012;424:261-76.
39. Zhang N, Xu H, Li B, Bai Y, Liu D. Influence of the dissolved oxygen content on corrosion of the ferritic-martensitic steel P92 in supercritical water. Corrosion Science 2012;56:123-8.
40. Chen T, Guestrin C. Xgboost: A scalable tree boosting system; proceedings of the Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, F, 2016.
41. Ouyang R, Curtarolo S, Ahmetcik E, Scheffler M, Ghiringhelli LM. SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys Rev Materials 2018;2.
42. Zhang TY, Cao B, Zhang SY, Sun S. Tree-classifier for linear regression software [No. 2021SR1951267], 2021. Available from: https://register.ccopyright.com.cn/ [Last accessed on 20 Apr 2022].
43. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python, the journal of machine learning research 12 (2012) 2825-2830. Available from: https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf?ref=https://githubhelp.com [Last accessed on 20 Apr 2022].
44. Lundberg SM, Erion GG, Lee SI. Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv 2018; 1802.03888.
45. Uusitalo M, Vuoristo P, Mäntylä T. High temperature corrosion of coatings and boiler steels below chlorine-containing salt deposits. Corrosion Science 2004;46:1311-31.