Quantifying translational and bond-orientational order metrics in hyperuniform and nonhyperuniform many-particle systems
Anirban Mukherjee, Salvatore Torquato
Abstract
Quantifying the degree of order/disorder in many-particle systems remains an outstanding problem in physics, materials science, and mathematics. To this end, we consider the translational order metric τT, defined as the squared L2 norm of the total correlation function h(r), and introduce its bond-orientational analogue τO, defined from the weighted total correlation function hf(r) using local orientational weights (S. Torquato et al., Phys. Rev. X 16, 011042 (2026)). The pair (τT,τO) places both forms of order on a common two-point statistical footing. We compute both metrics for two nonhyperuniform sphere-packing models in 2D and 3D as functions of packing fraction ϕ: 1) equilibrium hard particles and 2) nonequilibrium random sequential addition (RSA) packings. For the nonhyperuniform systems, bond-orientational order remains subdominant along the equilibrium-fluid and RSA configurations; however, its magnitude relative to the translational metric increases near the upper end of the equilibrium-fluid branches, much more strongly in 2D than in 3D, and the two metrics become comparable along the sampled crystal branches. At common packing fractions, equilibrium fluids and RSA packings trace distinct (τT,τO) trajectories, revealing preparation-dependent differences in structural order. As a representative hyperuniform family, we study 2D disordered stealthy hyperuniform (SHU) ground states for 0<χ<1/2, where χ is the stealthiness parameter. Within the disordered SHU phase, bond-orientational order remains subdominant, but its magnitude relative to the translational metric increases toward the disorder-to-order threshold. In all three models, τT and τO are positively correlated beyond the Poisson-reference regime: τO increases monotonically with τT across the sampled state points.
Create a lesson
Related papers
Towards synergy between theory and experiments: First-passage processes under resetting with stochastic return protocols
Ashutosh Dubey, Arnab Pal
Emergent Loop Geometry in Gauge-Equivariant Graph Neural Networks
Ali Rayat, Gia-Wei Chern
Extending the mean first-passage-time formalism to the reconstruction of high free-energy barriers: A top-start approach
Ying Jiang, Ivan Saika-Voivod
Exact Microscopic One-Body Reduction in the Symmetric Exclusion Process
Olivier Bénichou, Aurélien Grabsch
Macroscopic fluctuation theory of stochastic systems undergoing monitoring
João Costa, Pedro Ribeiro, Andrea De Luca
Charge-selective topological locking and ultraslow coarsening in magnetoelastic media
Tirthankar Banerjee, Amaresh Jaiswal