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Static structural signatures of nearly jammed disordered and ordered hard-sphere packings: Direct correlation
Steven Atkinson1, Frank H Stillinger2, Salvatore Torquato3
1Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, USA.
Physical Review. E
|October 15, 2016
Summary
Researchers identified static structural signatures in hard-particle systems approaching jamming. The direct correlation function (c(r)) reveals universal and protocol-specific information, aiding in understanding amorphous materials and predicting jamming onset.
Area of Science:
- Condensed Matter Physics
- Materials Science
- Statistical Mechanics
Background:
- Hard-particle systems are compressed into disordered, jammed states, crucial for amorphous materials.
- Dynamical signatures of jamming are known, but static structural signatures remain elusive.
- Hyperuniformity, an anomalous suppression of density fluctuations, is observed as systems approach jamming.
Purpose of the Study:
- To identify static structural signatures indicating the onset of jamming in hard-particle systems.
- To analyze the role of the direct correlation function (c(r)) in characterizing jamming.
- To investigate differences in jamming behavior between various simulation algorithms.
Main Methods:
- Analysis of the direct correlation function (c(r)) and its singularities.
- Application of Ornstein-Zernike equation to understand c(r) structure.
- Simulation of 3D monodisperse hard spheres using Lubachevsky-Stillinger (LS) and Torquato-Jiao (TJ) algorithms.
Main Results:
- Identified a short-ranged scaling c(r)∝-1/r and long-ranged c(r)∝-1/r^2 for disordered packings near jamming.
- Observed a delta function at c(D) indicating particle contacts, appearing earlier in TJ-generated configurations.
- Both LS and TJ algorithms show structure factors tending to zero as jamming is approached.
Conclusions:
- Static signatures are exhibited by hard-particle packings as they approach jamming.
- The direct correlation function (c(r)) is a sensitive tool for monitoring the formation of incipient rigid networks.
- Algorithmic differences (LS vs. TJ) lead to distinct structural evolution and early jamming signatures.
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