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1. Toward Task Capable Active Matter: Learning to Avoid Clogging in Confined Collectives via Collisions

   https://doi.org/10.3389/fphy.2022.735667  

   Aina, Kehinde O.; Avinery, Ram; Kuan, Hui-Shun; Betterton, Meredith D.; Goodisman, Michael A.; Goldman, Daniel I. (June 2022, Frontiers in Physics)

   Social organisms which construct nests consisting of tunnels and chambers necessarily navigate confined and crowded conditions. Unlike low density collectives like bird flocks and insect swarms in which hydrodynamic and statistical phenomena dominate, the physics of glasses and supercooled fluids is important to understand clogging behaviors in high density collectives. Our previous work revealed that fire ants flowing in confined tunnels utilize diverse behaviors like unequal workload distributions, spontaneous direction reversals and limited interaction times to mitigate clogging and jamming and thus maintain functional flow; implementation of similar rules in a small robophysical swarm led to high performance through spontaneous dissolution of clogs and clusters. However, how the insects learn such behaviors and how we can develop “task capable” active matter in such regimes remains a challenge in part because interaction dynamics are dominated by local, potentially time-consuming collisions and no single agent can survey and guide the entire collective. Here, hypothesizing that effective flow and clog mitigation could be generated purely by collisional learning dynamics, we challenged small groups of robots to transport pellets through a narrow tunnel, and allowed them to modify their excavation probabilities over time. Robots began excavation with equal probabilities to excavate and without probability modification, clogs and clusters were common. Allowing the robots to perform a “reversal” and exit the tunnel when they encountered another robot which prevented forward progress improved performance. When robots were allowed to change their reversal probabilities via both a collision and a self-measured (and noisy) estimate of tunnel length, unequal workload distributions comparable to our previous work emerged and excavation performance improved. Our robophysical study of an excavating swarm shows that despite the seeming complexity and difficulty of the task, simple learning rules can mitigate or leverage unavoidable features in task capable dense active matter, leading to hypotheses for dense biological and robotic swarms. 

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2. Geometric control of motility-induced phase separation

   https://doi.org/10.1039/D6SM00213G  

   Webb, Toler H; Ansell, Helen S; Sussman, Daniel (May 2026, Soft Matter)

   Curvature fundamentally alters the collective properties of soft, active, and biological materials. Here we study motility-induced phase separation (MIPS), a canonical non-equilibrium transition, and demonstrate that even weak and slowly varying curvature provides robust geometric control over the dense MIPS phase. This includes dictating both the location and morphology of the MIPS cluster, even in regimes where curvature has minimal effect on the overall phase boundaries. Focusing on active Brownian particles confined to the surface of a torus, we show that varying the aspect ratio drives a structural transition of the dense cluster from a disk localized at the outer equator to a band wrapping the minor circumference. We then discuss how the curved geometry provides a platform for comparing different theoretical frameworks for the MIPS phase: by analyzing the geometries of the cluster boundaries, we show that the dense phase shape is more consistent with a boundary-length-minimizing, thermodynamic picture than with the simplest kinetic picture in the large-particle-number limit. Our results establish curved space not only as a tool to shape and guide non-equilibrium dynamics, but as a uniquely sensitive arena for probing the fundamental mechanisms of active matter. 

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3. Emergent Collective Locomotion in an Active Polymer Model of Entangled Worm Blobs

   https://doi.org/10.3389/fphy.2021.734499  

   Nguyen, Chantal; Ozkan-Aydin, Yasemin; Tuazon, Harry; Goldman, Daniel. I.; Bhamla, M. Saad; Peleg, Orit (September 2021, Frontiers in Physics)

   null (Ed.)

   Numerous worm and arthropod species form physically-connected aggregations in which interactions among individuals give rise to emergent macroscale dynamics and functionalities that enhance collective survival. In particular, some aquatic worms such as the California blackworm ( Lumbriculus variegatus ) entangle their bodies into dense blobs to shield themselves against external stressors and preserve moisture in dry conditions. Motivated by recent experiments revealing emergent locomotion in blackworm blobs, we investigate the collective worm dynamics by modeling each worm as a self-propelled Brownian polymer. Though our model is two-dimensional, compared to real three-dimensional worm blobs, we demonstrate how a simulated blob can collectively traverse temperature gradients via the coupling between the active motion and the environment. By performing a systematic parameter sweep over the strength of attractive forces between worms, and the magnitude of their directed self-propulsion, we obtain a rich phase diagram which reveals that effective collective locomotion emerges as a result of finely balancing a tradeoff between these two parameters. Our model brings the physics of active filaments into a new meso- and macroscale context and invites further theoretical investigation into the collective behavior of long, slender, semi-flexible organisms. 

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4. Absorbing–active transition in a multi-cellular system regulated by a dynamic force network

   https://doi.org/10.1039/C9SM01244C  

   Nan, Hanqing; Zheng, Yu; Lin, Yiheng H.; Chen, Shaohua; Eddy, Christopher Z.; Tian, Jianxiang; Xu, Wenxiang; Sun, Bo; Jiao, Yang (September 2019, Soft Matter)

   Collective cell migration in 3D extracellular matrix (ECM) is crucial to many physiological and pathological processes. Migrating cells can generate active pulling forces via actin filament contraction, which are transmitted to the ECM fibers and lead to a dynamically evolving force network in the system. Here, we elucidate the role of this force network in regulating collective cell behaviors using a minimal active-particle-on-network (APN) model, in which active particles can pull the fibers and hop between neighboring nodes of the network following local durotaxis. Our model reveals a dynamic transition as the particle number density approaches a critical value, from an “absorbing” state containing isolated stationary small particle clusters, to an “active” state containing a single large cluster undergoing constant dynamic reorganization. This reorganization is dominated by a subset of highly dynamic “radical” particles in the cluster, whose number also exhibits a transition at the same critical density. The transition is underlaid by the percolation of “influence spheres” due to the particle pulling forces. Our results suggest a robust mechanism based on ECM-mediated mechanical coupling for collective cell behaviors in 3D ECM. 

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5. Synchronous and Fully Steerable Active Particle Systems for Enhanced Mimicking of Collective Motion in Nature

   https://doi.org/10.1002/adma.202304759  

   Chen, Zhihan; Ding, Hongru; Kollipara, Pavana Siddhartha; Li, Jingang; Zheng, Yuebing (August 2023, Advanced Materials)

   Abstract The collective motion observed in living active matter, such as fish schools and bird flocks, is characterized by its dynamic and complex nature, involving various moving states and transitions. By tailoring physical interactions or incorporating information exchange capabilities, inanimate active particles can exhibit similar behavior. However, the lack of synchronous and arbitrary control over individual particles hinders their use as a test system for the study of more intricate collective motions in living species. Herein, we propose a novel optical feedback control system that enables the mimicry of collective motion observed in living objects using active particles. This system allows for the experimental investigation of the velocity alignment, a seminal model of collective motion (known as the Vicsek model), in a microscale perturbed environment with controllable and realistic conditions. We observe the spontaneous formation of different moving states and dynamic transitions between these states. Additionally, we quantitatively validate the high robustness of the active‐particle group at the critical density under the influence of different perturbations. Our findings support the effectiveness of velocity alignment in real perturbed environments, thereby providing a versatile platform for fundamental studies on collective motion and the development of innovative swarm microrobotics. This article is protected by copyright. All rights reserved 

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