Machine learning-based phase design of Mg-Re alloy LPSO phase

Minglei Zhang; Jian Zeng; Xiaoya Chen; Quanan Li; Zheng Wu

doi:10.1007/s10853-025-10940-4

journal article·2025-07

Machine learning-based phase design of Mg-Re alloy LPSO phase

Minglei Zhang, Jian Zeng, Xiaoya Chen, Quanan Li, Zheng Wu

Journal of Materials Science· Volume 60· Issue 26

Publisher page

Bibliographic record

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

Metals & alloys

OpenAlex topics: Magnesium Alloys: Properties and Applications · Titanium Alloys Microstructure and Properties · Hydrogen Storage and Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (41)

Show source-provided references

1. Hao Z, Ju Y, Chen L (2023) The use of aluminium and magnesium alloys in automotive lightweight technologies. J Mech Sci Technol 37:4615–4622. https://doi.org/10.1007/s12206-023-0712-210.1007/s12206-023-0712-2 ↗

2. Bai JY, Yang Y, Wen C, Chen J, Zhou G, Jiang B, Peng XD, Pan FS (2023) Applications of magnesium alloys for aerospace: a review. J Magnes Alloys 11:3609–3619. https://doi.org/10.1016/j.jma.2023.09.01510.1016/j.jma.2023.09.015 ↗

3. Chen XY, Li QA, Yan JL, Chen PJ (2023) Microstructure and high temperature mechanical properties of the Mg-Gd-Y(-Nd)-Zr alloy. J Mater Res Technol 24:866–878. https://doi.org/10.1016/j.jmrt.2023.03.06510.1016/j.jmrt.2023.03.065 ↗

4. She J, Chen J, Xiong XM, Yang Y, Peng XD, Chen DL, Pan FS (2024) Research advances of magnesium and magnesium alloys globally in 2023. J Magnes Alloys 12:3441–3475. https://doi.org/10.1016/j.jma.2024.10.00110.1016/j.jma.2024.10.001 ↗

5. Luo Q, Guo YL, Liu B, Feng YJ, Zhang JY, Li Q, Chou K (2020) Thermodynamics and kinetics of phase transformation in rare earth-magnesium alloys: a critical review. J Mater Sci Technol 44:171–190. https://doi.org/10.1016/j.jmst.2020.01.02210.1016/j.jmst.2020.01.022 ↗

6. Yang XD, Zhou XJ, Yu SL, Zhang J, Lu XZ, Chen XM, Lu LW, Huang WY, Liu YR (2022) Tensile behavior at various temperatures of the Mg-Gd-Y-Zn-Zr alloys with different initial morphologies of LPSO phases prior to extrusion. Mat Sci Eng a-Struct 851:143634. https://doi.org/10.1016/j.msea.2022.14363410.1016/j.msea.2022.143634 ↗

7. Zhou XJ, Liu CM, Gao YH, Jiang SN, Han XZ, Chen ZY (2017) Evolution of LPSO phases and their effect on dynamic recrystallization in a Mg-Gd-Y-Zn-Zr alloy. Metall Mater Trans A 48:3060–3072. https://doi.org/10.1007/s11661-017-4081-210.1007/s11661-017-4081-2 ↗

8. Su C, Wang JF, Hu H, Wen YL, Liu SJ, Ma K (2021) Enhanced strength and corrosion resistant of Mg-Gd-Y-Al alloys by LPSO phases with different Al content. J Alloys Compd 885:160557. https://doi.org/10.1016/j.jallcom.2021.16055710.1016/j.jallcom.2021.160557 ↗

9. Pei ZR, Yin JQ (2019) Machine learning as a contributor to physics: understanding Mg alloys. Mater Des 172:107759. https://doi.org/10.1016/j.matdes.2019.10775910.1016/j.matdes.2019.107759 ↗

10. Liu XJ, Xu PC, Zhao JJ, Lu WC, Li MJ, Wang G (2022) Material machine learning for alloys: applications, challenges and perspectives. J Alloys Compd 921:165984. https://doi.org/10.1016/j.jallcom.2022.16598410.1016/j.jallcom.2022.165984 ↗

11. Mi XX, Jing XR, Wang HL, Xu JB, She J, Tang AT, Holmedal B, Pan FS (2023) A machine learning enabled ultra-fine grain design strategy of Mg-Mn-based alloys. J Mater Res Technol 23:4576–4590. https://doi.org/10.1016/j.jmrt.2023.02.09110.1016/j.jmrt.2023.02.091 ↗

12. Moses A, Chen D, Wan P, Wang SY (2023) Prediction of electrochemical corrosion behavior of magnesium alloy using machine learning methods. Mater Today Commun 37:107285. https://doi.org/10.1016/j.mtcomm.2023.10728510.1016/j.mtcomm.2023.107285 ↗

13. Gui YW, Li QA, Zhu KG, Xue YB (2021) A combined machine learning and EBSD approach for the prediction of 10–12 twin nucleation in an Mg-RE alloy. Mater Today Commun 27:102282. https://doi.org/10.1016/j.mtcomm.2021.10228210.1016/j.mtcomm.2021.102282 ↗

14. Wang S, Wu Y, Xu Q, Ma YM, Wang TZ (2024) Research on stamping formability and process simulation of AZ31 magnesium alloy based on deep learning. Mater Today Commun 40:109807. https://doi.org/10.1016/j.mtcomm.2024.10980710.1016/j.mtcomm.2024.109807 ↗

15. Qin Q, Zhang Z, Long H, Zhuo J, Li Y (2024) Prediction of creep properties of Co–10Al–9W superalloys with machine learning. J Mater Sci 59:4571–4585. https://doi.org/10.1007/s10853-024-09507-610.1007/s10853-024-09507-6 ↗

16. Liu G, Wu Q, Ma Y, Huang J, Xie Q, Xiao Q, Gao T (2025) Machine learning-based phase prediction in high-entropy alloys: further optimization of feature engineering. J Mater Sci 60:3999–4019. https://doi.org/10.1007/s10853-025-10697-w10.1007/s10853-025-10697-w ↗

17. Cheng YH, Wang LF, Bai YL, Wang HX, Cheng WL, Tiyyagura HR, Komissarov A, Shin KS (2024) A brief review of machine learning-assisted Mg alloy design, processing, and property predictions. J Mater Res Technol 30:8108–8127. https://doi.org/10.1016/j.jmrt.2024.05.13910.1016/j.jmrt.2024.05.139 ↗

18. Dong S, Wang Y, Li J, Li Y, Wang L, Zhang J (2023) Machine learning aided prediction and design for the mechanical properties of magnesium alloys. Met Mater Int 30:593–606. https://doi.org/10.1007/s12540-023-01531-610.1007/s12540-023-01531-6 ↗

19. Hu MW, Tan QY, Knibbe R, Xu M, Liang GF, Zhou JX, Xu J, Jiang B, Li X, Ramajayam M, Dorin T, Zhang MX (2024) Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning. Comput Mater Sci 244:113204. https://doi.org/10.1016/j.commatsci.2024.11320410.1016/j.commatsci.2024.113204 ↗

20. Shen QQ, Yin Q, Zhao HL, Zhang SY, Fan YH, Dong XL, Guo CW (2024) Inversely optimized design of Al-Mg-Si alloys using machine learning methods. Comput Mater Sci 242:113107. https://doi.org/10.1016/j.commatsci.2024.11310710.1016/j.commatsci.2024.113107 ↗

21. Tong ZN, Wang LY, Zhu GM, Zeng XQ (2019) Predicting twin nucleation in a polycrystalline Mg alloy using machine learning methods. Metall Mater Trans A 50:5543–5560. https://doi.org/10.1007/s11661-019-05468-710.1007/s11661-019-05468-7 ↗

22. Mi X, Tian L, Tang A, Kang J, Peng P, She J, Wang H, Chen X, Pan F (2022) A reverse design model for high-performance and low-cost magnesium alloys by machine learning. Comput Mater Sci 201:110881. https://doi.org/10.1016/j.commatsci.2021.11088110.1016/j.commatsci.2021.110881 ↗

23. Li JP, Yang Z, Liu T, Guo YC, Xia F, Yang JM, Liang MX (2007) Microstructures of extruded Mg–12Gd–1Zn–0.5Zr and Mg–12Gd–4Y–1Zn–0.5Zr alloys. Scripta Mater 56:137–140. https://doi.org/10.1016/j.scriptamat.2006.09.00810.1016/j.scriptamat.2006.09.008 ↗

24. Cui WD, Xiao L, Liu WC, Wu GH, Wang XH, Li ZQ (2017) Effect of Zn addition on microstructure and mechanical properties of Mg–9Gd–3Y–0.5Zr alloy. J Mater Res 33:733–744. https://doi.org/10.1557/jmr.2017.45810.1557/jmr.2017.458 ↗

25. Jin X, Xu W, Shan D, Liu C, Zhang Q (2017) Deformation behavior, microstructure evolution and hot workability of Mg-3.7Gd-2.9Y-0.7Zn-0.7Zr alloy. Met Mater Int 23:434–443. https://doi.org/10.1007/s12540-017-6352-210.1007/s12540-017-6352-2 ↗

26. Yin SQ, Zhang ZQ, Liu X, Le QC, Lan Q, Bao L, Cui JZ (2017) Effects of Zn/Gd ratio on the microstructures and mechanical properties of Mg-Zn-Gd-Zr alloys. Mat Sci Eng a-Struct 695:135–143. https://doi.org/10.1016/j.msea.2017.03.11710.1016/j.msea.2017.03.117 ↗

27. Xue ZY, Han XZ, Zhou ZY, Wang YL, Li XS, Wu JP (2018) Effects of microstructure and texture evolution on strength improvement of an extruded Mg-10Gd-2Y-05Zn-03Zr Alloy. Metals 8:1087. https://doi.org/10.3390/met812108710.3390/met8121087 ↗

28. Liu JN, Zhang XR, Lv WX, Zhou YM, Fang DQ, Ding XD, Sun J (2021) Microstructure and mechanical properties of Mg-2.0Gd-1.2Y-1.0Zn-0.2Zr Alloy. Met Mater Int 27:1969–1979. https://doi.org/10.1007/s12540-020-00782-x10.1007/s12540-020-00782-x ↗

29. Liu Y, Wen JB, Yao H, He JG, Li H (2020) Enhancing the corrosion resistance performance of Mg-1.8Zn-1.74Gd-0.5Y-0.4Zr biomaterial via solution treatment process. Materials 13:836. https://doi.org/10.3390/ma1304083610.3390/ma13040836 ↗

30. Wang D, Fu PH, Peng LM, Wang YX, Ding WJ (2020) A study of microstructure, mechanical behavior and strengthen mechanism in the Mg-10Gd-0.2Zn-(Y)-0.4Zr alloy. Mat Sci Eng a-Struct 793:139881. https://doi.org/10.1016/j.msea.2020.13988110.1016/j.msea.2020.139881 ↗

31. Kim J, Liao HX, Ou X, Zhang ZB, Kang KK, Lee TY, Pan FS (2022) Effect on Zn on microstructures and mechanical properties of Mg-Gd-Y-Zn LPSO alloys. Met Mater Int 28:2613–2620. https://doi.org/10.1007/s12540-022-01176-x10.1007/s12540-022-01176-x ↗

32. Lei B, Dong ZH, Yang Y, Jiang B, Yuan M, Yang HB, Wang QH, Huang GS, Song JF, Zhang DF, Pan FS (2022) Influence of Zn on the microstructure and mechanical properties of Mg-Gd-Zr alloy. Mat Sci Eng a-Struct 843:143136. https://doi.org/10.1016/j.msea.2022.14313610.1016/j.msea.2022.143136 ↗

33. Lei B, Wang CH, Jiang B, Bai SW, Dong ZH, Qian XY, He C, Xu JY, Yang HB, Wang QH, Pan FS (2022) Role of Y on the microstructure and mechanical properties of Mg-Gd-Zr alloy. Mat Sci Eng a-Struct 861:144371. https://doi.org/10.1016/j.msea.2022.14437110.1016/j.msea.2022.144371 ↗

34. Xi GQ, Zhao XH, Ma YL, Mou Y, Xiong J, Ma K, Wang JF (2023) Comparative study on corrosion behavior and mechanism of As-Cast Mg-Zn-Y and Mg-Zn-Gd alloys. Acta Metall Sin-Engl 36:310–322. https://doi.org/10.1007/s40195-022-01455-x10.1007/s40195-022-01455-x ↗

35. Zheng J, Yan ZM, Ji JS, Shi YS, Zhang H, Zhang ZM, Xue Y (2022) Effect of heat treatment on mechanical properties and microstructure evolution of Mg-9.5Gd-4Y-2.2Zn-0.5Zr alloy. J Magnes Alloys 10:1124–1132. https://doi.org/10.1016/j.jma.2021.05.01810.1016/j.jma.2021.05.018 ↗

36. Wang D, He Y, Yu YY, Fan P, Gao H, Fu PH, Peng LM (2023) Microstructure and mechanical properties of cast Mg-6Gd-Zr alloy with different Y addition. J Mater Res Technol 27:4494–4502. https://doi.org/10.1016/j.jmrt.2023.11.03510.1016/j.jmrt.2023.11.035 ↗

37. Xia XS, Yang EA (2023) Effect of Zn addition on the microstructure, mechanical properties, and fracture behavior of As-Cast Mg-Gd-Y-Zr alloys. Materials 16:2720. https://doi.org/10.3390/ma1607272010.3390/ma16072720 ↗

38. Xia XS, Shu DY, Yang EC, Hu CK (2023) A comparative study of the role of Zn in microstructures and the mechanical properties of Mg-Gd-Y-Zr alloys. J Mater Res Technol 25:2903–2912. https://doi.org/10.1016/j.jmrt.2023.06.14210.1016/j.jmrt.2023.06.142 ↗

39. Chen MY, Tao X, Wang LQ, Ma K, Zhang DD, Li YL, Wei SZ, Zhang Z, Zhao ZY, Bai PK (2024) Phase transformation during solid-solution and aging treatment of a Mg-Gd-Y-Zn-Zr alloy containing LPSO and W phases. J Alloys Compd 1005:176176. https://doi.org/10.1016/j.jallcom.2024.17617610.1016/j.jallcom.2024.176176 ↗

40. Chen XY, Wang DZ, Li QA, Xiong Y, Tian KK, Chen PJ (2024) Hot compression behavior and microstructure evolution of Mg-Gd-Y-Zn-Zr alloy. J Mater Res Technol 30:755–768. https://doi.org/10.1016/j.jmrt.2024.03.11210.1016/j.jmrt.2024.03.112 ↗

41. Li L, Cui L, Sha J, Zhao Z, Bao J, Yang Y, Qiao M, Tian J, Liu W, Zhang Z (2025) Deformation behavior and resultant microstructural evolution of Mg-11Gd-1Zn-0.5Sn-0.5Zr alloy containing LPSO phase during hot compression. J Mater Sci 60:2095–2117. https://doi.org/10.1007/s10853-024-10557-z10.1007/s10853-024-10557-z ↗