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Robustness of Dynamical Cluster Analysis in a Glass-Forming Metallic Liquid using an Unsupervised Machine Learning Algorithm

Published online by Cambridge University Press:  29 January 2016

Abhishek Jaiswal  and
Yang Zhang
Abhishek Jaiswal
Affiliation:
Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A.
Yang Zhang*
Affiliation:
Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A. Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A.
*
*To whom correspondence should be addressed. Email: [email protected]
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Abstract

We performed dynamical cluster analysis in a Cu-Zr-Al based glass-forming metallic liquid using an unsupervised machine learning algorithm. The size of the dynamical clusters is used to quantify the onset of cooperative dynamics as the underlying mechanism leading to the Arrhenius dynamic crossover in transport coefficients of the metallic liquid. This technique is useful to directly visualize dynamical clusters and quantify their sizes upon cooling. We demonstrate the robustness of this algorithm by performing sensitivity analysis against two key parameters: number of mobility groups and inconsistency coefficient of the hierarchical cluster tree. The results elucidate the optimized range of values for both of these parameters that capture the underlying physical picture of increasing cooperative dynamics appropriately.

Keywords

liquidsimulationamorphous
Type
Articles
Information
MRS Advances , Volume 1 , Issue 26: Theory, Characterization, and Modeling , 2016 , pp. 1929 - 1934
Copyright
Copyright © Materials Research Society 2016 

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References

REFERENCES

Greer, A. L., Science 267, 1947 (1995).Google Scholar
Johnson, W. L., MRS Bull. 24, 42 (1999).CrossRefGoogle Scholar
Inoue, A., Acta Mater. 48, 279 (2000).Google Scholar
Cheng, Y. Q. and Ma, E., Prog. Mater. Sci. 56, 379 (2011).Google Scholar
Chen, M., NPG Asia Mater. 3, 82 (2011).Google Scholar
Lačević, N., Starr, F. W., Schrøder, T. B., and Glotzer, S. C., J. Chem. Phys. 119, 7372 (2003).Google Scholar
Ediger, M. D., Annu. Rev. Phys. Chem. 51, 99 (2000).Google Scholar
Berthier, L. and Biroli, G., Rev. Mod. Phys. 83, 587 (2011).Google Scholar
Iwashita, T., Nicholson, D. M., and Egami, T., Phys. Rev. Lett. 110, 205504 (2013).Google Scholar
Blodgett, M. E., Egami, T., Nussinov, Z., and Kelton, K. F., Sci. Rep. 5, 13837 (2015).Google Scholar
Jaiswal, A., Egami, T., and Zhang, Y., Phys. Rev. B 91, 134204 (2015).Google Scholar
Plimpton, S., J. Comput. Phys. 117, 1 (1995).Google Scholar
Cheng, Y. Q., Ma, E., and Sheng, H. W., Phys. Rev. Lett. 102, 245501 (2009).Google Scholar
Matlab and Statistics Toolbox Release 2014a (The MathWorks, Inc., Natick, Massachusetts, USA, 2014).Google Scholar
Jain, A. K. and Dubes, R. C., Algorithms for Clustering Data (Prentice-Hall, Inc., Eaglewood Cliffs, NJ, 1988).Google Scholar
Zahn, C. T., IEEE Trans. Comput. C-20, 68 (1971).CrossRefGoogle Scholar
Brillo, J., Pommrich, A. I., and Meyer, A., Phys. Rev. Lett. 107, (2011).Google Scholar
Jaiswal, A., Podlesynak, A., Ehlers, G., Mills, R., O’Keeffe, S., Stevick, J., Kempton, J., Jelbert, G., Dmowski, W., Lokshin, K., Egami, T., and Zhang, Y., Phys. Rev. B 92, 024202 (2015).Google Scholar