Characterizing Different Motility Induced Regimes in Active Matter with Machine Learning and Noise

Abstract

We examine motility-induced phase separation (MIPS) in two-dimensional run and tumble disk systems using both machine learning and noise fluctuation analysis. Our measures suggest that within the MIPS state there are several distinct regimes as a function of density and run time, so that systems with MIPS transitions exhibit an active fluid, an active crystal, and a critical regime. The different regimes can be detected by combining an order parameter extracted from principal component analysis with a cluster stability measurement. The principal component-derived order parameter is maximized in the critical regime, remains low in the active fluid, and has an intermediate value in the active crystal regime. We demonstrate that machine learning can better capture dynamical properties of the MIPS regimes compared to more standard structural measures such as the maximum cluster size. The different regimes can also be characterized via changes in the noise power of the fluctuations in the average speed. In the critical regime, the noise power passes through a maximum and has a broad spectrum with a $1/f^{1.6}$ signature, similar to the noise observed near depinning transitions or for solids undergoing plastic deformation.

11 pages, 9 figures

References in corpus (28)

• Active Particles in Complex and Crowded Environments
• The Mechanics and Statistics of Active Matter
• Motility-Induced Phase Separation
• Machine learning phases of matter
• Dynamical clustering and phase separation in suspensions of self-propelled colloidal particles
• Athermal Phase Separation of Self-Propelled Particles with no Alignment
• Powerlaw: a Python package for analysis of heavy-tailed distributions
• Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
• Depinning and nonequilibrium dynamic phases of particle assemblies driven over random and ordered substrates: a review
• Freezing and phase separation of self-propelled disks
• Interrupted Motility Induced Phase Separation in Aligning Active Colloids
• Motility-Induced Microphase and Macrophase Separation in a Two-Dimensional Active Brownian Particle System
• Active phase separation by turning toward regions of higher density
• Vacancy-stabilized crystalline order in hard cubes
• Tuning the random walk of active colloids
• Light-controlled Assembly of Active Colloidal Molecules
• Extreme active matter at high densities
• Self-Organized Critical Coexistence Phase in Repulsive Active Particles
• Universal Behavior of the Resistance Noise across the Metal-Insulator Transition in Silicon Inversion Layers
• Quantifying hidden order out of equilibrium
• Critical motility-induced phase separation belongs to the Ising universality class
• Unsupervised machine learning for detection of phase transitions in off-lattice systems I. Foundations
• Crackling Noise, Power Spectra and Disorder Induced Critical Scaling
• Unsupervised machine learning for detection of phase transitions in off-lattice systems II. Applications
• Noise at the Wigner Glass Transition
• Yield in Amorphous Solids: The Ant in the Energy Landscape Labyrinth
• Self-organized defect strings in two-dimensional crystals
• Detecting Depinning and Nonequilibrium Transitions with Unsupervised Machine Learning

Cited by in corpus (4)

• Discovering dynamic laws from observations: the case of self-propelled, interacting colloids
• Emergent short-range repulsion for attractively coupled active particles
• From Attraction to Repulsion: Emergent Interactions in Harmonically Coupled Active Binary System
• Orthogonal projections of hypercubes