arXiv subjects

Giulia Janzen

Publications and source records attributed to Giulia Janzen.

13 recordsLinked to original sources

arXiv2026

Curvature-induced host-mediated polarization of active particles

Polar collective motion commonly arises from alignment interactions, particle anisotropy, or an imposed directional bias. Here we identify a distinct route to polar order that does not rely on alignment interactions between the active particles. We show that non-aligning active Brownian particles embedded in a dense passive medium can develop polar coherence when confined to a compact curved surface. Persistent active motion redistributes stress through the host and creates passive-depleted regions. When the stress-spreading length becomes comparable to the sphere radius, these regions merge into elongated scars that channel active motion and, through feedback with the active flux, promote a common direction of motion. Removing the passive host suppresses polar coherence even though the active particles continue to cluster on the same sphere. Our results establish an environment-mediated route to collective polarity in which symmetry breaking emerges from the coupling between active motion, passive stress redistribution, and compact geometry.

cond-mat.soft↗

arXiv2026

Persistence-Driven Void Formation in Dense Active-Passive Mixtures

It is well established that dilute active dopants can melt an arrested amorphous solid by enhancing cage breaking and accelerating structural relaxation. Yet it remains unclear whether increasing persistence simply amplifies this effective melting or instead reorganizes the fluidization mechanism itself. Here we show that, in a minimal active-passive mixture, increasing persistence drives a crossover from homogeneous fluidization to a localized mechanical instability, demonstrating that sustained active forcing restructures relaxation in space rather than merely strengthening it. Persistent dopants accumulate stress and nucleate voids as their mechanically perturbed regions overlap. In this regime, rearrangements localize at void boundaries, and active and passive particles exhibit comparable mobility, producing dynamics reminiscent of crowd mosh pits. Persistence therefore reorganizes fluidization through stress accumulation and confinement, revealing a distinct nonequilibrium localization mechanism in disordered solids.

cond-mat.soft↗

arXiv2025

Active Matter as a framework for living systems-inspired Robophysics

Robophysics investigates the physical principles that govern living-like robots operating in complex, realworld environments. Despite remarkable technological advances, robots continue to face fundamental efficiency limitations. At the level of individual units, locomotion remains a challenge, while at the collective level, robot swarms struggle to achieve shared purpose, coordination, communication, and cost efficiency. This perspective article examines the key challenges faced by bio-inspired robotic collectives and highlights recent research efforts that incorporate principles from active-matter physics and biology into the modeling and design of robot swarms.

cond-mat.soft↗

arXiv2025

Active Filaments on Curved Surfaces: From Single Filaments to Dilute Suspensions

Curvature plays a central organizational role in active polymer dynamics. Using large-scale Langevin-dynamics simulations, we study active semiflexible filaments confined to smooth curved surfaces and map how curvature, bending rigidity, and activity interact. We find geodesic alignment, curvature lensing, and curvature-induced trapping. In particular, regions of negative Gaussian curvature localize filaments and hinder global surface exploration. These results show how surface geometry can be used to control the organization and transport of active matter on curved substrates

cond-mat.soft↗

arXiv2025

Collective filament wrapping and nested spiral formation in active polydisperse systems

We investigate a two-dimensional polydisperse suspension of self-propelled semiflexible filaments and reveal a collective wrapping mechanism that is absent in monodisperse systems. At intermediate activity levels, long filaments coil around shorter ones, forming nested spiral structures stabilized by filament length disparity. These assemblies generalize the single-filament spiraling seen in active systems into cooperative, multi-filament configurations. As activity increases, the nested spirals undergo structural transitions: medium-length filaments unwind, longer filaments encapsulate shorter ones, and eventually all spiral structures dissolve. This reorganization is reflected in the dynamics, where van Hove distributions uncover coexisting confined and motile filament populations. Our findings identify filament length as a key control parameter for nonequilibrium self-assembly and establish inter-filament wrapping as a minimal mechanism for hierarchical organization in active matter. This mechanism provides a simple model for the cooperative confinement and structural hierarchy observed in both biological and synthetic active systems.

cond-mat.soft↗

arXiv2025

Emergent Dynamics of Active Systems on Curved Environments

Curvature plays a central role in the proper function of many biological processes. With active matter being a standard framework for understanding many aspects of the physics of life, it is natural to ask what effect curvature has on the collective behaviour of active matter. In this paper, we use the classical theory of surfaces to explore the active motion of self-propelled agents confined to move on a smooth curved two-dimensional surface embedded in Euclidean space. Even without interactions and alignment, the motion is non-trivially affected by the presence of curvature, leading to effects akin, e.g.\ to gravitational lensing and tidal forces. Such effects can lead to intermittent trapping of particles and profoundly affect their flocking behaviour. We show that these effects are governed by a geometric torque that, in the absence of noise and interactions, compels particles to move along geodesics.

cond-mat.soft↗

arXiv2024

Active Polymer Behavior in Two Dimensions: A Comparative Analysis of Tangential and Push-Pull Models

In this work, we compare the structural and dynamic behavior of active filaments in two dimensions using tangential and push-pull models, including a variant with passive end monomers. These models serve as valuable frameworks for understanding self-organization in biological polymers and synthetic materials. At low activity, all models exhibit similar behaviors. Differences emerge in the intermediate range as activity increases, though at higher activity levels, their behaviors converge. Importantly, adjusting for differences in mean active force reveals nearly identical behavior across models. Our results highlight the importance of force definitions in active polymer simulations and provide insights into phase transitions across varying filament configurations.

cond-mat.soft↗

arXiv2024

Shape matters: Inferring the motility of confluent cells from static images

Cell motility in dense cell collectives is pivotal in various diseases like cancer metastasis and asthma. A central aspect in these phenomena is the heterogeneity in cell motility, but identifying the motility of individual cells is challenging. Previous work has established the importance of the average cell shape in predicting cell dynamics. Here, we aim to identify the importance of individual cell shape features, rather than collective features, to distinguish between high-motility (active) and low-motility (passive) cells in heterogeneous cell layers. Employing the Cellular Potts Model, we generate simulation snapshots and extract static features as inputs for a simple machine-learning model. Our results show that when the passive cells are non-motile, this machine-learning model can accurately predict whether a cell is passive or active using only single-cell shape features. Furthermore, we explore scenarios where passive cells also exhibit some degree of motility, albeit less than active cells. In such cases, our findings indicate that a neural network trained on shape features can accurately classify cell motility, particularly when the number of active cells is low, and the motility of active cells is significantly higher compared to passive cells. This work offers potential for physics-inspired predictions of single-cell properties with implications for inferring cell dynamics from static histological images.

physics.bio-ph↗

arXiv2024

Density and inertia effects on two-dimensional active semiflexible filament suspensions

We examine the influence of density on the transition between chain and spiral structures in planar assemblies of active semiflexible filaments, utilizing detailed numerical simulations. We focus on how increased density, and higher P\'eclet numbers, affect the activity-induced transition spiral state in a semiflexible, self-avoiding active chain. Our findings show that increasing the density causes the spiral state to break up, reverting to a motile chain-like shape. This results in a density-dependent reentrant phase transition from spirals back to open chains. We attribute this phenomenon to an inertial effect observed at the single polymer level, where increased persistence length due to inertia has been shown in recent three-dimensional studies to cause polymers to open up. Our two-dimensional simulations further reveal that a reduction in the damping coefficient leads to partial unwinding of the spirals, forming longer arms. In suspension, interactions among these extended arms can trigger a complete unwinding of the spirals, driven by the combined effects of density and inertia.

cond-mat.soft↗

arXiv2023

Rejuvenation and memory effects in active glasses induced by thermal and active cycling

It has recently been shown that thermal active glasses can display physical aging behavior comparable to that of passive glasses, although there are some notable distinctions due to the intrinsic non-equilibrium nature of active matter. The question whether active disordered materials can also exhibit rejuvenation and memory effects, akin to the phenomenology of e.g.\ spin glasses, has thus far remained unexplored. Here we address this question by numerical simulations of active glasses composed of active Brownian particles that are subjected to a thermal or active cycling protocol. We find that an active system undergoing thermal cycling indeed shows rejuvenation and memory effects, with the strength of rejuvenation depending on the persistence time. In contrast, however, a passive Brownian system subjected to the same thermal cycle lacks the rejuvenation effect. We attribute this to the enhanced motility of active particles, which enables them to escape from their cages and restart aging at the new temperature, thus rejuvenating the material. Finally, we also demonstrate that both rejuvenation and memory effects can be induced by an activity cycle which quenches the material from an active to passive glass and back, providing a unique means to rejuvenate active matter.

cond-mat.soft↗

arXiv2023

Classifying the age of a glass based on structural properties: A machine learning approach

It is well established that physical aging of amorphous solids is governed by a marked change in dynamical properties as the material becomes older. Conversely, structural properties such as the radial distribution function exhibit only a very weak age dependence, usually deemed negligible with respect to the numerical noise. Here we demonstrate that the extremely weak age-dependent changes in structure are in fact sufficient to reliably assess the age of a glass with the support of machine learning. We employ a supervised learning method to predict the age of a glass based on the system's instantaneous radial distribution function. Specifically, we train a multilayer perceptron for a model glassformer quenched to different temperatures, and find that this neural network can accurately classify the age of our system across at least four orders of magnitude in time. Our analysis also reveals which structural features encode the most useful information. Overall, this work shows that through the aid of machine learning, a simple structure-dynamics link can indeed be established for physically aged glasses.

cond-mat.soft↗

arXiv2023

Dead or alive: Distinguishing active from passive particles using supervised learning

A longstanding open question in the field of dense disordered matter is how precisely structure and dynamics are related to each other. With the advent of machine learning, it has become possible to agnostically predict the dynamic propensity of a particle in a dense liquid based on its local structural environment. Thus far, however, these machine-learning studies have focused almost exclusively on simple liquids composed of passive particles. Here we consider a mixture of both passive and active (i.e.\ self-propelled) Brownian particles, with the aim to identify the active particles from minimal local structural information. We compare a state-of-the-art machine learning approach for passive systems with a new method we develop based on Voronoi tessellation. Both methods accurately identify the active particles based on their structural properties at high activity and low concentrations of active particles. Our Voronoi method is, however, substantially faster to train and deploy because it requires fewer, and easy to compute, input features. Notably, both become ineffective when the activity is low, suggesting a fundamentally different structural signature for dynamic propensity and non-equilibrium activity. Ultimately, these efforts might also find relevance in the context of biological active glasses such as confluent cell layers, where subtle changes in the microstructure can hint at pathological changes in cell dynamics.

cond-mat.soft↗

arXiv2021

Aging in thermal active glasses

It is well established that glassy materials can undergo aging, i.e., their properties gradually change over time. There is rapidly growing evidence that dense active and living systems also exhibit many features of glassy behavior, but it is still largely unknown how physical aging is manifested in such active glassy materials. Our goal is to explore whether active and passive thermal glasses age in fundamentally different ways. To address this, we numerically study the aging dynamics following a quench from high to low temperature for two-dimensional passive and active Brownian model glass-formers. We find that aging in active thermal glasses is governed by a time-dependent competition between thermal and active effects, with an effective temperature that explicitly evolves with the age of the material. Moreover, unlike passive aging phenomenology, we find that the degree of dynamic heterogeneity in active aging systems is relatively small and remarkably constant with age. We conclude that the often-invoked mapping between an active system and a passive one with a higher effective temperature rigorously breaks down upon aging, and that the aging dynamics of thermal active glasses differs in several distinct ways from both the passive and athermal active case.

cond-mat.stat-mech↗