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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: zhyang@illinois.edu
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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

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