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

TL;DR

This study uses machine learning and noise analysis to identify and characterize multiple regimes within motility-induced phase separation in active matter, revealing distinct dynamical and structural states.

Contribution

It introduces a combined approach of PCA-based order parameters and noise fluctuation analysis to distinguish active fluid, crystal, and critical regimes in MIPS.

Findings

Identification of three distinct MIPS regimes: active fluid, active crystal, and critical.

Machine learning captures dynamical properties better than standard structural measures.

Critical regime exhibits maximum noise power with a 1/f^{1.6} spectrum.

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.