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Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses

  • Jie Xiong (a1), Tong-Yi Zhang (a2) and San-Qiang Shi (a1)

Abstract

There is a genuine need to shorten the development period for new materials with desired properties. In this work, machine learning (ML) was conducted on a dataset of the elastic moduli of 219 bulk-metallic glasses (BMGs) and another dataset of the critical casting diameters (Dmax) of 442 BMGs. The resulting ML model predicted the moduli and Dmax of BMGs in good agreement with most experimentally measured values, and the model even identified some errors reported in the literature. This work indicates the great potential of ML in design of advanced materials with target properties.

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Corresponding author

Address all correspondence to Tong-Yi Zhang at mezhangt@ust.hk

References

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1.Wang, W.H.: The elastic properties, elastic models and elastic perspectives of metallic glasses. Prog. Mater. Sci. 57, 487656 (2012).
2.Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., and Kim, C.: Machine learning in materials informatics: Recent applications and prospects. NPJ Comput. Mater. 3, 54 (2017).
3.Raccuglia, P., Elbert, K.C., Adler, P.D.F., Falk, C., Wenny, M.B., Mollo, A., Zeller, M., Friedler, S.A., Schrier, J., and Norquist, A.J.: Machine-learning-assisted materials discovery using failed experiments. Nature 533, 7376 (2016).
4.Ramakrishna, S., Zhang, T.Y., Lu, W.C., Qian, Q., Low, J.S.C., Yune, J.H.R., Tan, D.Z.L., Bressan, S., Sanvito, S., and Kalidindi, S.R.: Materials informatics. J. Intell. Manuf. 29, 120 (2018).
5.Sun, Y.T., Bai, H.Y., Li, M.Z., and Wang, W.H.: Machine learning approach for prediction and understanding of glass-forming ability. J. Phys. Chem. Lett. 8, 34343439 (2017).
6.Ward, L., O'Keeffe, S.C., Stevick, J., Jelbert, G.R., Aykol, M., and Wolverton, C.: A machine learning approach for engineering bulk metallic glass alloys. Acta Mater. 159, 102111 (2018).
7.Ren, F., Ward, L., Williams, T., Laws, K.J., Wolverton, C., Hattrick-Simpers, J., and Mehta, A.: Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci. Adv. 4, 1566 (2018).
8.Liu, Z.Q. and Zhang, Z.F.: Strengthening and toughening metallic glasses: The elastic perspectives and opportunities. J. Appl. Phys. 115, 163505 (2014).
9.Long, Z., Liu, W., Zhong, M., Yun, Z., Zhao, M., Liao, G., and Chen, Z.: A new correlation between the characteristics temperature and glass-forming ability for bulk metallic glasses. J. Therm. Anal. Calorim. 3, 16451660 (2018).
10.Wang, J.Q., Wang, W.H., Yu, H.B., and Bai, H.Y.: Correlations between elastic moduli and molar volume in metallic glasses. Appl. Phys. Lett. 94, 121904 (2009).
11.Zhao, K., Bai, Z., Zhang, L., and Liu, G.: Correlation between atomic size and elastic properties/glass transition temperature in metallic glasses. Sci. China: Phys., Mech. Astron. 60, 106121 (2017).
12.Xia, M.X., Meng, Q.G., Zhang, S.G., Ma, C.L., and Li, J.G.: Thermodynamic characteristics of metallic glass-forming liquids. Acta Phys. Sin. 55, 65436549 (2006).
13.Jiang, Q., Chi, B.Q., and Li, J.C.: A valence electron concentration criterion for glass-formation ability of metallic liquids. Appl. Phys. Lett. 82, 29842986 (2003).
14.Inoue, A., and Takeuchi, A.: Recent development and application products of bulk glassy alloys. Acta Mater. 59, 22432267 (2011).
15.Laws, K.J., Miracle, D.B., and Ferry, M.: A predictive structural model for bulk metallic glasses. Nat. Commun. 6, 8123 (2015).
16.Peng, H., Li, S.S., and Huang, T.Y.: A glass forming ability indicator of Mg-based metallic glasses using atomic radius and electronegativity. J. Tsinghua Univ. 8, 11881192 (2010).
17.Lu, Z.P., Liu, C.T., and Dong, Y.D.: Effects of atomic bonding nature and size mismatch on thermal stability and glass-forming ability of bulk metallic glasses. J. Non-Cryst. Solids 341, 93100 (2004).
18.Pyykkö, P. and Atsumi, M.: Molecular single-bond covalent radii for elements 1–118. Chem. Eur. J. 15, 186197 (2009).
19.Huheey, J.E., Keiter, E.A., and Keiter, R.L.: Inorganic Chemistry: Principles of Structure and Reactivity, 4th ed. (HarperCollins, New York, 1993), pp. 513515.
20.Cordero, B., Gómez, V., Platero-Prats, A.E., Revés, M., Echeverría, J., Cremades, E., Barragán, F., and Alvarez, S.: Covalent radii revisited. J. Chem. Soc., Dalton Trans. 21, 28322838 (2008).
21.Miracle, D.B.: A physical model for metallic glass structures: An introduction and update. JOM 64, 846855 (2012).
22.Guyon, I. and Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 11571182 (2011).
23.James, G., Witten, D., Tibshirani, R., and Hastie, T.: An Introduction to Statistical Learning with Applications in R (Springer, New York, 2013).
24.Zhang, G. and Ge, H.: Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins. Comput. Biol. Chem. 46, 1622 (2013).
25.Xi, X.K., Li, S., Wang, R.J., Zhao, D.Q., Pan, M.X., and Wang, W.H.: Bulk scandium-based metallic glasses. J. Mater. Res. 20, 22432247 (2005).
26.Choi-Yim, H., Xu, D., and Johnson, W.L.: Ni-based bulk metallic glass formation in the Ni–Nb–Sn and Ni–Nb–Sn–X (X = B, Fe, Cu) alloy systems. Appl. Phys. Lett. 82, 10301032 (2003).
27.Choi-Yim, H., Xu, D., Lind, M.L., Löffler, J.F., and Johnson, W.L.: Structure and mechanical properties of bulk glass-forming Ni-Nb-Sn alloys. Scr. Mater. 54, 187190 (2006).
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Xiong et al. supplementary material
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