REFERENCES
1. Su, P.; Diao, C.; Tang, Y.; Yan, C. Review on progress of rare earth molecule-based functional materials in 2025. J. Rare. Earths. 2026, 44, 2249-58.
2. Prados, T. M.; Barroso, T. L. C. T.; Forster-Carneiro, T.; Lovón-Canchumani, G. A.; Colpini, L. M. S. Life cycle assessment and circular economy in the production of rare earth magnets: an updated and comprehensive review. Clean. Technol. Environ. Policy. 2025, 27, 471-94.
3. Wang, Q.; Wang, L.; Zhao, S.; Li, F.; Chen, W.; Wang, P. A critical life cycle assessment of present and potential rare earth circularity routes from permanent magnets. Resour. Conserv. Recycl. 2025, 215, 108106.
4. Fritz, A. G.; Tarka, T. J.; Mauter, M. S. Assessing the economic viability of unconventional rare earth element feedstocks. Nat. Sustain. 2023, 6, 1103-12.
5. Pei, K.; Zhang, X.; Lin, M.; Yan, A. Effects of Ce-substitution on magnetic properties and microstructure of Nd–Pr–Fe–B melt-spun powders. J. Magn. Magn. Mater. 2016, 398, 96-100.
6. Zhu, M.; Han, R.; Li, W.; et al. An enhanced coercivity for (CeNdPr)–Fe–B sintered magnet prepared by structure design. IEEE. Trans. Magn. 2015, 51, 1-4.
7. Xi, L.; Li, A.; Feng, H.; Tan, M.; Zhu, M.; Li, W. Phase structure of Al doped Ce-rich alloys and its effect on magnetic properties of sintered Ce-Fe-B magnets. J. Alloys. Compd. 2019, 782, 723-8.
8. Skoug, E. J.; Meyer, M.; Pinkerton, F.; Tessema, M. M.; Haddad, D.; Herbst, J. Crystal structure and magnetic properties of
9. Feng, Q.; Huang, D.; Zhao, L.; et al. Overcoming refractory REFe2 phase bottleneck in high‐Ce Nd–Ce–Fe–B magnets via praseodymium‐induced phase transformation: enabling liquid‐phase grain boundary diffusion of dysprosium. Adv. Funct. Mater. 2026, 36, e25964.
10. Cheng, M.; Fu, C. L.; Okabe, R.; et al. Artificial intelligence-driven approaches for materials design and discovery. Nat. Mater. 2026, 25, 174-90.
11. Qin, X.; Wang, Q.; Zhao, X.; et al. A property-oriented self-decision design strategy of low-alloyed rare earth-free magnesium alloys with a good strength-ductility synergy based on machine learning. J. Mater. Inf. 2025, 5, 13.
12. Jordan, M. I.; Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 2015, 349, 255-60.
13. Cheng, W. Magnetic remanence prediction of NdFeB magnets based on a novel machine learning intelligence approach using a particle swarm optimization support vector regression. Int. J. Softw. Sci. Comput. Intell. 2014, 6, 72-81.
14. Zhang, Y.; Xu, X. Predicting magnetic remanence of NdFeB magnets from composition. J. Supercond. Nov. Magn. 2021, 34, 2711-5.
15. Qiao, Z.; Dong, S.; Li, Q.; et al. Performance prediction models for sintered NdFeB using machine learning methods and interpretable studies. J. Alloys. Compd. 2023, 963, 171250.
16. Konak, A.; Coit, D. W.; Smith, A. E. Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 2006, 91, 992-1007.
17. Pereira, J. L. J.; Oliver, G. A.; Francisco, M. B.; Cunha, S. S.; Gomes, G. F. A review of multi-objective optimization: methods and algorithms in mechanical engineering problems. Arch. Comput. Methods. Eng. 2022, 29, 2285-308.
18. Nuhanović, A.; Hivziefendić, J.; Hadžimehmedović, A. Distribution network reconfiguration considering power losses and outages costs using genetic algorithm. J. Electr. Eng. 2013, 64, 265-71.
19. Dou, M.; Jia, R.; Li, G. An optimization model of sewage discharge in an urban wetland based on the multi-objective wolf pack algorithm. Environ. Monit. Assess. 2019, 191, 763.
20. Wang, Z.; Zhang, X.; Zhang, Z.; Sheng, D. Credit portfolio optimization: a multi-objective genetic algorithm approach. Borsa. Istanb. Rev. 2022, 22, 69-76.
21. Khatamsaz, D.; Vela, B.; Singh, P.; Johnson, D. D.; Allaire, D.; Arróyave, R. Multi-objective materials bayesian optimization with active learning of design constraints: design of ductile refractory multi-principal-element alloys. Acta. Mater. 2022, 236, 118133.
22. Zhang, G.; Wang, Y.; Meng, X.; et al. Life cycle assessment on the vanadium production process: a multi-objective assessment under environmental and economic perspectives. Resour. Conserv. Recycl. 2023, 192, 106926.
23. Zeraati, M.; Arshadizadeh, R.; Chauhan, N. P. S.; Sargazi, G. Genetic algorithm optimization of magnetic properties of Fe-Co-Ni nanostructure alloys prepared by the mechanical alloying by using multi-objective artificial neural networks for the core of transformer. Mater. Today. Commun. 2021, 28, 102653.
24. Wakjira, T. G.; Kutty, A. A.; Alam, M. S. A novel framework for developing environmentally sustainable and cost-effective ultra-high-performance concrete (UHPC) using advanced machine learning and multi-objective optimization techniques. Constr. Build. Mater. 2024, 416, 135114.
25. Pandey, D.; Niwaria, K.; Chourasia, B. Machine learning algorithms: a review. Int. Res. J. Eng. Technol. 2019, 6, 916-22. https://www.irjet.net/archives/V6/i2/IRJET-V6I2176.pdf. (accessed on 17 Aug 2026).
26. Nagelkerke, N. J. D. A note on a general definition of the coefficient of determination. Biometrika 1991, 78, 691-2.
27. Hodson, T. O. Root mean square error (RMSE) or mean absolute error (MAE): when to use them or not. Geosci. Model. Dev. Discuss. 2022, 15, 5481-7.
28. Belle, V.; Papantonis, I. Principles and practice of explainable machine learning. Front. Big. Data. 2021, 4, 688969.
29. Ikhlayel, M. Evaluation of the environmental impacts of rare earth elements production. Int. J. Environ. Stud. 2017, 74, 939-57.
30. Lee, J. C.; Wen, Z. Rare earths from mines to metals: comparing environmental impacts from China’s main production pathways. J. Ind. Ecol. 2017, 21, 1277-90.
31. Schreiber, A.; Marx, J.; Zapp, P.; Kuckshinrichs, W. Comparative life cycle assessment of neodymium oxide electrolysis in molten salt. Adv. Eng. Mater. 2020, 22, 1901206.
32. Ferreira, H.; Leite, M. G. P. A life cycle assessment study of iron ore mining. J. Clean. Prod. 2015, 108, 1081-91.
33. Farjana, S. H.; Huda, N.; Mahmud, M. P. Life cycle assessment of cobalt extraction process. J. Sustain. Min. 2019, 18, 150-61.
34. Liu, L. F.; Gao, F.; Wang, S. B.; Wang, Z. H.; Gong, X. Z.; Li, X. Q. Life cycle environmental impact assessment of amorphous alloy strips production. Mater. Sci. Forum. 2016, 847, 328-34.
35. Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE. Trans. Evol. Computat. 2002, 6, 182-97.
36. Etghani, M. M.; Shojaeefard, M. H.; Khalkhali, A.; Akbari, M. A hybrid method of modified NSGA-II and TOPSIS to optimize performance and emissions of a diesel engine using biodiesel. Appl. Therm. Eng. 2013, 59, 309-15.
37. Yoon, K. P.; Hwang, C. L. Multiple attribute decision making: an introduction. Sage Publications. Inc.: 1995.
38. Chakraborty, S. TOPSIS and modified TOPSIS: a comparative analysis. Decis. Anal. J. 2022, 2, 100021.
39. Hesthaven, J.; Ubbiali, S. Non-intrusive reduced order modeling of nonlinear problems using neural networks. J. Comput. Phys. 2018, 363, 55-78.
40. Zhao, C.; Jiang, Q.; Rehman, S. U.; et al. Effect of La–Y co-substitution on magnetic properties and microstructure of NdFeB alloy ribbons. J. Rare. Earths. 2022, 40, 1894-8.
