Robustness of dynamical cluster analysis in a glass-forming metallic liquid using an unsupervised machine learning algorithm

Abhishek Jaiswal, Yang Zhang

Research output: Contribution to journalArticlepeer-review

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. MRS Advances

Original languageEnglish (US)
Pages (from-to)1929-1934
Number of pages6
JournalMRS Advances
Volume1
Issue number26
DOIs
StatePublished - 2016

Keywords

  • amorphous
  • liquid
  • simulation

ASJC Scopus subject areas

  • Mechanical Engineering
  • Mechanics of Materials
  • Materials Science(all)
  • Condensed Matter Physics

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