Page 83 - Read Online

Page 16 of 17 Cheng et al. J. Mater. Inf. 2025, 5, 53 https://dx.doi.org/10.20517/jmi.2025.61

Mg-Gd-Y-Zn-Zr alloys by altering cooling rate. J. Mater. Res. Technol. 2023, 24, 7258-69. DOI
13. Chen, J.; Ji, C.; Huang, Q.; et al. Formation mechanism of W phase and its effects on the mechanical properties of Mg−Dy−Zn alloys.
J. Magnes. Alloys. 2025, 13, 2174-89. DOI
14. Wu, G.; Tong, X.; Wang, C.; Jiang, R.; Ding, W. Recent advances on grain refinement of magnesium rare-earth alloys during the
whole casting processes: a review. J. Magnes. Alloys. 2023, 11, 3463-83. DOI
15. Xue, K.; Luo, Z.; Xia, S.; Dong, J.; Li, P. Study of microstructural evolution, mechanical properties and plastic deformation behavior
of Mg-Gd-Y-Zn-Zr alloy prepared by high-pressure torsion. Mater. Sci. Eng. A. 2024, 891, 145953. DOI
16. Jiang, Y.; Le, Q.; Zhu, Y.; et al. Review on forming process of magnesium alloy characteristic forgings. J. Alloys. Compd. 2024, 970,
172666. DOI
17. Wei, J.; Chu, X.; Sun, X.; et al. Machine learning in materials science. InfoMat 2019, 1, 338-58. DOI
18. Zhang, C. C.; Zhang, K.; Ni, R.; Liu, H.; Shen, J. Unleashing the potential of machine learning: an exploration of state-of-the-art
algorithms and real-world applications in computer vision. In 2023 Congress in Computer Science, Computer Engineering, & Applied
Computing (CSCE), Las Vegas, USA. July 24-27, 2023. IEEE; 2023. pp. 422-5. DOI
19. Kate, C.; Kalpana, C.; Sharma, A.; Yadav, A. S.; Kumar, A.; Kumar, S. S. Investigation of machine learning algorithms for pattern
recognition in image processing. In 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA)
Coimbatore, India. August 03-05, 2023. IEEE; 2023. pp. 898-904. DOI
20. Sharma, H.; Jindal, H.; Devi, B. Advancements in natural language processing: techniques and applications. In 2023 International
Conference on Advanced Computing & Communication Technologies (ICACCTech), Banur, India. December 23-24, 2023. IEEE;
2023. pp. 61-5. DOI
21. Rahnama, A.; Zepon, G.; Sridhar, S. Machine learning based prediction of metal hydrides for hydrogen storage, part I: prediction of
hydrogen weight percent. Int. J. Hydrogen. Energy. 2019, 44, 7337-44. DOI
22. Liu, H.; Cheng, J.; Dong, H.; et al. Screening stable and metastable ABO perovskites using machine learning and the materials
3
project. Comput. Mater. Sci. 2020, 177, 109614. DOI
23. Ghorbani, M.; Boley, M.; Nakashima, P.; Birbilis, N. A machine learning approach for accelerated design of magnesium alloys. Part
A: alloy data and property space. J. Magnes. Alloys. 2023, 11, 3620-33. DOI
24. Fu, Z.; Liu, W.; Huang, C.; Mei, T. A review of performance prediction based on machine learning in materials science.
Nanomaterials 2022, 12, 2957. DOI PubMed PMC
25. Lee, K.; Song, Y.; Kim, S.; et al. Genetic design of new aluminum alloys to overcome strength-ductility trade-off dilemma. J. Alloys.
Compd. 2023, 947, 169546. DOI
26. Li, J.; Zhang, Y.; Cao, X.; et al. Accelerated discovery of high-strength aluminum alloys by machine learning. Commun. Mater. 2020,
1, 74. DOI
27. Xue, D.; Balachandran, P. V.; Hogden, J.; Theiler, J.; Xue, D.; Lookman, T. Accelerated search for materials with targeted properties
by adaptive design. Nat. Commun. 2016, 7, 11241. DOI PubMed PMC
28. Qian, C.; Tan, R. K.; Ye, W. Design of architectured composite materials with an efficient, adaptive artificial neural network-based
generative design method. Acta. Mater. 2022, 225, 117548. DOI
29. Debnath, A.; Krajewski, A. M.; Sun, H.; et al. Generative deep learning as a tool for inverse design of high entropy refractory alloys. J.
Mater. Inf. 2021, 1, 3. DOI
30. Tian, Y.; Li, T.; Pang, J.; et al. Materials design with target-oriented Bayesian optimization. npj. Comput. Mater. 2025, 11, 1704. DOI
31. Qin, Z.; Zhao, H.; Zhang, S.; et al. Design of high performance Cu-Ni-Si alloys via a multiobjective strategy based on machine
learning. Mater. Today. Commun. 2024, 39, 108833. DOI
32. Padhy, S. P.; Chaudhary, V.; Lim, Y. F.; et al. Experimentally validated inverse design of multi-property Fe-Co-Ni alloys. iScience
2024, 27, 109723. DOI PubMed PMC
33. Ghorbani, M.; Boley, M.; Nakashima, P. N. H.; Birbilis, N. An active machine learning approach for optimal design of magnesium
alloys using Bayesian optimisation. Sci. Rep. 2024, 14, 8299. DOI PubMed PMC
34. Mazaheri, A.; Kamkar, S. Webpage saliency prediction using a single layer support vector regressor. In 2024 10th International
Conference on Artificial Intelligence and Robotics (QICAR). 2024. pp. 80-3. DOI
35. Mi, X.; Tian, L.; Tang, A.; et al. A reverse design model for high-performance and low-cost magnesium alloys by machine learning.
Comput. Mater. Sci. 2022, 201, 110881. DOI
36. Uhrig, R. E. Introduction to artificial neural networks. In Proceedings of IECON ’95 - 21st Annual Conference on IEEE Industrial
Electronics, Orlando, USA. November 06-10, 1995. IEEE; 1995. pp. 33-7. DOI
37. Choi, R. Y.; Coyner, A. S.; Kalpathy-Cramer, J.; Chiang, M. F.; Campbell, J. P. Introduction to machine learning, neural networks, and
deep learning. Transl. Vis. Sci. Technol. 2020, 9, 14. DOI PubMed PMC
38. Li, Z.; Li, S.; Birbilis, N. A machine learning-driven framework for the property prediction and generative design of multiple principal
element alloys. Mater. Today. Commun. 2024, 38, 107940. DOI
39. Liu, B.; Mazumder, R. Randomization can reduce both bias and variance: a case study in random forests. arXiv 2024, arXiv:2402.
12668. Available online: https://doi.org/10.48550/arXiv.2402.12668. (accessed 10 Nov 2025)
40. 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, 2016. Association for Computing Machinery; 2016. pp. 785-94. DOI
41. Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE. Trans. Evol.