Rational design of stealthy hyperuniform two-phase media with tunable order

Robert A. DiStasio, Jr., Ge Zhang, Frank H. Stillinger, and Salvatore Torquato
Phys. Rev. E 97, 023311 – Published 27 February 2018

Abstract

Disordered stealthy hyperuniform materials are exotic amorphous states of matter that have attracted recent attention because of their novel structural characteristics (hidden order at large length scales) and physical properties, including desirable photonic and transport properties. It is therefore useful to devise algorithms that enable one to design a wide class of such amorphous configurations at will. In this paper, we present several algorithms enabling the systematic identification and generation of discrete (digitized) stealthy hyperuniform patterns with a tunable degree of order, paving the way towards the rational design of disordered materials endowed with novel thermodynamic and physical properties. To quantify the degree of order or disorder of the stealthy systems, we utilize the discrete version of the τ order metric, which accounts for the underlying spatial correlations that exist across all relevant length scales in a given digitized two-phase (or, equivalently, a two-spin state) system of interest. Our results impinge on a myriad of fields, ranging from physics, materials science and engineering, visual perception, and information theory to modern data science.

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  • Received 4 December 2017

DOI:https://doi.org/10.1103/PhysRevE.97.023311

©2018 American Physical Society

Physics Subject Headings (PhySH)

  1. Research Areas
Statistical Physics & ThermodynamicsGeneral Physics

Authors & Affiliations

Robert A. DiStasio, Jr.1, Ge Zhang2, Frank H. Stillinger2, and Salvatore Torquato2,3,*

  • 1Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA
  • 2Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
  • 3Department of Physics, Princeton Institute for the Science and Technology of Materials, and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA

  • *torquato@princeton.edu

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Issue

Vol. 97, Iss. 2 — February 2018

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