Self-Organizing and Evolving Active Matter

Biological matter has the fascinating ability to autonomously generate material deformations via intrinsic active forces, where the latter are often present within effectively two-dimensional structures. The dynamics of such “active surfaces” inevitably entails a complex, self-organized interplay between the geometry of a surface and its mechanical interactions with the surrounding. In this talk, I will first discuss general numerical challenges in analyzing self-organizing active surfaces and the bifurcation structure of emergent shape spaces. I will then focus on active surfaces with broken up-down symmetry, of which the eukaryotic cell cortex and epithelial tissues are key biological examples. A natural interplay between active stress and curvature leads for such surfaces to a comprehensive library of spontaneous shape transformations that resemble stereotypical morphogenetic processes. These include cell-division-like invaginations and the autonomous formation of tubular surfaces of arbitrary length, both of which robustly overcome well-known shape instabilities that would arise in analogue passive systems.

Biomimetic motile sessile drops of complex liquids are described by thermodynamically consistent mesoscopic hydrodynamics models in gradient dynamics form [1] that are supplemented by selected out-of-equilibrium drivers, namely (i) active bulk stress [2] and (ii) chemostatted reactive surfactants [3]. After discussing experimental and theoretical motivations we introduce the general modelling approach, subsequently specified for two specific cases. First, a model for partially wetting drops on solid substrates is made active by incorporating a nonreciprocal coupling to a polarisation field in the form of self-propulsion and active stress [2]. We show that the employed polarisation-surface coupling results in (hysteretic) transitions between resting and moving dops, the splitting of drops, and chiral motion. Second, we incorporate autocatalytic reactive surfactants into a related gradient dynamics model. Breaking detailed balance by chemostatting, a positive feedback loop between the local reactions and the Marangoni effect results in persistent self-organized chemo-mechanical dynamics [3]. Besides simple directional motion, we discuss crawling and shuttling drops as well as drops performing random walks, thus exploring the entire substrate. Glimpses at the underlying bifurcation structure indicate local and global mechanisms that we expect to be relevant not only to directly related biomimetic drop systems but also to structurally similar systems like chemically active phase separating mixtures. Throughout the talk we relate the models and results to recent efforts of physicists and mathematicians alike to break Newton's third law to make systems active, and in passing discuss a possible classification into mechanical, thermodynamical and chemical nonreciprocity. [1] U. Thiele, Colloids Surf. A 553, 487-495 (2018). http://dx.doi.org/10.1016/j.colsurfa.2018.05.049; [2] F. Stegemerten, K. John, and U. Thiele, Soft Matter 18, 5823 (2022), http://dx.doi.org/10.1039/D2SM00648K; S. Trinschek, F. Stegemerten, K. John, and U. Thiele, Phys. Rev. E 101, 062802 (2020), http://dx.doi.org/10.1103/PhysRevE.101.062802; [3] F. Voss and U. Thiele, J. Eng. Math. 149, 2 (2024). http://dx.doi.org/10.1007/s10665-024-10402-x; and F. Voss and U. Thiele, preprint (2025) All papers/preprints of the group can be downloaded at https://www.uwethiele.de/publ.html

The development of an organism starting from a fertilized egg involves the self-organized formation of patterns and the generation of shape. Such morphogenetic processes often rely on flows of cells and molecules driven by molecular force generation. Here, we investigate how the shape of an organism guides such active flows and thereby the formation of chemical patterns. We study active surfaces, i.e. active processes confined to a surface of complex geometry. We focus in particular on an active fluid model of the cell cortex. With this, we find that active cortical stresses can drive a rotation of the cell that aligns the chemical pattern of the stress regulator with the geometry of the cell surface. Specifically, we find that active tension in the cytokinetic ring ensures that a cell divides along its longest axes, consistent with experimental observations in mouse and nematode embryos. Thereby the cytokinetic ring is aligned with the saddle of a prolate sphere. Notably we find that even point like sources of isotropic active tension are advected towards such saddle geometries, also in non-spherical topologies. We find that such an advection of active particles towards certain points in the intrinsic surface geometry of a fluid film is a generic consequence of viscosity. Thus, active surfaces generally exhibit a sense of their geometry, as surface geometry guides flows and thus patterns, which may contribute to the robustness of morphogenetic processes.

Biofilms are complex aggregates of microorganisms, consisting primarily of bacteria, that are covered by an extracellular matrix and often adhere to a surface. Despite significant interest from medicine, industry, ecology, and basic sciences, biofilms still resist fundamental understanding, presumably due to their complexity. Here, I present some of our results demonstrating that the concept of active liquid crystals may help in understanding certain aspects of bacterial biofilms. Specifically, I will discuss the relevance of cellular orientation patterns to the 3D structures of colonies [1] and the production of certain matrix component [2], as well as the aggregate formation of planktonic cells under starvation [3]. [1] T. Shimaya and K. A. Takeuchi, Tilt-induced polar order and topological defects in growing bacterial populations. PNAS Nexus 1, pgac269 (2022). [2] F. Yokoyama and K. A. Takeuchi, in preparation. [3] T. Shimaya, F. Yokoyama, and K. A. Takeuchi, Smectic-like bundle formation of planktonic bacteria upon nutrient starvation, Soft Matter 21, 2868 (2025).

Controlling the behavior of microswimmers is a major challenge to extract work for novel active matter applications. Geometric confinement is often used for controlling soft matter systems. However, in comparison to the case of Newtonian fluids, the effects of solid interfaces on microswimmers moving through an anisotropic fluid are far less understood. By means of nematic multiparticle collision dynamics simulations and analytical modeling, we investigate the dynamical behavior of swimmers immersed in a nematic liquid crystal and confined by solid walls. For isolated squirmers, we find a rich phase diagram including oscillatory dynamics for weak pushers, depending on the strength of their propulsion and degree of confinement. Our theoretical model shows that, unlike in the isotropic case, in a nematic fluid, force dipole, source dipole, and source quadrupole singularities all are required for the onset of oscillations. Increasing the number of squirmers shows the emergence of cooperativity in pusher-type squirmers, while pullers’ flow fields hinder each other’s motion. The interplay of nematodynamic torque, wall-induced elastic repulsion, and active flows thus offers the opportunity for both control and transport in active nematic systems.

Cell migration is crucial to many biological processes, including embryonic morphogenesis, wound healing, tissue regeneration, immune response, and cancer progression. One of the main challenges in studying cell motility is understanding how cells respond to external stimuli, such as chemical, geometrical, and physical signals. These responses often involve movement toward or away from a stimulus, which is called a taxis. Several types of taxis are known, including chemotaxis, haptotaxis, curvotaxis, and durotaxis, etc. However, an important feature of the tactic movement of living cells -- non-negligible active random fluctuations -- has not been accounted for properly. In this talk, I will introduce a simple way to account for such active fluctuations by introducing stochastic forces into the equations of motion for individual cells. This phenomenological description of the random motion of living cells dates back to Przibram and Furth (1920), who introduced the persistent random walk (PRW) to describe the random swimming of living microorganisms. However, the PRW model has not been applied to the migration of crawling animal cells until Gail and Boone (1970) for fibroblasts on flat, homogeneous solid substrates. We propose that the PRW model and related self-propelled particle models provide a phenomenological paradigm for quantifying cellular taxis, in which the fluctuations or randomness are taken into account by persistent random motion and the taxis is included into some “potentials”. The potential can be either derived from phenomenological physics-based cell models or learned from experimental data (stochastic cell trajectories) using deep learning methods. Based on this simple idea, I will present our theoretical models for some typical cellular taxis such as haptotaxis on substrates with fibronectin gradients, curvotaxis on stiff cylinders, and durotaxis on substrates with stiffness gradients. References: [1] Xiaoyu Yu#, Haiqin Wang#, Fangfu Ye, Qihui Fan*, Xiaochen Wang*, and Xinpeng Xu*, Biphasic curvature-dependence of cell migration inside microcylinders: persistent randomness versus directionality, bioRxiv:2022.522287. [2]. Subhaya Bose#, Haiqin Wang#, Arvind Gopinath*, Xinpeng Xu*, and Kinjal Dasbiswas*, Elastic interactions compete with persistent cell motility to drive durotaxis, arXiv:2402.15036.

Self propelling droplets are a fascinating experimental model for isotropic chemically active particles. Their motion is driven by a spontaneously broken symmetry, as they transition from an inactive state of uniform chemical activity to directed motion via an advection-diffusion instability of their chemical product. Experimentally, being a three component system of water, oil and surfactant, these emulsions are well controllable and reproducible. However, owing to their nonlinear propulsion mechanism and long-lived self-produced chemical signals, leading to a manifold of emergent collective states like self-trapping or confinement controlled aggregation.

A wealth of self-organization processes in active matter systems will be discussed while the particle size is systematically lowered from macroscopic (granular) to mesoscopic (colloidal) and even finally to quantum. For active granular vibrobots we discuss a collective self-sustained cooling mechanism which is induced by dry friction, the emergence of a spontaneous self-wrapping in chiral active polymers and odd diffusion in chiral active granular particles. For active colloids, self-configuring active colloidal molecules are proposed which exhibit unusual anti-aligning properties. Moreover the motion of an active colloids in a tunable environment is discussed where both persistence and chirality can be tailored. Finally the idea of quantum active matter is introduced. A first step is to engineer active motion in quantum systems such as ultracold gases by time-dependent optical fields.

When a particle or droplet, including surface-active chemicals such as camphor and alcohol, is floated on the water surface, it releases surface-active molecules to the water surface around it. The released molecules decrease the water surface tension and form the surface tension gradient, which can drive the particle or droplet. Since the surface tension gradient is generated through diffusion, this type of motion is called "diffusiophoresis." The diffusiophoresis is also observed in the motion of living organisms which detect the chemicals emitted by themselves. In the presentation, we discuss how the particle shape affects the diffusiophoretic motion. First, we investigated the case of an elliptic-shaped particle. From the numerical simulation and theoretical analysis, we suggest that the elliptic particles move in their minor-axis direction. We made experiments using an elliptic camphor disk and confirmed that it preferably moves in its minor-axis direction. In contrast to the case with particles, droplets can deform. For example, a pentanol droplet performs a spontaneous motion with deformation. We constructed a mathematical model that describes the coupling between shape and motion by considering the free energy functional. Using this model, we performed theoretical analyses and numerical simulations. We discussed the relation between the shape and motion by comparing the experimental results and theoretical approach.

Enzymatic nanomotors self-propel by converting free molecular energy from chemical reactions. While these systems have primarily been studied and applied as individual particles, they hold significant potential when implemented as collective swarms. Recently, the collective motion of nanomotors has garnered interest due to their expanded area of influence, enhanced effects at target sites, and unique motion dynamics observed only in large populations. However, studies on nanomotor swarms have been largely restricted to 2D, providing limited insights into their overall 3D behavior. Additionally, the combination of properties such as magnetic navigation and enzyme-powered motion remains underexplored, despite its potential to enable reconfigurable and adaptable collective dynamics controlled on demand. In this study, we introduce urease-powered nanomotors based on bioproduced magnetosomes that can be (i) magnetically guided as a nanomotor ensemble to a target area and (ii) catalytically expand the swarm to enhance the area explored at the air-water interface. We first investigate this catalytic collective phenomenon under varying conditions, including particle and substrate concentrations and media viscosity. Special attention is given to often-overlooked aspects of collective active matter, such as analyzing motion from both top and side views to assess 3D behavior indirectly and the role of fluid dynamics caused by inactive nanoparticles in urea. Finally, we examine swarm behavior under a magnetic gradient field to steer the swarm toward a target area and catalytically trigger its expansion in open settings. This multimodal strategy is then exploited in confined environments with turns to demonstrate the increased concentration of magneto-enzymatic swarms to the target area. This approach fosters reconfigurable nanomotor swarms for implementations requiring precise localization and maximum agent concentration in inaccessible areas, particularly for applications targeting the air-water interface, such as environmental remediation of oil spills and bacteria accumulations.

Active fluids consist of self-propelled particles (as bacteria or artificial microswimmers) and display properties that differ strongly from their passive counterparts. Unique physical phenomena, as enhanced Brownian diffusivity, viscosity reduction, active transport and mixing or the extraction of work from chaotic motion, result from the activity of the particles, locally injecting energy into the system. The presence of living and cooperative species may also induce collective motion and organization at the mesoscopic or macroscopic level impacting the constitutive relationships in the semi-dilute or dense regimes. Individual bacteria transported in viscous flows show complex interactions with flows and bounding surfaces resulting from their complex shape as well as their activity. Understanding these transport dynamics is crucial, as they impact soil contamination, transport in biological conducts or catheters, and constitute thus a serious health thread. Here we investigate the trajectories of individual E-coli bacteria in confined and complex geometries with and without flow, using microfluidic model systems in combination with a novel Langrangian 3D tracking method. Combining experimental observations and modelling we elucidate the origin of upstream swimming, lateral drift, persistent transport along edges as well as bacteria accumulation in specific geometries. Increasing bacteria concentrations collective motion emerges, where typical lengthscales can be identified in the velocity correlations. Using PIV measurements, we characterize the emerging vortex like structures as a function of confinement and we discuss how the corresponding lengthscale can be controlled though bounding walls or flows. The understanding gained can be, for example, used to control bacteria transport in complex geometries or shed light on the role of emergent mescoscopic structures on the macroscopic properties of active suspensions.

Bacterial suspensions are a typical and well-known example of active matter comprising self-propelling components. Here we present two numerical studies on the dynamics of bacterial suspensions, particularly focusing on their behavior under spatial confinement. One relies on the continuum description of the suspension in terms of the velocity field, known as the Toner-Tu-Swift-Hohenberg equation. A slip boundary condition is implemented at the confining boundaries. We find that an edge current of the suspension emerges and exhibits temporal oscillation [1]. The other is an agent-based one that models a bacterial suspension as a collection of self-propelled rods. Various types of confinements and obstacles involving curved boundaries are considered. We find that the rods align normal to the boundary to form clusters, and the clusters collectively move along the boundary [2]. [1] Matsukiyo and Fukuda, Phys. Rev. E 109, 0546406 (2024). [2] Kaneko and Fukuda, in preparation.

Many microorganisms (e.g. Paramecium) move by a carpet of cyclically beating cilia that cover their surface. These cilia often beat in an organized fashion, such that the beating phases form a traveling wave, referred to as a metachronal wave. Often, studies on the swimming of such microorganisms neglect the metachronal dynamics. We show that such an approach is not sufficient if the organism swims in corrugated microchannels. By modeling the motion of the cilia as a time-varying effective slip velocity applied on the microorganism's surface, which we approximate as an infinite cylinder, we show analytically that the swimming speed is sensitive to the corrugation height, provided that the wavelength of the corrugation matches that of the metachronal wave. Indeed, the direction of motion itself may invert with respect to swimming in bulk fluid, with the channel then acting as a filter which blocks the passage of some microorganisms, but allows others to pass through. We also show that the interplay between the corrugation and the slip velocity profile allows microorganisms to swim with zero time-averaged slip velocity, which would not move in bulk fluid. Finally, we complement our theory with preliminary results from hydrodynamic simulations for microorganisms of finite length.

Several chemical and biomedical applications require systematic processing of micron-sized fluid samples. To realize this, so-called microfluidic lab-on-a-chip devices with micron-sized channels are widely used. Often, one needs to manipulate two layers of immiscible fluids in such channels. The interface separating the two fluid components costs energy, which is quantified by surface tension. Thus, any deformation of a planar fluid interface increases energy. In our work, we assign one more property to the interface in the form of dipolar forces acting perpendicular to the interface. We name it the \emph{activity} of the interface, and it is achieved by covering the interface with active particles. The notion of activity is inspired by swimming microorganisms. Using computer simulations, we discover that the presence of activity affects the stability of the interface. We notice that dipolar forces pointing toward the interface counter the stabilizing effect of surface tension, so that the interface deforms. In contrast, force components pointing away stabilize the interface against perturbation. The destabilizing activity might seem undesirable at first. However, we demonstrate that one can leverage such activity-induced instability to generate microfluidic droplets. In addition, we can also control droplet formation by varying the magnitude of dipolar forces in real time, which can be accomplished using light-sensitive active particles in practice. Realizing our proposed microfluidic design will impact various real-life applications, e.g., encapsulation of biological cells for tissue engineering or for early disease diagnosis.

Motion of active Brownian particles is strongly affected by confinement effects, which impact the persistence of the ballistic dynamics and can guide transitions and instabilities when dealing with deformable interfaces. Here, I will present some experimental results describing the dynamics of active colloids interacting with soft fluctuating membranes. Our experimental systems are composed of Giant unilamellar vesicles (GUVs) with tunable membrane tension and Janus self-propelled colloids showing different hydrodynamic and particle-membrane interactions. We were able to observe emerging dynamics such as particle orbital motion around the vesicle [1], active vesicle transport [2] and autonomous particle engulfment by the vesicle membrane [3]. References: [1] V Sharma, E Azar, AP Schroder, CM Marques, A Stocco. Active colloids orbiting giant vesicles, Soft Matter 2021, 17, 4275. [2] V Sharma, CM Marques, A Stocco. Driven Engulfment of Janus Particles by Giant Vesicles in and out of thermal equilibrium, Nanomaterials 2022, 12, 1434. [3] F Fessler, M Wittmann, J Simmchen, A Stocco. Dynamics of Active Colloid Engulfment by Giant Lipid Vesicles, Soft Matter 2024, 20, 5904.

In this talk, I will discuss two types of intermittent active diffusion processes as observed in experiments of (i) bacterial navigation through porous gels and (ii) biohybrid colloidal transport driven by living cells. In the first part, we address the question how bacteria navigate in their habitat purposefully and efficiently, e.g., in the soil, which is a complex, structured environment. Combining experiments and active particle modeling, we address the interrelation of bacterial navigation at the microscale and their large-scale spreading in heterogeneous environments considering both, undirected motion in a porous medium as well as biased motion in chemical gradients. Remarkable motility characteristics, including transient subdiffusion, primarily due to intermittent mechanical trapping with power-law distributed trap times, are revealed. Furthermore, we provide evidence that bacteria can perform chemotaxis in porous media even though their mean free path is severely restricted by adjusting their change in swimming direction upon a turn event, so that the direction of the next run phase is biased towards the source of the chemoattractant (turn angle bias). Active particle modeling, based on the experimentally inferred statistical properties of the swimming pattern, indicates that turn angle bias is the predominant chemotaxis strategy of bacteria in porous media. In the second part, we describe the transport of polystyrene beads whose motion is actively driven by cells via direct mechanical contact. We will first discuss the stochastic dynamics of a single cell-cargo pair, particularly focusing on the existence of an optimal cargo size that enhances the diffusion of the load-carrying cells, even exceeding their motility in the absence of cargo, and estimate the active forces exerted by cells to move colloids. Afterwards, we present the collective transport of micron-sized particles on a monolayer of motile cells. The transport of colloids shows a crossover from superdiffusive to normal-diffusive dynamics. The particle displacement distribution is distinctly non-Gaussian even at macroscopic timescales exceeding the measurement time. We obtain the distribution of diffusion coefficients from the experimental data and introduce a model for the displacement distribution that matches the experimentally observed non-Gaussian statistics. We argue why similar transport properties are expected for many composite active matter systems. These results can provide the basis for the future design of cellular microcarriers and for more advanced transport tasks in complex, disordered environments, e.g. tissues.

Biological swimmers are intrinsically active and exhibit self-propulsion mechanisms optimized for survival and foraging tasks. Quantifying the time required for them to travel specific distances is challenging due to the complex coupling between swimming orientation and positional dynamics. Here, we investigate the first-passage-time (FPT) properties of active Brownian particles to reach an absorbing wall in two dimensions [1]. Employing a perturbation approach, we obtain exact analytical predictions for the survival and FPT distributions for low activity. Using the median as a metric, we quantify the anisotropy emerging from the starting angle and find that it becomes more pronounced when the initial distance from the wall is comparable to the length they travel before reorienting due to rotational diffusion. We further numerically study hydrodynamic effects in "wet" systems, where the confined geometry leads to height-dependent diffusion coefficients and backflows that affect agents differently depending on their swimming mechanism's flow signature. Our results show that hindered diffusion near the wall can increase the median FPT by orders of magnitude compared to "dry" agents. Additionally, due to the competition between noise and hydrodynamic attraction, pushers tend to need less activity to reach the boundary than pullers do. [1] Y. Baouche, M. Le Goff, C. Kurzthaler, T. Franosch, Physical Review E 111, 054113 (2025)

As the simplest living organisms on our planet, yet capable of complex behaviors such as proliferation and navigation, bacteria present compelling model systems for a wide range of fundamental questions in the field of active matter physics. Bacterial chemotaxis, i.e. biased movement relative to chemical concentration gradients, has become a paradigm for low-Reynolds number motility, information processing, navigation, and collective dynamics. Chemotaxis and proliferation create feedback between bacterial population densities and chemical concentration fields and can give rise to collective range expansions driven by chemotactic traveling waves. The overall population growth depends on both the chemotactic navigation performance and the individual growth rate, yet in nature these two parameters are typically subject to a tradeoff due to resource limitation. Trade-offs can be addressed by different behavioral strategies, setting the stage for a range of possible ecological dynamics in mixed populations. I will discuss our experimental and theoretical efforts to qualitatively and quantitatively understand the spatio-temporal self-organization of mixed microbial populations based on their navigation and proliferation properties.

A massive formation of stable sea foam is regularly observed on certain coastlines. These naturally occurring foams are associated with a loss of phytoplankton biomass and biodiversity in the seawater. We are investigating whether the phytoplankton advected into the foam during its formation remains trapped in the complex network of internal channels in the foam. In this talk, I will present experiments carried out in the laboratory to study the retention in a liquid foam of a model phytoplankton organism: the unicellular alga Chlamydomonas reinhardtii (CR), which is bi-flagellate and therefore motile. We measured the escape dynamics of CR cells from the foam. A comparison between live and dead cells shows that live CR cells tend to be retained in the foam. Finally, I will discuss the microscopic mechanisms that can lead to this entrapment, which raises the question of the transport of microswimmers in confined and environments under flow.

TBA

Active soft matter systems continue to captivate researchers due to their relevance to biological and synthetic systems alike. Understanding and controlling emergent behaviors in these systems is essential for designing reconfigurable synthetic materials, advancing micro-robotics, and exploring collective cell migration. Among various synthetic active systems, self-propelled rods are currently a subject of great interest in active soft matter physics, serving as a minimal model due to their ability to mimic and provide deeper insights into biological phenomena such as bacterial swarms and biopolymers like microtubule assemblies. In equilibrium systems, the shape anisotropy of individual building blocks is known to play a crucial role in creating exotic structures and controlling phase behavior. However, whether and how this shape anisotropy influences internally driven, out-of-equilibrium synthetic systems remains elusive. Here, we present combined experimental and simulation studies using colloidal self-propelled rods to elucidate when and how shape-induced alignment, aspect ratio, steric interactions, hydrodynamic interactions, and density fluctuations drive self-organization in active matter, drawing inspiration from the rich collective dynamics observed in bacterial systems such as E. coli. We demonstrate that, as particle concentration increases, the system undergoes a distinct sequence of transitions, from run-and-tumble motion to swarming, active turbulence, flocking, formation of large clusters, and ultimately jamming [2]. By systematically varying rod aspect ratio and particle density, we construct a comprehensive state diagram mapping these distinct collective phases. We further characterize the spatiotemporal evolution of these states through analyses of velocity correlations and giant number fluctuations. Our findings highlight how particle anisotropy, hydrodynamics, and density govern emergent structures, providing novel pathways to design and control collective behaviors in synthetic active materials and establishing clear parallels with biological phenomena, such as bacterial swarming and biofilm formation. Keywords: Self-propelled rods, Active Turbulence, Flocking, Active Matter, Self-organization Acknowledgements: We gratefully acknowledge the Netherlands Organization for Scientific Research (NWO) for funding through the NWO-M1 and XS grants. References [1] M. C. Marchetti, et al., Hydrodynamics of soft active matter, Reviews of modern physics 85, 1143 1189, 2013. [2] Yogesh Shelke, Anpuj Nair S, and Hanumantha Rao Vutukuri, Shaping Phase Behavior in Active Rod Systems: Swarming, Flocking, Active Turbulence, and Jamming, Under Review.

Systems of interacting active particles have been the subject of extensive theoretical research, particularly to explore their phase diagrams [1] and correlation functions [2]. Recently, experimental studies have revealed strong memory effects when these active systems are immersed in a viscoelastic medium, leading to intriguing behaviors in both individual [3] and collective [4] dynamics. While memory effects have been well studied in equilibrium systems, their impact on out-of-equilibrium active phases remains a largely open topic. In this talk, I will present recent and ongoing work investigating the dynamics of active particles embedded in a viscoelastic medium, modeled by a memory kernel acting on individual particles. The primary focus will be on how memory effects modify motility-induced phase separation (MIPS) [1] at high density and activity. For the simplest model, which incorporates only a memory kernel, I will demonstrate that memory hinders motility-induced phase separation, resulting in a fluidized system even under high activity. For a more realistic model, where individual particles exhibit chiral trajectories [3], the system remains fluid but shows striking statistical behavior reminiscent of colloidal gels. I will discuss the implications of these findings and outline future directions, including the integration of hydrodynamic interactions between particles. [1] P. Digregorio et al., Phys. Rev. Lett. 121, 098003 (2018). [2] A. Poncet, O. Bénichou, V. Démery & D. Nishiguchi, Phys. Rev. E 103, 012605 (2021). [3] N. Narinder, C. Bechinger & J. Gomez-Solano, Phys. Rev. Lett. 121, 078003 (2018). [4] S. Liu, S. Shankar, M. C. Marchetti & Y. Wu, Nature 590, 80 (2021).

We study the hydrodynamic interactions of swimming microorganisms with nearby deformable bound- aries omnipresent in their natural habitats. The boundary, characterized by its surface tension and bending rigidity, is deformed by the flow produced by the microswimmer and thereby modifies its swimming veloc- ities. Describing the far-field flow of the agent as a combination of a force and torque dipole, we compute small instantaneous deformations of the boundary. We further use the Lorentz reciprocal theorem to obtain leading-order corrections of its swimming velocities and compute a phase diagram based on the swimmer’s initial orientation and the material properties of the deformable boundary. Our results reveal that pushers can both re-orient away from the boundary, leading to overall hydroelastic scattering, or become trapped near the boundary, while pullers exhibit enhanced trapping. These findings demonstrate that the complex hydroelastic interactions can generate behaviors that are fundamentally different to swimming near planar walls. Ref: S. Garai, et. al., arXiv preprint arXiv:2502.02462

I will present our recent works on how hydrodynamics can determine the physical responses of (1) cilia and (2) a multiflagellated microswimmer. Based on the analytical modelling and numerical simulations, we try to understand how complex living systems can function by only utilizing simple physical rules. References: [1] Cheng, Vilfan, Wang, Golestanian, and FM, Journal of the Royal Society Interface 21, 20240221 (2024). [2] Hu, and FM, Physical Review Letters 132, 204002 (2024).

Collective swimming of sperm are observed in various species, suggesting that grouping enhances motility. In this study, we developed a numerical model of sperm computed by fluid-structure interactions between multiple flagella, showing that hydrodynamic interactions allow the sperm model to form polar orders, in which they swim alongside each other. A wave propagation model controlled by the time derivative of flagellar curvature was introduced to represent flagellar synchronization via hydrodynamic interactions. The polar state resulted in hydrodynamic flagellar synchronization due to relatively long contact time. This increased swimming speed and flagellar beat speed by around 10% compared to no synchronization. Our results showed that polar ordering enables swimming faster and more efficient swimming than solitary swimming.

Experiments show that, by placing colloidal beads coated with an actin branching activator in a solution with purified protein, it is possible to recapitulate in vitro the emergence of comet tails that generate propulsive motion through actin polymerization. Spontaneous processive motion is achieved by chemophoretic particles via mechano-chemical coupling. In this talk, we will describe a theoretical model of the spontaneous chemophoresis in synthetic actin comets and their resulting flocking behavior. To model the motion of synthetic actin comets, we apply physical first principles such as chemical advection/diffusion/reaction and solve the resulting equations to obtain the temporal evolution of beads and comet tails. In addition to movement in the bulk, we simulate the behavior of comet tails in complex geometries such as networks of microfluidic channels reminiscent of crowded cellular geometries.

Cooperation is a fundamental strategy utilized by biological systems to execute complex tasks with enhanced efficiency compared to single entities. By facilitating workload distribution, improving robustness, and increasing flexibility, cooperative behavior underpins the functionality of many biological assemblies. Despite its utility in natural systems, replicating such cooperative strategies at the molecular level within artificial systems remains a significant challenge. Realizing this capability could drive transformative advancements in the fields of microrobotics and nanotechnology. In this paper, we present a molecular transport system driven by the cooperative actions of artificial molecular machines—photoresponsive DNA-conjugated microtubules powered by kinesin motor proteins. Photoresponsive DNA conjugates mediate mechanical communication among the microtubules, enabling their light-induced organization into transport groups. These microtubule groups exhibit the ability to load, transport, and unload cargo, with cargo unloading achieved through the dissociation of transporter groups into individual microtubules. The formation of microtubule groups significantly enhances the system’s cargo-handling capabilities, allowing for the transport of larger and more numerous cargoes over longer distances compared to single microtubule-based transporters. Furthermore, we demonstrate spatially controlled cargo collection through ultraviolet light irradiation, enabling user-defined cargo aggregation locations. This work provides a proof-of-concept for cooperative task execution by artificial molecular machines, establishing a foundation for the development of molecular robotic systems with advanced functionalities.

In small scales, diffusion exceeds agitation by flows. Inside a cell, there are many kinds of active proteins with functions, such as molecular motors, ion channels, and so on. They change their conformation by consuming chemical energy, and such conformation changes can induce diffusion enhancement. In this study, we have analyzed the diffusion enhancement by such active agents. We have investigated based on two mathematical models: In the first one, it is assumed that the active proteins considered to be force dipoles and induce flows incoherently. In the other one, the system is composed with active agents without medium (fluid), and they change their shape and interact only through excluded volume effect. In both cases, it can be shown that diffusion is enhanced by the activity of agents. This is the results of cooperative effect of active agents. To elucidate the effect of single active agent on the diffusion, we also investigated the cooperative effect of diffusion and flow induced by an active agent. It can be prove that diffusion under a reciprocal flow is grater than a pure diffusion, though a reciprocal flow does not results in net convection.

One of the key achievements of equilibrium polymer physics is the prediction of scaling laws governing the viscoelastic properties of entangled polymer systems, validated in both natural polymers, such as DNA, and synthetic polymers, including polyethylene, which form materials like plastics. Recently, focus has shifted to active polymers—systems composed of motile units driven far from equilibrium, such as California blackworms, self-propelled biopolymers, and soft robotic grippers. Despite their growing importance, we do not yet understand their viscoelastic properties and universal scaling laws. Here, we use Brownian dynamics simulations to investigate the viscoelastic properties of highly-entangled, flexible self-propelled polymers. Our results demonstrate that activity enhances the elasticity by orders of magnitude due to the emergence of grip forces at entanglement points, leading to its scaling with polymer length $\sim L$. Furthermore, activity fluidizes the suspension, with the long-time viscosity scaling as $\sim L^2$, compared to $\sim L^3$ in passive systems [1]. These insights open new avenues for designing activity-responsive polymeric materials. 1. Giant Activity-Induced Elasticity in Entangled Polymer Solutions D. Breoni, C. Kurzthaler, B. Liebchen, H. Löwen, and S. Mandal Nature Communications (2025, in press)

Living cells exhibit non-equilibrium dynamics emergent from the intricate interplay between molecular motor activity and its viscoelastic cytoskeletal matrix. The deviation from thermal equilibrium can be quantified through frequency-dependent effective temperature or time-reversal symmetry breaking quantified e.g. through the Kullback-Leibler divergence. Here, we investigate the fluctuations of an AFM tip embedded within the active cortex of mitotic human cells with and without perturbations that reduce cortex activity through inhibition of material turnover or motor proteins. While inhibition of motor activity significantly reduces both effective temperature and time irreversibility, inhibited material turnover leaves the effective temperature largely unchanged but lowers the time irreversibility. Our experimental findings in combination with a minimal model highlight that time irreversibility and effective temperature can follow opposite trends in active living systems, challenging in particular the validity of effective temperature as a proxy for the distance from thermal equilibrium. Furthermore, we propose that the strength of thermal noise and the occurrence of time-asymmetric deflection spikes in the dynamics of regulated observables are inherently coupled in living systems, revealing a previously unrecognized link between entropy production and time irreversibility.

Modeling of active polymers is a fast growing field in active matter physics because of its implications on understanding the individual and collective dynamics of active biological filaments. However, the role of hydrodynamic interactions is still not fully understood. We employ mesoscopic simulations to study active polymers in a solvent via multi-particle collision dynamics. We investigate linear chains in which either the head or tail monomer exerts an active force, directed away from or towards its neighbor, respectively, while the remaining monomers are passive. We find that in stiff chains, the position of the active monomer has minimal influence on both the structural and dynamic properties of the chain. In flexible chains, however, its effect is strongly pronounced. An active head monomer pulls the chain behind it, straightening the backbone-an effect that can be interpreted as effective stiffening. In contrast, an active tail pushes into the chain, causing crumpling. This leads to faster decorrelation of the polymer backbone over time, rendering the active motion less persistent. These effects occur regardless of whether hydrodynamic interactions are included. Additionally, we examine the effect of applying contractile vs. extensile force dipoles between the active monomer and the solvent. While one might expect the corresponding puller- or pusher-type flow fields to emerge, we find that the active force propagates along the chain, resulting in more complex force multipoles. In simulations of single chains, the hydrodynamic flow fields played a relatively minor role. However, we are now studying the collective behavior of monolayers of active polymers, where hydrodynamic interactions are expected to play a central role.

The spatio-temporal dynamics of cellular protein concentrations near membranes composed of different lipids is modeled herein by a three-variable continuum model describing the concentration of a protein attached to the membrane, the concentration of a protein freely diffusing in the cytosol and the local composition of lipids in a binary membrane. This models contains two globally conserved fields representing (i) the total protein content (sum of integrated cytosolic and membrane bound protein concentrations) and (ii) the averaged fractions of the two lipid species in the membrane. The model combines a simple conserved reaction-diffusion model originally derived to study cell polarization (for the protein dynamics) coupled to a Cahn-Hilliard equation describing phase separation or mixing of the two lipids species in the membrane. By means of linear stability of the spatially homogeneous steady state of the systems and by direct numerical simulation of the full equations, we find that the lipid dynamics can undergo classical phase separation for intermediate compositions with initially shorter wavelength. In contrast, the protein dynamics shows an oscillatory phase separation for intermediate total protein contents related to a long-wave length instability with complex eigenvalues and leading to traveling domains. Multiscale pattern formation with larger scale traveling and rotation protein domains coexisting with smaller scale stationary lipid domains are found in parameter regions where both of these instabilities are present. In the parameter regime of such multiscale patterns, we find both traveling protein domains and arrested coarsening of stationary lipid domains above a critical coupling between the protein and lipid dynamics. It is also shown that the basic instabilities and the phase diagram of the model are well captured by a few extensions of a conserved FitzHugh-Nagumo model recently suggested by Brauns and Marchetti in Phys. Rev. X 2024 as qualitative model for non-reciprocal pattern formation. This extended model is given by two non-reciprocally coupled Cahn-Hilliard equations allowing for instabilities of both involved species. This qualitative model also contains substantially different interface tensions in the two considered species mimicking the different physical properties of lipids and proteins in a cellular situation. The latter aspect also helps to explain the asymmetry in behavior (static lipid vs. travelin protein pattens) found in the simulations of the full model.

Active matter is a class of out-of-equilibrium systems composed of self-driven individuals, which are widely found across scales from the macroscopic to the microscopic. Typical examples include animal groups, cell tissues, micro-nano machines, and molecular motors. The individual components of these systems break detailed balance by consuming locally stored free energy to achieve self-propelled motion, and through interactions, they exhibit a rich variety of collective behaviors. In many active matter systems, the structure or motion of individuals exhibits chirality (i.e., mirror asymmetry), a feature that further extends the dynamic characteristics of active matter, enhancing the complexity of both individual and collective behaviors. This has thus attracted significant research interest. This talk will focus on a series of works from our research group on chiral active matter, including disordered hyperuniform states in chiral algal systems, chiral boundary flows in bacterial systems, and chiral emergence phenomena in non-reciprocal robotic systems. We reveal the out-of-equilibrium characteristics of these chiral dynamic phenomena from both theoretical and experimental perspectives, while also exploring the potential impacts and practical applications of chiral active matter in fields such as biology, mathematical modeling, and engineering.

Biological collectives often adapt to changing environmental conditions by reorganizing their physical structure. Honeybee swarms provide a striking example: composed of thousands of workers and a queen, the swarm adapts its morphology to buffer perturbations such as wind, rain, and temperature fluctuations while maintaining biological function. We combine physics-based modeling with behavioral experiments to investigate how the swarm’s architecture enables it to sense and respond to environmental changes. To connect local interactions to global adaptations, we use time-lapse X-ray computed tomography and machine learning to track ~10⁴ individual bees within the swarm. We find that bees self-organize into a thermally induced, nematic-like state, exhibiting long-range orientational order. Without central control, this organization emerges as individual bees modify their physical bonds with neighbors in response to local signals (e.g., tension in bee-bee bonds and pheromone-driven aggregation) and dynamic environmental cues, driving local order and global morphological change. During cyclic thermal fluctuations, small morphological changes occur within individual oscillations, while larger changes accumulate over multiple cycles, demonstrating how memory of past conditions shapes adaptive responses across timescales. We explore how these structural adaptations reflect trade-offs faced by individuals and the collective during the multifunctional optimization of competing biological objectives.

Cyanobacteria, a group of multi-cellular organisms that form elongated filaments, are thought to be the first photosynthetic organisms to evolve on Earth. Some of them can glide on solid surfaces by secreting mucilage. During colonization of a surface, the cyanobacteria undergo a transition from an isotropic state to intricate patterns in density and orientation. These patterns emerge at scales much larger than the length of a single filament. Combining experiments with novel theoretical and numerical modelling, we explore the emergence of these patterns and how the emerging structures depend on the properties on the individual filaments. Our simulations reproduce quantitatively the experimental results and point to a coupling of density and nematic alignment among filaments.

Enzymatic nanomotors harvest kinetic energy through the catalysis of chemical fuels. When a drop containing nanomotors is placed in a fuel-rich environment, they assemble into ordered groups and exhibit intriguing collective behaviour akin to the bioconvection of aerobic microorganismal suspensions. This collective behaviour presents numerous advantages compared to individual nanomotors, including expanded coverage and prolonged propulsion duration. However, the physical mechanisms underlying the collective motion have yet to be fully elucidated. Our study investigates the formation of enzymatic swarms using experimental analysis and computational modelling. We show that the directional movement of enzymatic nanomotor swarms is due to their solutal buoyancy. We investigate various factors that impact the movement of nanomotor swarms, such as particle concentration, fuel concentration, fuel viscosity, and vertical confinement. We examine the effects of these factors on swarm self-organization to gain a deeper understanding. In addition, the urease catalysis reaction produces ammonia and carbon dioxide, accelerating the directional movement of active swarms in urea compared with passive ones in the same conditions. The numerical analysis agrees with the experimental findings. Our findings are crucial for the potential biomedical applications of enzymatic nanomotor swarms, ranging from enhanced diffusion in bio-fluids and targeted delivery to cancer therapy.

Bacteria inhabit a wide range of fluidic environments—from pipelines and soil to tissue fluids—where they constantly interact with flow fields and boundaries. A key step in biofilm formation and infection onset is the transition of motile bacteria from bulk fluids to persistent surface accumulation. Among various mechanisms, hydrodynamic trapping near surfaces is especially robust and ubiquitous, making it challenging to mitigate. While external fields such as gravity, magnetism, or light have been used to redirect bacterial motion toward the center of flow channels, these methods are often ineffective in complex or irregular geometries. In this talk, I present a discovered mechanism in which dilute suspensions of actin filaments spontaneously form nematic alignment under shear flow and actively regulate bacterial distribution. The aligned filaments generate orientational torques on swimming bacteria, suppressing flagella-induced chiral drift and driving them away from surfaces toward the flow centerline—where fluid velocity is maximal and bacteria can be efficiently advected. Comparative experiments across swimming bacterial species and different complex fluid suspensions demonstrate the unique effectiveness of actin filaments in detaching bacteria from surfaces and promoting bulk accumulation under flow. By integrating both shear-induced alignment torque and chiral drift torque into a unified theoretical framework, we reveal the underlying orientational dynamics that govern bacterial spatial distributions in flow. These findings not only provide insights into microswimmer–fluid–structure interactions but also offer a promising strategy for developing filament-like additives to suppress surface colonization, prevent biofilm formation, and mitigate bacterial infections.

Intrinsic drive in active suspensions induces collective behavior that is unfamiliar from passive counterparts. Examples are self-driven collective flows of polar orientational order or active, mesoscale turbulence. We analyze how non-Newtonian rheology and viscoelasticity affect these collective dynamics. For active suspensions showing shear thickening of the carrier liquid, we find significant impact on mesoscale turbulence. Right- and left-handed vortices elongate, align their axes of elongation, and positionally order into centered rectangular lattices. The effect can be used to arrange additional passive particles of unmatched mass density into regular lattices. Shear-thinning active suspensions feature an extension of the turbulent regime to lower activities when compared to corresponding Newtonian fluids. This behavior emerges from hysteretic loops when increasing the activity to the turbulent regime and then reducing activity again. Elevated vorticity implies higher shear rates. The latter reduce the viscosity of the shear thinning fluid, which in turn supports turbulence. Finally, elastic contributions in viscoelastic suspensions hinder persistent orientationally ordered flows in one direction due to the associated retracting elastic forces. As a consequence, we find local circling of the fluid. That is, polar orientational order and associated flows locally rotate their directions over time. Spatially heterogeneous solutions, including stripe-like patterns, emerge. Overall, we demonstrate that adding complexity to the flow behavior of the carrier fluid in active suspensions qualitatively affects their dynamic appearance in various ways. We hope for stimulating discussions on experimental realizations and observations of our predictions.

One of the hallmarks of bacterial biofilms is their ability to display a broad array of phenotypes even in isogenic populations. An example of such plasticity is the motility-matrix switch present in B. subtilis biofilms, where cells are known to change from a motile state, in which the biofilm resembles a viscous fluid, to a matrix state, in which the biofilm is closer to an elastic solid, and back. By collecting fluorescence time-lapse data on different combinations of knock-out strains and the wild-type, we find that the mixture of the two phenotypes is essential for proper biofilm development. Intriguingly, mixing two strains each able to perform one task preserves the biofilm morphology suggesting that the local and temporal pattern in phenotype is a result of environmental selection, rather than bacterial response to different external cues. Further exploration shows that matrix production is essential for the emergence of wrinkly structures in the biofilms, which can be leveraged by antibiotic resistant cells to escape and rescue the bacterial population when the environment changes, providing evidence of an evolutionary function for these structural features. Importantly, the escaping cells need to able to display both phenotypes to successfully take advantage of the biofilm wrinkles.

Motile microorganisms like bacteria and algae combine self-propulsion, cooperation, and decision-making at the micron scale. Inspired by these biological systems, synthetic microswimmers are emerging as human-made counterparts capable of self-propulsion. Recent breakthroughs provide a platform to integrate additional functionalities, bridging the gap between biology and synthetic systems. We propose and experimentally demonstrate a mechanism enabling synthetic microswimmers—such as autophoretic colloids, droplet swimmers, and ion-exchange-driven modular swimmers—to make autonomous navigational decisions. These swimmers generate chemo-hydrodynamic signals that interact with boundaries, creating echoes that carry structural information about the environment. Remarkably, these echoes invoke automatic responses, such as synthetic chemotaxis, enabling the swimmers to avoid dead ends and autonomously find paths through complex mazes. Our findings illustrate how simple physical principles can endow synthetic systems with advanced navigation functionalities.

TBA

Active matter has emerged as a new central field within statistical physics. A defining characteristic of active matter is its capacity to harness energy to facilitate self propulsion. Usual active matter, however, remains inherently ‘dead’, lacking the capability to process information, evolve and to adapt to its environment beyond predetermined policies. This limitation has recently fueled growing interest in the concept of smart matter—active systems endowed with learning and responsive capabilities (1,2). In this talk, I will present a new statistical physics framework for smart matter, where agents dynamically tune their interaction parameters—or policy—to maximize a reward function, thereby steering the system toward a desired collective state. Learning occurs in a decentralized manner: agents not only adapt individually but also share their acquired policies with neighbors. By embedding the learning process into a kinetic theory framework through a reward-dependent interaction rule, we derive hydrodynamic equations that describe the evolution of policy fields. These equations allow for an analytical characterization of collective learning in smart agent systems (3). We apply this framework to spatially heterogeneous environments, where agents must continually adapt to maintain high rewards amid evolving conditions. Inspired by robotic experiments, using theory and agent-based simulations, we explore a phenomenon we term teaching-induced aggregation, in which collective learning enables agents to optimize their proximity to an external light source. Altogether, our approach offers a foundational analytical perspective on the behavior and design of smart, evolving matter. REFERENCES (1) C. Kaspar et al., "The rise of intelligent matter", Nature 594 (2021) 345. (2) H. Levine and D. I. Goldman, "Physics of smart active matter: integrating active matter and control to gain insights into living systems", Soft Matter 19 (2023) 4204. (3) G. Jung, M. Ozawa and E. Bertin, "Kinetic theory of decentralized learning for smart active matter", arXiv:2501.03948, accepted in Physical Review Letters (2025)

Natural microswimmers use various swimming gaits to propel under low Reynolds number conditions in their fluid surrounding for various reasons: search for nutrition, escape from predators or search for prey. A very common strategy for propulsion is the non-reciprocal deformation of the shape of the swimmer in an effort to realize its motion through the medium. In recent times substantial efforts have been made to design artificial swimmers that can successfully mimic their natural counterparts and are thus able to perform specific tasks, such as targeted drug delivery in the case of nanomedical applications. In this work, we train a two-dimensional, triangular swimmer [1] to move in a desired direction using different propulsion gaits. This training is achieved with adaptive neural networks (which connect the degrees of freedom and the forces acting on the swimmer), involving thereby the NEAT algorithm [2] which optimizes the internal architecture of these networks. While in a previous work [3] a simple one-dimensional, linear three-bead swimmer was trained to swim in a chemical landscape [3], we focus here on the considerably more challenging case of the triangular swimmer: flapping (similar to the swimming gait of Chlamydomonas), chiral (drifting circle) and walking respectively are three emergent non-reciprocal propulsion modes that we have found. In two dimensions, a simple reward, for example, displacement, cannot induce motility in the swimmer, and in the absence of a well-defined reward scheme, the swimmer either stays stationary or rotates in a circular path with no net displacement. However, a complex reward scheme, which is a function of instantaneous displacement, average displacement, angular displacement, and shape factor, can be beneficial for the swimmer to set it into motion. Similar as in Ref. [1], we are able to extract valuable information about the swimmer’s strategies by analyzing the internal structure of the emerging networks. Notably, the emergent networks corresponding to different swimming gaits are simple in nature though visibly different for different swimming gaits [4]. [1] M. S. Rizvi, A. Farutin, and C. Misbah, Phys. Rev. E, 97(2) (2018), p.023102. [2] K. O. Stanley, R. Miikkulainen, Evol. Comput., 10 (2002), 99. [3] B. Hartl, M. Hϋbl, G. Kahl, and A. Zӧttl, Proc. Natl. Acad. Sci. USA., 118 (2021), e2019683118. [4] R. Maity, Maximilian Hϋbl, Julian Lemmel, Benedikt Hartl, and Gerhard Kahl, Emergent swimming strategies of an intelligent triangular three-bead swimmer, Submitted.

Living systems operating far from thermal equilibrium employ well-defined regulation and adaptation processes that depend on molecular interactions and feedback mechanisms. Regulation and adaptation in synthetic active particles is, however, largely absent due to the simplicity of their realization. We experimentally demonstrate how two non-equilibrium processes generate a regulated polarization state of synthetic active particles in localized osmotic flow fields. Employing a single heat source at a solid-liquid interface, thermally induced osmotic and phoretic forces simultaneously repel and attract active particles, causing them to orbit at a stable distance determined by the source temperature. The steady state achieved by these temperature-induced processes results in particle polarization that remains constant irrespective of the heat source temperature—demonstrating self-regulation through inherent properties rather than external control systems. Through independent control of the heat source and active particles, we explore this self-regulated polarization effect, revealing the predominance of hydrodynamic interactions. This thermo-osmotic flow regulation mechanism illustrates how synthetic active matter can autonomously respond and control its local environment. Using multiple heat sources may allow for dynamic decision-making based on physical signals.

Microswimmers consist of different body parts which enables them to deform their shape periodically in order to locomote in viscous fluids at low Reynolds number, realized, for example by waving attached cilia or flagella, or by deforming their entire cell body. Our aim is to understand how microswimmers can change their deformation and swimming gaits based on sensory input of their environment and internal state. When coupled to a physical environment a microswimmer can not just decide where to go, but how to deform its shape. Physics will then tell us where to go. We model the microswimmer decision-making on the sensory-based deformation using reinforcement learning. Using genetic algorithms of evolving artificial neural networks allows us to extract simple artifical decison-making principles.

Recently, much attention has been paid to active systems that utilize information instead of energy, i.e., informational active matter [1]. During the last decade, there have been many studies on information engines that use information to extract mechanical work. We first discuss the locomotion of a thermally driven elastic two-sphere microswimmer with internal feedback control that is realized by the position-dependent friction coefficients [2]. In our model, the two spheres are in equilibrium with independent heat baths having different temperatures, causing a heat flow between the two spheres. We generally show that the average velocity of the microswimmer is finite when the friction coefficients are position-dependent. Using the framework of stochastic thermodynamics, we obtain the entropy production rate and further discuss the efficiency of the thermal microswimmer. The proposed self-propulsion mechanism highlights the importance of information in active matter and can be a fundamental process in various biological systems. Moreover, using an underdamped active Ornstein-Uhlenbeck particle, we propose two information swimmer models having either external or internal feedback control and perform their numerical simulations [3]. Depending on the velocity that is measured after every fixed time interval (measurement time), the friction coefficient is modified in the externally controlled model, whereas the persistence time for the activity is changed in the internally controlled one. In the steady state, both of these information swimmers acquire finite average velocities in the noisy environment, and the efficiency can be maximized by tuning the measurement time. The internally controlled swimmer is more relevant to biological systems and can generally achieve a larger average velocity and efficiency than the externally controlled model. [1] B. VanSaders and V. Vitelli, arXiv:2302.07402 [2] J. Li, Z. Zhang, Z. Hou, Y. Hosaka, K. Yasuda, L. He, S. Komura, arXiv:2502.20752 [3] Z. Hou, Z. Zhang, J. Li, K. Yasuda, and S. Komura, EPL 150, 27001 (2025).

Systems of interacting active particles can show diverse collective states, including motility-induced phase separation, flocking, etc. To what extent these collective states are influenced by the details of the microscopic interaction is not fully understood. To shed light on this question, we investigate a class of active particle models, in which the particles differ in shape and center of mass. Depending on the particles' properties, even simple steric interactions can then induce torques on the active particles. We find that the emergent collective states can differ significantly between the different types of particles. Our study therefore helps to classify the influence of microscopic interactions on the emergent collective states in active matter and bears implications for the development of continuum theories. Joint work with Colin-Marius Koch.

In this talk I will first present the susceptibility of long-range ordered phases of two-dimensional dry aligning active matter to population disorder, taken in the form of a distribution of intrinsic individual chiralities. Using a combination of particle-level models and hydrodynamic theories derived from them, we show that while in finite systems all ordered phases resist a finite amount of such chirality disorder, the homogeneous ones (polar flocks and active nematics) are unstable to any amount of disorder in the infinite-size limit. I will further show that spontaneous density segregation in dense systems of aligning circle swimmers is a condensation phenomenon at odds with the phase separation scenarios usually observed in two-dimensional active matter. The condensates, which take the form of vortices or rotating polar packets, can absorb a finite fraction of the particles in the system, and keep a finite or slowly growing size as their mass increases. Our results are obtained both at particle and continuous levels. We consider both ferromagnetic and nematic alignment, and both identical and disordered chiralities. Condensation implies synchronization, even though our systems are in 2D and bear strictly local interactions. We propose a phenomenological theory based on observed mechanisms that accounts qualitatively for our results.

Self-propelled particles with local alignment interactions display collective motion or flocking, as predicted by the Vicsek model and its numerous variants. Systems of self-propelled particles consisting of two species that anti-align or even interact non-reciprocally show even more complex collective phenomena, ranging from anti-parallel flocking over run-and chase behavior to chiral phases. Large ensembles of self-propelled particles may consist of many species with varying inter-species interactions. Whether flocking can survive the presence of anti-alignment and/or non-reciprocal interactions re-mained elusive so far. As a first step to shed light into collective phenomena possibly emerging in self-propelled multi-species ensembles with heterogeneous inter-species interactions we consider here the extreme case of a Vicsek-like model with fully random non-reciprocal interactions between the individual particles. First we analyze its mean-field theory and derive analytically the dynamical self-consistency equations. Depending on the average non-reciprocity and alignment-bias parameters a disordered phase with short-range correlations or macroscopic flocking occurs. Suprisingly, for sufficiently large alignment-bias, the random non-reciprocal interactions can support a global chiral phase in which the collective movement direction rotates slowly. Next we investigate short-range interactions (in 2d) numerically and find that for small interaction ranges or low densities the collective mean-field behavior is destroyed. Instead a self-organized clique formation emerges, in which medium size clusters of particles that have predominantly aligning interactions meet accidentally and stay together for macroscopic times. The analysis of spatial correlation functions provides further insight into the spatio-temporal evolution of this collective self-organization in a randomly and non-reciprocally interacting many-particles system. We believe that our study may serve as a starting point of the study of multi-species flocking models with non-random but complex inter-species interactions.

Classical thermodynamic concepts have proven challenging to define rigorously in active systems, which are inherently driven far from equilibrium. Instead of attempting to adapt equilibrium thermodynamics to active matter, we attempt to reverse the approach: constructing a minimal, fully measurable macroscopic experimental system to test whether thermodynamic variables naturally emerge and align with classical definitions. We set up an experimental platform consisting of macroscopic spheres individually propelled by internal motors. We designed the system to remotely monitor the energy input to each of the motors in a non-invasive manner. By continuously tracking energy consumption and its direct conversion into translational and rotational kinetic energy at the single-agent level, we can infer the local energy dissipation. These agents spontaneously self-organize from a disordered state resulting in chaotic individual motion to coherently moving synchronized states. The transitions between these states correlates with measurable shifts in energy consumption, effective temperature, and entropy production, due to changes in the utilized degrees of freedom within the system. Complementing our experiments, we developed a digital twin—a numerical model reproducing the mechanics of the singe sphere and the system’s dynamics. Our model provides insights and lets us tune dynamics that are experimentally inaccessible. Together, our experiments and numerical model create a potent platform to explicitly test theoretical predictions. In particular, we attempt to address the question: How distinct active matter is from equilibrium thermodynamics?

Stochastic thermodynamics offers powerful tools to quantify entropy production, dissipation, and energy efficiency via measurements of time-reversal symmetry breaking (TRSB) in small-scale, fluctuating processes. These connections, however, rely on the assumption that all dissipative degrees of freedom are accessible and resolved in the model. In the presence of hidden degrees of freedom, the relation between TRSB and dissipation becomes elusive, creating a conceptual gap for complex many-body systems. Despite this challenge, growing research efforts aim to quantify dissipation and TRSB in active matter. I will review recent insights, open questions, and points of discussion. As an example, I will show how TRSB can serve as an order parameter for detecting dynamical phase transitions in systems with dissipative pattern formation. Suchanek, Kroy, & Loos, PRL (2023), PRE 108, 064610 (2023), PRE 108, 064123 (2023). Martynec, Klapp, & Loos, NJP (2021). Loos, Klapp, & Martynec, PRL (2023).