REFERENCES

1. Russell, A. Ductility in intermetallic compounds. Adv. Eng. Mater. 2003, 5, 629-39.

2. Taub, A. I.; Fleischer, R. L. Intermetallic compounds for high-temperature structural use. Science 1989, 243, 616-21.

3. Dshemuchadse, J.; Steurer, W. Some statistics on intermetallic compounds. Inorg. Chem. 2015, 54, 1120-8.

4. Yamaguchi, M.; Inui, H.; Ito, K. High-temperature structural intermetallics. Acta. Mater. 2000, 48, 307-22.

5. Kimura, Y.; Pope, D. P. Ductility and toughness in intermetallics. Intermetallics 1998, 6, 567-71.

6. Zhu, D.; Pan, K.; Wu, H.; et al. Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning. J. Mater. Res. Technol. 2023, 26, 8836-45.

7. Ravindran, P.; Asokamani, R. Correlation between electronic structure, mechanical properties and phase stability in intermetallic compounds. Bull. Mater. Sci. 1997, 20, 613-22.

8. Kim, S. H.; Kim, H.; Kim, N. J. Brittle intermetallic compound makes ultrastrong low-density steel with large ductility. Nature 2015, 518, 77-9.

9. Crawley, A. F. Densities of liquid metals and alloys. Int. Metall. Rev. 1974, 19, 32-48.

10. Fleischer, R. L. High-strength, high-temperature intermetallic compounds. J. Mater. Sci. 1987, 22, 2281-8.

11. Stoloff, N.; Liu, C.; Deevi, S. Emerging applications of intermetallics. Intermetallics 2000, 8, 1313-20.

12. Uenishi, K.; Kobayashi, K. Processing of intermetallic compounds for structural applications at high temperature. Intermetallics 1996, 4, S95-101.

13. Paul, A. R.; Mukherjee, M.; Singh, D. A critical review on the properties of intermetallic compounds and their application in the modern manufacturing. Cryst. Res. Technol. 2022, 57, 2100159.

14. Fatima, B.; Chouhan, S. S.; Acharya, N.; Sanyal, S. P. Density functional study of XRh (X=Sc, Y, Ti and Zr) intermetallic compounds. Comput. Mater. Sci. 2014, 89, 205-15.

15. Lu, Z. W.; Wei, S.; Zunger, A.; Frota-Pessoa, S.; Ferreira, L. G. First-principles statistical mechanics of structural stability of intermetallic compounds. Phys. Rev. B. Condens. Matter. 1991, 44, 512-44.

16. Zhu, D.; Pan, K.; Wu, Y.; et al. Improved material descriptors for bulk modulus in intermetallic compounds via machine learning. Rare. Met. 2023, 42, 2396-405.

17. Oliynyk, A. O.; Mar, A. Discovery of intermetallic compounds from traditional to machine-learning approaches. Acc. Chem. Res. 2018, 51, 59-68.

18. Medasani, B.; Gamst, A.; Ding, H.; et al. Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning. npj. Comput. Mater. 2016, 2, 1.

19. Nie, M.; Chen, D.; Wang, D. Reinforcement learning on graphs: a survey. IEEE. Trans. Emerg. Top. Comput. Intell. 2023, 7, 1065-82.

20. Xie, T.; Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 2018, 120, 145301.

21. Park, C. W.; Wolverton, C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery. Phys. Rev. Mater. 2020, 4, 063801.

22. Reiser, P.; Neubert, M.; Eberhard, A.; et al. Graph neural networks for materials science and chemistry. Commun. Mater. 2022, 3, 93.

23. Dreger, M.; Eslamibidgoli, M. J.; Eikerling, M. H.; Malek, K. Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations. J. Mater. Inf. 2023, 3, 2.

24. Choudhary, K.; Decost, B. Atomistic line graph neural network for improved materials property predictions. npj. Comput. Mater. 2021, 7, 650.

25. Wu, X.; Wang, H.; Gong, Y.; et al. Graph neural networks for molecular and materials representation. J. Mater. Inf. 2023, 3, 12.

26. Fung, V.; Zhang, J.; Juarez, E.; Sumpter, B. G. Benchmarking graph neural networks for materials chemistry. npj. Comput. Mater. 2021, 7, 554.

27. Dai, M.; Demirel, M. F.; Liang, Y.; Hu, J. Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials. npj. Comput. Mater. 2021, 7, 574.

28. Jain, A.; Ong, S. P.; Hautier, G.; et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL. Materials. 2013, 1, 011002.

29. Chicco, D.; Warrens, M. J.; Jurman, G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ. Comput. Sci. 2021, 7, e623.

30. Chai, T.; Draxler, R. R. Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature. Geosci. Model. Dev. 2014, 7, 1247-50.

31. Zhu, D.; Wu, H.; Hou, F.; et al. A transfer learning strategy for tensile strength prediction in austenitic stainless steel across temperatures. Scr. Mater. 2024, 251, 116210.

32. Bradley, A. P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern. Recognit. 1997, 30, 1145-59.

33. Yoshida Laboratory. XenonPy. 2020. https://github.com/yoshida-lab/XenonPy. (accessed 2025-02-05).

34. Fessler, J.; Sutton, B. Nonuniform fast fourier transforms using min-max interpolation. IEEE. Trans. Signal. Process. 2003, 51, 560-74.

35. Isayev, O.; Oses, C.; Toher, C.; Gossett, E.; Curtarolo, S.; Tropsha, A. Universal fragment descriptors for predicting properties of inorganic crystals. Nat. Commun. 2017, 8, 15679.

36. Ioffe, S.; Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv2015, arXiv:1502.03167. Available online: https://doi.org/10.48550/arXiv.1502.03167. (accessed 5 Feb 2025)

37. Du, J.; Zhang, S.; Wu, G.; Moura, J. M. F.; Kar, S. Topology adaptive graph convolutional networks. arXiv2017, arXiv:1710.10370. Available online: https://doi.org/10.48550/arXiv.1710.10370. (accessed 5 Feb 2025)

38. Belkina, A. C.; Ciccolella, C. O.; Anno, R.; Halpert, R.; Spidlen, J.; Snyder-Cappione, J. E. Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nat. Commun. 2019, 10, 5415.