Local Density Fluctuations, Hyperuniformity, and Order Metrics
Salvatore Torquato, Frank H. Stillinger

TL;DR
This paper investigates the variance in point distributions within windows to understand hyperuniform systems, revealing their critical nature, optimal patterns, and proposing local variance as an order metric across dimensions.
Contribution
It introduces a new perspective on hyperuniformity as a critical point with long-range direct correlation functions and compares various lattice and disordered patterns for variance minimization.
Findings
Periodic linear arrays minimize variance in 1D hyperuniform patterns.
The body-centered cubic lattice has lower variance than face-centered cubic in 3D.
Certain disordered hyperuniform patterns have exactly evaluated correlation functions and critical exponents.
Abstract
We study the variance in the number of points contained within a window of arbitrary size, and to further illuminate our understanding of {\it hyperuniform} systems, i.e., point patterns that do not possess long-wavelength fluctuations. For large windows, hyperuniform systems are characterized by a local variance that grows only as the surface area (rather than the volume) of the window. We show that a homogeneous point pattern in a hyperuniform state is at a ``critical-point'' of a type with appropriate scaling laws and critical exponents, but one in which the {\it direct correlation function} (rather than the pair correlation function) is long-ranged.variance.We prove that the simple periodic linear array yields the global minimum value of the average variance among all infinite one-dimensional hyperuniform patterns. We also evaluate the variance for common infinite periodic…
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