Hostname: page-component-669899f699-7xsfk Total loading time: 0 Render date: 2025-04-24T20:49:09.736Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  24 April 2019

Jie Xiong Open the ORCID record for Jie Xiong [Opens in a new window] ,
Tong-Yi Zhang  and
San-Qiang Shi
Jie Xiong
Affiliation:
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Tong-Yi Zhang*
Affiliation:
Materials Genome Institute, Shanghai University, Shanghai, China
San-Qiang Shi
Affiliation:
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
*
Address all correspondence to Tong-Yi Zhang at [email protected]
Get access

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.

Type
Artificial Intelligence Research Letters
Information
MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 576 - 585
Copyright
Copyright © Materials Research Society 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1.Wang, W.H.: The elastic properties, elastic models and elastic perspectives of metallic glasses. Prog. Mater. Sci. 57, 487–656 (2012).Google Scholar
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).Google Scholar
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, 73–76 (2016).Google Scholar
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, 1–20 (2018).Google Scholar
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, 3434–3439 (2017).Google Scholar
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, 102–111 (2018).Google Scholar
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).Google Scholar
8.Liu, Z.Q. and Zhang, Z.F.: Strengthening and toughening metallic glasses: The elastic perspectives and opportunities. J. Appl. Phys. 115, 163505 (2014).Google Scholar
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, 1645–1660 (2018).Google Scholar
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).Google Scholar
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).Google Scholar
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, 6543–6549 (2006).Google Scholar
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, 2984–2986 (2003).Google Scholar
14.Inoue, A., and Takeuchi, A.: Recent development and application products of bulk glassy alloys. Acta Mater. 59, 2243–2267 (2011).Google Scholar
15.Laws, K.J., Miracle, D.B., and Ferry, M.: A predictive structural model for bulk metallic glasses. Nat. Commun. 6, 8123 (2015).Google Scholar
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, 1188–1192 (2010).Google Scholar
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, 93–100 (2004).Google Scholar
18.Pyykkö, P. and Atsumi, M.: Molecular single-bond covalent radii for elements 1–118. Chem. Eur. J. 15, 186–197 (2009).Google Scholar
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. 513–515.Google Scholar
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, 2832–2838 (2008).Google Scholar
21.Miracle, D.B.: A physical model for metallic glass structures: An introduction and update. JOM 64, 846–855 (2012).Google Scholar
22.Guyon, I. and Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2011).Google Scholar
23.James, G., Witten, D., Tibshirani, R., and Hastie, T.: An Introduction to Statistical Learning with Applications in R (Springer, New York, 2013).Google Scholar
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, 16–22 (2013).Google Scholar
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, 2243–2247 (2005).Google Scholar
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, 1030–1032 (2003).Google Scholar
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, 187–190 (2006).Google Scholar
Supplementary material: File

Xiong et al. supplementary material

Tables S1-S3

Download Xiong et al. supplementary material(File)
File 308 KB