Predicting and designing properties of twelve alloy families using artificial neural networks and generative adversarial networks
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
OpenAlex topics: Machine Learning in Materials Science · High Entropy Alloys Studies · Additive Manufacturing Materials and Processes
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (46)
Show source-provided references
1. Morgan D and Jacobs R 2020 Annu. Rev. Mater. Res. https://doi.org/10.1146/annurev-matsci-070218-01001510.1146/annurev-matsci-070218-010015 ↗
2. Choudhary K, DeCost B, Chen C, Jain A, Tavazza F, Cohn R et al 2022 Npj Comput. Mater. 8 5910.1038/s41524-022-00734-6 ↗
3. Butler K, Davies D, Cartwright H, Isayev O and Walsh A 2018 Nature 55910.1038/s41586-018-0337-2 ↗
4. Raccuglia Elbert K, Adler Falk C, Wenny M, Mollo A et al 2016 Nature 533 7310.1038/nature17439 ↗
5. Cortes C and Vapnik V 2009 Lab. Syst. 297 273
6. Che Z and Peng C 2024 Mathematics 12 115310.3390/math12081153 ↗
7. Wang C, Fu H, Jiang L, Xue D and Xie J 2019 Npj Comput. Mater. https://doi.org/10.1038/s41524-019-0227-710.1038/s41524-019-0227-7 ↗
8. Shen C, Wang C, Wei X, Li Y, van der Zwaag S and Xu W 2019 Acta Mater. https://doi.org/10.1016/j.actamat.2019.08.03310.1016/j.actamat.2019.08.033 ↗
9. Chou Y, Tsai J T and Chou J H 2016 IEEE Access 4 110.1109/ACCESS.2016.2521162 ↗
10. Dobrzański L A and Sitek W 1998 J. Mater. Process. Technol. 78 5910.1016/S0924-0136(97)00464-0 ↗
11. Liu Z Y, Wang W D and Gao W 1996 J. Mater. Process. Technol. 57 33210.1016/0924-0136(95)02089-6 ↗
12. Bartók A, Kermode J, Bernstein N and Csányi G 2018 Phys. Review X https://doi.org/10.48550/arXiv.1805.0156810.48550/arXiv.1805.01568 ↗
13. Khaliullin R Z, Eshet H, Kühne T D, Behler J and Parrinello M 2011 Nat. Mater. 10 69310.1038/nmat3078 ↗
14. Asteris P, Roussis P and Douvika M 2017 Mater. Sens.. https://doi.org/10.3390/s1706134410.3390/s17061344 ↗
15. Merayo D, Rodríguez-Prieto A and Camacho A 2020 IEEE Access 8 1344410.1109/ACCESS.2020.2965769 ↗
16. Lan Q, Wang X, Sun J, Chang Z, Deng Q, Sun Q et al 2022 Mater. Today Commun. 31 10330110.1016/j.mtcomm.2022.103301 ↗
17. Srivastava N, Singh L and Yadav M 2023 Met Mater Int. https://doi.org/10.1007/s12540-023-01552-110.1007/s12540-023-01552-1 ↗
18. Xu H, Xu Z, and Zhang K 2020 Proc. 2020 Int. Conf. 56510.1088/1755-1315/565/1/012039 ↗
19. Yu C, Wang W and Bugiolacchi R 2024 Appl. Soft Comput. 166 11220110.1016/j.asoc.2024.112201 ↗
20. Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al 2014 Adv. Neural Inf. Process. Syst. https://doi.org/10.1145/342262210.1145/3422622 ↗
21. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al 2020 Commun. ACM 63 13910.1145/3422622 ↗
22. Xu X and Hu J 2023 J. Non-Cryst. Solids 613 12237810.1016/j.jnoncrysol.2023.122378 ↗
23. Li Z, Li S and Birbilis N 2024 Mater. Today Commun. 38 10794010.1016/j.mtcomm.2023.107940 ↗
24. Leni D, Kesuma D, Maimuzar H and Afriyani S 2024 Eng. Proc. 63 4
25. ASM Handbook 1990 (10th edn.) Volume 1: Properties and selection: Irons, steels, and high-performance alloys
26. ASM Handbook 1990 (10th edn.) Volume 2: Properties and selection: Nonferrous alloys and special-purpose materials
27. Gilbert J K 2000 Understanding the aluminium temper designation system in: Introduction to aluminium alloys and tempers Chapter 4, p. 39 https://doi.org/10.1361/iaat2000p03910.1361/iaat2000p039 ↗
28. Wang R, Wang D, Nagaumi H, Wu Z, Zhang X and Li X 2022 Mater Lett. 312 13165810.1016/j.matlet.2022.131658 ↗
29. El Mahallawi I, Abdel-Karim R and Naguib A 2001 Mater. Sci. Technol. 17 138510.1179/026708301101509340 ↗
30. Chi H, Pian L, Gu J, Sun Y, Pang X, Xin Z et al 2024 Steel Res. Int. https://doi.org/10.1002/srin.20240041210.1002/srin.202400412 ↗
31. Padhy S P, Chaudhary V, Lim Y-F, Zhu R, Thway M, Hippalgaonkar K et al 2024 iScience 27 10972310.1016/j.isci.2024.109723 ↗
32. ASM Handbook 1991 (10th edn.) Vol. 4, Heat treating ISBN 1615031626, 9781615031627
33. Malashin I, Tynchenko V, Gantimurov A, Nelyub V and Borodulin A 2025 Polymers 17 49110.3390/polym17040491 ↗
34. Cahn R W and Haasen P 1996 (4th edn.) Physical Metallurgy10.1017/CBO9781107295551 ↗
35. Mukhamedov B O, Karavaev K V and Abrikosov I A 2021 Phys. Rev. Mater. 5 10440710.1103/PhysRevMaterials.5.104407 ↗
36. Ma Q, Li X, Xin R, Liu E, Gao Q, Sun L et al 2023 J. Mater. Res. Technol. 26 416810.1016/j.jmrt.2023.08.139 ↗
37. Seco/Warwick (10th edn.) Heat treating data book, Available at https://www.secowarwick.com/en/tech-spotlights/heat-treating-data-book/
38. Chandra Kandpal B, Gupta D K, Kumar A, Kumar Jaisal A, Kumar Ranjan A, Srivastava A et al 2021 Mater. Today Proc. 44 19910.1016/j.matpr.2020.08.556 ↗
39. Dossett JL 2020 Practical Heat Treating: Basic Principles (ASM International)10.31399/asm.tb.phtbp.9781627083263 ↗
40. Jeon J, Seo N, Son S B, Lee S-J and Jung M 2021 Metals. 11 115910.3390/met11081159 ↗
41. Singh R 2016 (2nd eds) Applied Welding Engineering (Butterworth-Heinemann)
42. Marzie G, Zhipeng L, Nick B 2023 Revolutionising inverse design of magnesium alloys through generative adversarial networks. arXiv preprint http://arxiv.org/abs/2310.07836v3.
43. Karthika S and Durgadevi M 2021 Proceedings of the International Conference on Computing, Electronics and Electrical Engineering 1
44. Jabbar R, Jabbar R and Kamoun S 2022 Comput. Mater. Sci. 213 11161210.1016/j.commatsci.2022.111612 ↗
45. Fuhr A and Sumpter B 2022 Front Mater. 9 86527010.3389/fmats.2022.865270 ↗
46. Sharma P, Kumar M, Sharma H K and Biju S M 2024 Multimed Tools Appl. https://doi.org/10.1007/s11042-024-18767-y10.1007/s11042-024-18767-y ↗