Quantifying hidden order out of equilibrium

Abstract

While the equilibrium properties, states, and phase transitions of interacting systems are well described by statistical mechanics, the lack of suitable state parameters has hindered the understanding of non-equilibrium phenomena in diverse settings, from glasses to driven systems to biology. The length of a losslessly compressed data file is a direct measure of its information content: The more ordered the data is, the lower its information content and the shorter the length of its encoding can be made. Here, we describe how data compression enables the quantification of order in non-equilibrium and equilibrium many-body systems, both discrete and continuous, even when the underlying form of order is unknown. We consider absorbing state models on and off-lattice, as well as a system of active Brownian particles undergoing motility-induced phase separation. The technique reliably identifies non-equilibrium phase transitions, determines their character, quantitatively predicts certain critical exponents without prior knowledge of the order parameters, and reveals previously unknown ordering phenomena. This technique should provide a quantitative measure of organization in condensed matter and other systems exhibiting collective phase transitions in and out of equilibrium.

References in corpus (7)

• Active matter
• Hyperuniformity of critical absorbing states
• Generalized Thermodynamics of Phase Equilibria in Scalar Active Matter
• Generalized thermodynamics of Motility-Induced Phase Separation: Phase equilibria, Laplace pressure, and change of ensembles
• Universality Class of the Reversible-Irreversible Transition in Sheared Suspensions
• Analysis of the phase transition in the $2D$ Ising ferromagnet using a Lempel-Ziv string parsing scheme and black-box data-compression utilities
• Measuring complexity with zippers

Cited by in corpus (41)

• Machine learning and the physical sciences
• Play. Pause. Rewind. Measuring local entropy production and extractable work in active matter
• Multi-scale structural complexity of natural patterns
• Universal and accessible entropy estimation using a compression algorithm
• Measurement-induced criticality as a data-structure transition
• Learning entropy production via neural networks
• Order and Information in the Patterns of Spinning Magnetic Micro-disks at the Air-water Interface
• Absorbing-state transitions in granular materials close to jamming
• Irreversibility in dynamical phases and transitions
• Machine-learning Iterative Calculation of Entropy for Physical Systems
• Deep learning probability flows and entropy production rates in active matter
• Interplay between an absorbing phase transition and synchronization in a driven granular system
• Information density, structure and entropy in equilibrium and non-equilibrium systems
• Inferring entropy from structure
• Vicsek Model by Time-Interlaced Compression: a Dynamical Computable Information Density
• Correlation lengths in the language of computable information
• Universal Relation between Entropy and Kinetics
• Characterizing Different Motility Induced Regimes in Active Matter with Machine Learning and Noise
• Mutual information as a measure of mixing efficiency in viscous fluids
• Defect turbulence in a dense suspension of polar, active swimmers
• Complexity Scaling of Liquid Dynamics
• Stationary States of the One-Dimensional Discrete-Time Facilitated Symmetric Exclusion Process
• Detecting composite orders in layered models via machine learning
• Stationary States of the One-dimensional Facilitated Asymmetric Exclusion Process
• Principal component analysis of absorbing state phase transitions
• Dynamical and structural properties of an absorbing phase transition: a case study from granular systems
• The central role of entropy in adiabatic ensembles and its application to phase transitions in the grand-isobaric adiabatic ensemble
• Resolving entropy contributions in nonequilibrium transitions
• Unraveling liquid polymorphism in silicon driven out-of-equilibrium
• Entropy estimation in bidimensional sequences
• Multiclass classification utilising an estimated algorithmic probability prior
• The Discrete-Time Facilitated Totally Asymmetric Simple Exclusion Process
• Quantifying spatio-temporal patterns in classical and quantum systems out of equilibrium
• Emergent universal long-range structure in random-organizing systems
• Tuning of friction noise by accessing the rolling-sliding option
• Uncertainty Relations for Mesoscopic Coherent Light
• Physical meaning of principal component analysis for classical lattice systems with translational invariance
• Family-Vicsek universality of the binary intrinsic dimension of nonequilibrium data
• Exact Solution of the F-TASEP
• Quantum-optical reset with classical memory
• Patterns that persist: Heritable information in stochastic dynamics