Page 74 - Read Online
P. 74
Page 14 of 15 Hu et al. J Mater Inf 2023;3:1 I http://dx.doi.org/10.20517/jmi.2022.28
3. Wang WH, Dong C, Shek CH. Bulk metallic glasses. Mater Sci Eng: R Rep 2004;44:45–89. DOI
4. Ding S, Liu Y, Li Y, et al. Combinatorial development of bulk metallic glasses. Nat Mater 2014;13:494–500. DOI
5. Li MX, Sun YT, Wang C, et al. Data-driven discovery of a universal indicator for metallic glass forming ability. Nat Mater 2022;21:165–72.
DOI
6. Wang WH. Dynamic relaxations and relaxation-property relationships in metallic glasses. Prog Mater Sci 2019;106:100561. DOI
7. Qiao JC, Wang Q, Pelletier JM, et al. Structural heterogeneities and mechanical behavior of amorphous alloys. Prog Mater Sci
2019;104:250–329. DOI
8. Zhang LC, Jia Z, Lyu F, Liang SX, Lu J. A review of catalytic performance of metallic glasses in wastewater treatment: Recent progress
and prospects. Prog Mater Sci 2019;105:100576. DOI
9. Li Y, Zhao S, Liu Y, Gong P, Schroers J. How many bulk metallic glasses are there? ACS Comb Sci 2017;19:687–93. DOI
10. Kurtuldu G, Shamlaye KF, Löffler JF. Metastable quasicrystal-induced nucleation in a bulk glass-forming liquid. Proc Natl Acad Sci USA
2018;115:6123–8. DOI
11. Tanaka H. Bond orientational order in liquids: Towards a unified description of water-like anomalies, liquid-liquid transition, glass
transition, and crystallization. Eur Phys J E 2012;35:1–84. DOI
12. Xie Y, Sohn S, Wang M, et al. Supercluster-coupled crystal growth in metallic glass forming liquids. Nat Commun 2019;10:915. DOI
13. Hu YC, Schroers J, Shattuck MD, O’Hern CS. Tuning the glass-forming ability of metallic glasses through energetic frustration. Phys
Rev Mater 2019;3:085602. DOI
14. Hu YC, Zhang K, Kube SA, et al. Glass formation in binary alloys with different atomic symmetries. Phys Rev Mater 2020;4:105602.
DOI
15. Hu YC, Tanaka H. Physical origin of glass formation from multicomponent systems. Sci Adv 2020;6:eabd2928. DOI
16. Hu YC, Jin W, Schroers J, Shattuck MD, O’Hern CS. Glass-forming ability of binary Lennard-Jones systems. Phys Rev Mater
2022;6:075601. DOI
17. Hu YC, Tanaka H. Revealing the role of liquid preordering in crystallisation of supercooled liquids. Nat Commun 2022;13:4519. DOI
18. Cheng YQ, Ma E. Atomic-level structure and structure–property relationship in metallic glasses. Prog Mater Sci 2011;56:379–473. DOI
19. Laws KJ, Miracle DB, Ferry M. A predictive structural model for bulk metallic glasses. Nat Commun 2015 Dec;6:8123. DOI
20. Steinhardt PJ, Nelson DR, Ronchetti M. Bond-orientational order in liquids and glasses. Phys Rev B 1983;28:784. DOI
21. Rycroft CH, Grest GS, Landry JW, Bazant MZ. Analysis of granular flow in a pebble-bed nuclear reactor. Phys Rev E 2006;74:021306.
DOI
22. Rein ten Wolde P, Ruiz-Montero MJ, Frenkel D. Numerical calculation of the rate of crystal nucleation in a Lennard-Jones system at
moderate undercooling. J Chem Phys 1996;104:9932–47. DOI
23. Russo J, Tanaka H. The microscopic pathway to crystallization in supercooled liquids. Sci Rep 2012;2:505. DOI
24. Takeuchi A, Inoue A. Classification of bulk metallic glasses by atomic size difference, heat of mixing and period of constituent elements
and its application to characterization of the main alloying element. Mater Trans 2005;46:2817–29. DOI
25. Li Y, Guo Q, Kalb JA, Thompson CV. Matching glass-forming ability with the density of the amorphous phase. Science 2008;322:1816–9.
DOI
26. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature 2018;559:547–55.
DOI
27. Tshitoyan V, Dagdelen J, Weston L, et al. Unsupervised word embeddings capture latent knowledge from materials science literature.
Nature 2019;571:95. DOI
28. Friederich P, Häse F, Proppe J, Aspuru-Guzik A. Machine-learned potentials for next-generation matter simulations. Nat Mater 2021
Jun;20:750–61. DOI
29. Raccuglia P, Elbert KC, Adler PDF, et al. Machine-learning-assisted materials discovery using failed experiments. Nature 2016;533:73–6.
DOI
30. Hart GLW, Mueller T, Toher C, Curtarolo S. Machine learning for alloys. Nat Rev Mater 2021 Aug;6:730–55. DOI
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 Aug;7:1–10. DOI
32. Sun YT, Bai HY, Li MZ, Wang WH. Machine learning approach for prediction and understanding of glass-forming ability. J Phys Chem
Lett 2017 Jul;8:3434–39. DOI
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. DOI
34. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res 2011;12:2825–30. Available
from: https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf?ref=https:/ [Last accessed on 10 Feb 2023]
35. Kob W, Andersen HC. Testing mode-coupling theory for a supercooled binary Lennard-Jones mixture I: The van Hove correlation function.
Phys Rev E 1995;51:4626–41. DOI
36. Pedersen UR, Schrøder TB, Dyre JC. Phase diagram of Kob-Andersen-type binary Lennard-Jones mixtures. Phys Rev Lett
2018;120:165501. DOI
37. Ingebrigtsen TS, Dyre JC, Schrøder TB, Royall CP. Crystallization instability in glass-forming mixtures. Phys Rev X 2019;9:031016.
DOI
38. Iwasaki Y, Takeuchi I, Stanev V, et al. Machine-learning guided discovery of a new thermoelectric material. Sci Rep 2019;9:2751. DOI
39. Sarker S, Tang-Kong R, Schoeppner R, et al. Discovering exceptionally hard and wear-resistant metallic glasses by combining machine-