REFERENCES

1. Klement W, Willens RH, Duwez P. Non-crystalline structure in solidified gold-silicon alloys. Nature 1960;187:869-70.

2. Wang WH. The elastic properties, elastic models and elastic perspectives of metallic glasses. Prog Mater Sci 2012;57:487-656.

3. Ashby M, Greer A. Metallic glasses as structural materials. Scr Mater 2006;54:321-6.

4. Scully JR, Gebert A, Payer JH. Corrosion and related mechanical properties of bulk metallic glasses. J Mater Res 2006;22:302-13.

5. Sharma A, Zadorozhnyy V. Review of the recent development in metallic glass and its composites. Metals 2021;11:1933.

6. Inoue A. Stabilization of metallic supercooled liquid and bulk amorphous alloys. Acta Mater 2000;48:279-306.

7. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature 2018;559:547-55.

8. Sun YT, Bai HY, Li MZ, Wang WH. Machine learning approach for prediction and understanding of glass-forming ability. J Phys Chem Lett 2017;8:3434-9.

9. Dasgupta A, Broderick SR, Mack C, et al. Probabilistic assessment of glass forming ability rules for metallic glasses aided by automated analysis of phase diagrams. Sci Rep 2019;9:357.

10. Peng L, Long Z, Zhao M. Determination of glass forming ability of bulk metallic glasses based on machine learning. Comput Mater Sci 2021;195:110480.

11. Xiong J, Shi S, Zhang T. Machine learning prediction of glass-forming ability in bulk metallic glasses. Comput Mater Sci 2021;192:110362.

12. Afflerbach BT, Schultz L, Perepezko JH, Voyles PM, Szlufarska I, Morgan D. Molecular simulation-derived features for machine learning predictions of metal glass forming ability. Comput Mater Sci 2021;199:110728.

13. Keong K, Sha W, Malinov S. Artificial neural network modelling of crystallization temperatures of the Ni-P based amorphous alloys. Mater Sci Eng A 2004;365:212-8.

14. Cai A, Xiong X, Liu Y, An W, Tan J. Artificial neural network modeling of reduced glass transition temperature of glass forming alloys. Appl Phys Lett 2008;92:111909.

15. Cai AH, Liu Y, An WK, et al. Prediction of critical cooling rate for glass forming alloys by artificial neural network. Mater Des 2013;52:671-6.

16. Deng B, Zhang Y. Critical feature space for predicting the glass forming ability of metallic alloys revealed by machine learning. Chem Phys 2020;538:110898.

17. Mastropietro DG, Moya JA. Design of Fe-based bulk metallic glasses for maximum amorphous diameter (Dmax) using machine learning models. Comput Mater Sci 2021;188:110230.

18. Majid A, Ahsan SB, Tariq NUH. Modeling glass-forming ability of bulk metallic glasses using computational intelligent techniques. Appl Soft Comput J 2015;28:569-78.

19. Li J, Chen T, Zekiy AO. Correlative study between elastic modulus and glass formation in ZrCuAl(X) amorphous system using a machine learning approach. Appl Phys A 2021:127.

20. Samavatian M, Gholamipour R, Samavatian V. Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach. Comput Mater Sci 2021;186:110025.

21. Xiong J, Zhang T, Shi S. Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses. MRS Commun 2019;9:576-85.

22. Xiong J, Shi S, Zhang T. A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys. Mater Des 2020;187:108378.

23. Ward L, O’keeffe SC, Stevick J, Jelbert GR, Aykol M, Wolverton C. A machine learning approach for engineering bulk metallic glass alloys. Acta Mater 2018;159:102-11.

24. Liu X, Li X, He Q, et al. Machine learning-based glass formation prediction in multicomponent alloys. Acta Mater 2020;201:182-90.

25. Chen T, Rajiman R, Elveny M, et al. Engineering of novel Fe-based bulk metallic glasses using a machine learning-based approach. Arab J Sci Eng 2021;46:12417-25.

26. Li Z, Long Z, Lei S, Zhang T, Liu X, Kuang D. Predicting the glass formation of metallic glasses using machine learning approaches. Comput Mater Sci 2021;197:110656.

27. Zhang Y, Xing G, Sha Z, Poh L. A two-step fused machine learning approach for the prediction of glass-forming ability of metallic glasses. J Alloys Compd 2021;875:160040.

28. Cai A, Xiong X, Liu Y, An W, Tan J, Luo Y. Artificial neural network modeling for undercooled liquid region of glass forming alloys. Comput Mater Sci 2010;48:109-14.

29. Jeon J, Seo N, Kim H, et al. Inverse design of Fe-based bulk metallic glasses using machine learning. Metals 2021;11:729.

30. Xiong J, Shi S, Zhang T. Machine learning of phases and mechanical properties in complex concentrated alloys. J Mater Sci Technol 2021;87:133-42.

31. Zhou ZQ, He QF, Liu XD, et al. Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning. npj Comput Mater 2021;7:138.

32. Zhang Y, Ling C. A strategy to apply machine learning to small datasets in materials science. npj Comput Mater 2018;4:25.

33. Ren F, Ward L, Williams T, et al. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci Adv 2018;4:eaaq1566.

34. Ren B, Long Z, Deng R. A new criterion for predicting the glass-forming ability of alloys based on machine learning. Comput Mater Sci 2021;189:110259.

35. Kawazoe Y, Carow-Watamura U, Louzguine DV. Phase diagrams and physical properties of nonequilibrium alloys. 1st ed. Berlin Heidelberg: Springer; 1997.

36. Kawazoe Y. Nonequilibrium phase diagrams of ternary amorphous alloys. Berlin Heidelberg: Springer; 1997.

37. Lu Z, Liu C. A new glass-forming ability criterion for bulk metallic glasses. Acta Mater 2002;50:3501-12.

38. Lu Z, Bei H, Liu C. Recent progress in quantifying glass-forming ability of bulk metallic glasses. Intermetallics 2007;15:618-24.

39. Long Z, Wei H, Ding Y, Zhang P, Xie G, Inoue A. A new criterion for predicting the glass-forming ability of bulk metallic glasses. J Alloys Compd 2009;475:207-19.

40. Guo S, Liu C. Phase stability in high entropy alloys: formation of solid-solution phase or amorphous phase. Prog Nat Sci Mater Int 2011;21:433-46.

41. Tripathi MK, Chattopadhyay P, Ganguly S. Multivariate analysis and classification of bulk metallic glasses using principal component analysis. Comput Mater Sci 2015;107:79-87.

42. Chen T, Yu S, Sajjadifar S. Engineering of new Mg-based glassy compositions by a computational intelligence model. Mater Lett 2021;290:129441.

43. Johnson WL, Na JH, Demetriou MD. Quantifying the origin of metallic glass formation. Nat Commun 2016;7:10313.

44. Ghiringhelli LM, Vybiral J, Levchenko SV, Draxl C, Scheffler M. Big data of materials science: critical role of the descriptor. Phys Rev Lett 2015;114:105503.

45. Mizutani U. Hume-Rothery rules for structurally complex alloy phases. MRS Bull 2012;37:169-169.

46. Greer AL. Metallic glasses. Science 1995;267:1947-53.

47. Zhou Z, Zhou Y, He Q, Ding Z, Li F, Yang Y. Machine learning guided appraisal and exploration of phase design for high entropy alloys. npj Comput Mater 2019;5:128.

48. Raschka S. MLxtend: providing machine learning and data science utilities and extensions to Python’s scientific computing stack. JOSS 2018;3:638.

49. Palma-mendoza R, Rodriguez D, de-Marcos L. Distributed ReliefF-based feature selection in Spark. Knowl Inf Syst 2018;57:1-20.

50. Feng S, Fu H, Zhou H, Wu Y, Lu Z, Dong H. A general and transferable deep learning framework for predicting phase formation in materials. npj Comput Mater 2021;7:10.

51. Suryanarayana C, Seki I, Inoue A. A critical analysis of the glass-forming ability of alloys. J Non Cryst Solids 2009;355:355-60.

52. Inoue A, Takeuchi A. Recent development and application products of bulk glassy alloys. Acta Mater 2011;59:2243-67.