Literature DB >> 21959545

Detecting hidden spatial and spatio-temporal structures in glasses and complex physical systems by multiresolution network clustering.

P Ronhovde1, S Chakrabarty, D Hu, M Sahu, K K Sahu, K F Kelton, N A Mauro, Z Nussinov.   

Abstract

We elaborate on a general method that we recently introduced for characterizing the "natural" structures in complex physical systems via multi-scale network analysis. The method is based on "community detection" wherein interacting particles are partitioned into an "ideal gas" of optimally decoupled groups of particles. Specifically, we construct a set of network representations ("replicas") of the physical system based on interatomic potentials and apply a multiscale clustering ("multiresolution community detection") analysis using information-based correlations among the replicas. Replicas may i) be different representations of an identical static system, ii) embody dynamics by considering replicas to be time separated snapshots of the system (with a tunable time separation), or iii) encode general correlations when different replicas correspond to different representations of the entire history of the system as it evolves in space-time. Inputs for our method are the inter-particle potentials or experimentally measured two (or higher order) particle correlations. We apply our method to computer simulations of a binary Kob-Andersen Lennard-Jones system in a mixture ratio of A(80)B(20) , a ternary model system with components "A", "B", and "C" in ratios of A(88)B(7)C(5) (as in Al(88)Y(7)Fe(5) , and to atomic coordinates in a Zr(80)Pt(20) system as gleaned by reverse Monte Carlo analysis of experimentally determined structure factors. We identify the dominant structures (disjoint or overlapping) and general length scales by analyzing extrema of the information theory measures. We speculate on possible links between i) physical transitions or crossovers and ii) changes in structures found by this method as well as phase transitions associated with the computational complexity of the community detection problem. We also briefly consider continuum approaches and discuss rigidity and the shear penetration depth in amorphous systems; this latter length scale increases as the system becomes progressively rigid.

Entities:  

Year:  2011        PMID: 21959545     DOI: 10.1140/epje/i2011-11105-9

Source DB:  PubMed          Journal:  Eur Phys J E Soft Matter        ISSN: 1292-8941            Impact factor:   1.890


  42 in total

Review 1.  Theory of aging in structural glasses.

Authors:  Vassiliy Lubchenko; Peter G Wolynes
Journal:  J Chem Phys       Date:  2004-08-15       Impact factor: 3.488

2.  A structural model for metallic glasses.

Authors:  Daniel B Miracle
Journal:  Nat Mater       Date:  2004-09-19       Impact factor: 43.841

3.  Local resolution-limit-free Potts model for community detection.

Authors:  Peter Ronhovde; Zohar Nussinov
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2010-04-27

4.  Community structure in time-dependent, multiscale, and multiplex networks.

Authors:  Peter J Mucha; Thomas Richardson; Kevin Macon; Mason A Porter; Jukka-Pekka Onnela
Journal:  Science       Date:  2010-05-14       Impact factor: 47.728

5.  Synchronization reveals topological scales in complex networks.

Authors:  Alex Arenas; Albert Díaz-Guilera; Conrad J Pérez-Vicente
Journal:  Phys Rev Lett       Date:  2006-03-22       Impact factor: 9.161

6.  Atomic packing and short-to-medium-range order in metallic glasses.

Authors:  H W Sheng; W K Luo; F M Alamgir; J M Bai; E Ma
Journal:  Nature       Date:  2006-01-26       Impact factor: 49.962

7.  Structural study of supercooled liquid transition metals.

Authors:  T H Kim; K F Kelton
Journal:  J Chem Phys       Date:  2007-02-07       Impact factor: 3.488

Review 8.  Maps of random walks on complex networks reveal community structure.

Authors:  Martin Rosvall; Carl T Bergstrom
Journal:  Proc Natl Acad Sci U S A       Date:  2008-01-23       Impact factor: 11.205

9.  Growing length and time scales in glass-forming liquids.

Authors:  Smarajit Karmakar; Chandan Dasgupta; Srikanth Sastry
Journal:  Proc Natl Acad Sci U S A       Date:  2009-02-20       Impact factor: 11.205

10.  Multiresolution community detection for megascale networks by information-based replica correlations.

Authors:  Peter Ronhovde; Zohar Nussinov
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2009-07-14
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  4 in total

1.  Think locally, act locally: detection of small, medium-sized, and large communities in large networks.

Authors:  Lucas G S Jeub; Prakash Balachandran; Mason A Porter; Peter J Mucha; Michael W Mahoney
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2015-01-26

2.  Detection of hidden structures for arbitrary scales in complex physical systems.

Authors:  P Ronhovde; S Chakrabarty; D Hu; M Sahu; K K Sahu; K F Kelton; N A Mauro; Z Nussinov
Journal:  Sci Rep       Date:  2012-03-29       Impact factor: 4.379

3.  A nature inspired modularity function for unsupervised learning involving spatially embedded networks.

Authors:  Raj Kishore; Ajay K Gogineni; Zohar Nussinov; Kisor K Sahu
Journal:  Sci Rep       Date:  2019-02-22       Impact factor: 4.379

4.  Emergence of network features from multiplexity.

Authors:  Alessio Cardillo; Jesús Gómez-Gardeñes; Massimiliano Zanin; Miguel Romance; David Papo; Francisco del Pozo; Stefano Boccaletti
Journal:  Sci Rep       Date:  2013       Impact factor: 4.379

  4 in total

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