ANSSP Talks

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We show that, on a d-dimensional hypercubic lattice with d>1, conserved-mass transport processes, with multidirectional hopping that respects all symmetries of the lattice, exhibit power-law correlations for generic parameter values -- even far from phase transition point, if any. The key idea for generating the algebraic decay is the notion of multidirectional hopping, which means that several chunks of mass, or several particles, can hop out simultaneously from a lattice site in multiple directions, thus violating detailed balance. Notably, the systems we consider are described by a continuous-time Markov process; they are diffusive, lattice-rotation symmetric, and thus have no net mass current. Using hydrodynamic and exact microscopic theory, we show that, for spatial dimensions d>1, the steady-state static density-density correlation functions in the thermodynamic limit typically decay as a power-law, with power - (d+2). In particular, our theory explains why center-of-mass-conserving dynamics, used to model novel disordered hyperuniform state of matter, result in generic long-ranged correlations. Reference: Animesh Hazra, Tanmoy Chakraborty, Anirban Mukherjee, and Punyabrata Pradhan, PHYSICAL REVIEW E 112, 044130 (2025).

I will discuss how run-and-tumble particles (RTPs) on a two-dimensional square lattice undergo a re-entrant percolation transition that belongs to the superuniversality class of Ising-percolation. I will present evidence that motility-induced phase separation (MIPS) in RTPs occurs at the percolation critical point, with critical exponents related to those of percolation—consistent with the well-established correspondence between magnetism and percolation in the Ising model. Thus, the MIPS transitions can be identified as belonging to the Ising superuniversality class.

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Soft amorphous materials often flow via the deformation of
the constituent particles. In this talk, I will show how the onset of
yielding arises in different contexts of deformability, viz. shape
changes in soft rings under oscillatory shear and active
size-modulation (“breathing”) of soft particles. Both systems reveal
that deformability can delay or promote fluidization by coupling
internal shape dynamics to collective rearrangements. These results
highlight how tuning particle softness or internal activity provides
robust ways to control rigidity and flow in disordered materials.

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Stochastic information flow (SIF) quantifies how much information a subsystem gains about the rest of the system at the trajectory level. Overcoming the limitations of symmetric, ensemble-averaged measures, the SIF captures the fluctuating component of information flow, successfully characterizing the information exchange between identical components of a system. However, computational difficulties have hindered the empirical application of the SIF. In this work, we propose a scalable deep-learning method for estimating the SIF from general time-series data. Application of the method to model systems and experimental data demonstrates that the SIF is useful for characterizing phases and dynamics in a data-driven manner.

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We use coarse-grained molecular dynamics simulations,  in two dimensions, to probe the interplay between active matter and membrane mechanics in soft vesicles encapsulating environment-sensitive self-propelled particles (SPPs), modeled as polarized, disjoint-ring polymers. By systematically varying SPP packing fraction ($\rho$), motility force ($F_D$), vesicle bending rigidity ($\kappa$), and compressibility ($\sigma$), we uncover a sequence of dynamic transitions. At high $\rho$, increasing $F_D$ first induces collective vortical motion with stochastic reversals, which vanish beyond a second threshold, yielding persistent unidirectional rotation. Further enhancement of activity drives the system into a nematic-like state, deforming the vesicle into an elongated shape that exhibits ballistic motion. 

An active lattice gas model with excluded volume interactions is considered. It is shown that  hydrodynamic theory for this model predicts  a motility-induced phase separation under appropriate conditions.  The predictions from hydrodynamics  are in excellent agreement with microscopic simulations and we show results both for time evolution and steady state properties.

Resetting has been shown to reduce the mean completion time for a stochastic process. The time between two consecutive resetting events is drawn from a waiting time distribution that defines the resetting strategy or protocol. Previously, it has been shown that deterministic resetting process with a constant time period, referred to as sharp restart, can minimize the mean time to reach a fixed target. We consider the more realistic problem of a target positioned at a random distance from the resetting site, selected from a given target distribution1. We introduce the notion of a conjugate target distribution to a given waiting time distribution. For this conjugate target distribution, the waiting time distribution extremizes the mean time to locate the target. In the case of diffusion we derive an explicit expression for the conjugate target distribution to a given waiting time distribuition that holds in arbitrary spatial dimension. Our results show that stochastic resetting prevails over sharp restart for target distributions (Fig 1) with exponential or heavier tails.  

Fig 1: A schematic position vs time diagram for diffusion with resetting (in 1D). An absorbing target is located at a random distance chosen from a distribution of target centered around the origin (in gray). Two types of resetting protocols are illustrated. The red trajectory is subject to deterministic,periodic reset, i.e., where two consecutive resetting events occur after a constant interval of time. The blue trajectory is subject to stochastic reset, where the time intervals between the resetting events are taken from a waiting time distribution.

References: 1 Evans, M. and Ray, S. Physical Review Letters, 2025, 134, 247102.

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Activity and material renewability in living systems drive large-scale patterning of forces with distinctive phenotypic consequences. In the cytoskeleton, multiple myosin species bound to a crosslinked actin meshwork generate differential contractile stresses and undergo differential turnover, giving rise to a spectrum of mechanical phenotypes: from a cell-spanning force network anchored at focal adhesion sites (the canonical "stress fibers") to mechanically excitable states such as standing and travelling waves, and even their coexistence.

In this talk, we present a hydrodynamic theory of a renewable active actomyosin elastomer comprising one or more species of force generating and sensing motors. Such adaptive materials, operating far from equilibrium, continually reorganize their constituents in response to internal motor activity and external cues. This dynamic reconfiguration of the constituents modulates the effective rigidity of the meshwork and grants access to unconventional regions of mechanical response where rigidity becomes marginal, leading to a proliferation of floppy modes. We show that activity- and renewability-driven segregation and rigidity marginalization drive a uniform meshwork to spontaneously partition into spinodal stress patterns, followed by finite-time collapse of the highly contractile domains into tension-carrying singular "structures." In one, two, and three dimensions, these force-bearing structures manifest as force punctae, force chains, and force sheets, respectively. Embedded in an active elastic continuum, these singular tension structures can move, merge, and disassemble, resulting in a slow coarsening dynamics.

We provide a geometric perspective on these emergent dynamics in terms of elastic curvature flows, where the underlying metric degenerates into a fragile network of hypersurfaces. Beyond this geometric underpinning, our results provide a physical basis for the emergence of stress fibers in living cells and illustrate how state-dependent myosin turnover enables the cytoskeleton to navigate material space, giving rise to diverse functional phenotypes that can coexist within the same system.

The goal of this talk is to show that strong correlations between particles may emerge
dynamically due to a common stochastically fluctuating environment, even when there is no direct built-in interaction between particles. These correlations grow with time,
eventually driving the system into a `strongly correlated' nonequilibrium stationary
state with nontrivial properties. I will demonstrate this in an exactly solvable model of
noninteracting Brownian particles in a harmonic trap whose stiffness switches between two values at a constant rate. This model has been recently realized experimentally in optically trapped colloidal particle systems. Experimental results agree very well with theoretical predictions. Generalisation to other classical nad quantum models will also be discussed.

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We study the diffusion of a particle whose diffusion constant changes randomly over time. Specifically, it switches between values drawn from a given distribution at a fixed rate. Using a renewal approach, we derive exact expressions for the moments of the particle's position at any finite time and for any distribution with well-defined moments. In the long-time limit, we show that the cumulants of the position grow linearly with time and are directly related to the free cumulants of the underlying distribution of diffusion constants. For specific cases, we analyze the large deviations of the particle's position, revealing rich behaviors and dynamical transitions in the rate function. Our analytical predictions are confirmed by high-precision numerical simulations, achieving accuracies up to 10^{-2000}.

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Designing low-dissipation control driving protocols for small-scale systems (length-scale: ~nanometer—micrometer), where thermal fluctuations play a significant role, is an active area of research. A driving protocol refers to a procedure by which a system is driven from one configuration in its state space to another. For example, unzipping a DNA hairpin from a zipped configuration or vice versa . In this talk, I will specifically discuss designing 1 efficient driving procedures for a biomolecular motor—the ATPase , . In general, 2 3 designing such protocols is challenging due to the spatial nonlinearity of the systems and the presence of environmental thermal fluctuations. Nonetheless, a near-equilibrium (linear response ) framework is found to apply to a broad class of small-scale systems. We follow 4 this framework to design non-trivial protocols to drive the 's γ-shaft to synthesize ATP at low-dissipation cost. Our analysis reveals that the designed protocols, based on the linear response (or close-to-equilibrium) approach, dissipate lower energy as compared to the constant velocity driving protocol for a wide range of protocol durations . In the second part 5 of my talk, I will discuss our recent experimental results on the ATPase motor, where we compared the dissipation of driving this motor using two experimentally viable protocols: angle clamp and torque clamp. Our experimental results (supported by analytical findings) suggest that angle clamp driving requires less work than that of the torque clamp .

In experiments, cells in the tissue slow down as their density increases through cell division, resulting in spatially correlated glassy dynamics and solidification of the tissue. Hence, cell division is a driver of tissue solidification in experiments. Yet, in theoretical models of tissues, cell division always leads to tissue fluidization, suggesting a limitation in existing theoretical models of epithelial tissues. Here, I’ll demonstrate that tissue solidification can be achieved in these models, provided we account for a positive mechanochemical feedback loop that couples cell size to cell mechanics through cellular chemistry. I’ll also demonstrate how the crosstalk between this feedback loop and other feedback loops induces biochemical heterogeneity in the tissue.

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At density one-half in either lane of the model, the two sound velocites vanish and hence the dynamical behavior can no longer be reduced to the case of a single lane. The Popkov-Schutz model has 5 free parameters.  We will discuss their universality classes.

Besides its potential relevance to the life sciences, active matter also manifests as a novel, intrinsically non-equilibrium kind of matter, endowed with collective properties and functions absent in ordinary matter. As an example, active liquid composed of swimming bacteria was reported to turn a gear [1], which would be forbidden for ordinary liquid by the second law of thermodynamics. A challenge is how to control and design such functions of active matter. In this context, topology has emerged as a useful tool for designing robust collective phenomena in condensed matter physics [2]. However, experimental realizations of topological phenomena in active matter have thus far relied on the chirality of the active particles, which limits design capabilities.

Here we report a controlled realization of topological edge localization in dense bacterial suspension [3], induced by microfabricated geometry instead of the bacteria’s chirality. First we demonstrate that we can rectify bacterial collective motion in a channel by using its asymmetric shape. Then we construct networks made of such asymmetric channels and show that we can control the emergence of topological edge localization through the network design. Through modelling and experiments, we discuss what properties of the network and the bacterial flow are crucial to the observed topological phenomenon. We expect our results may pave the way for establishing a control and design principle of topological transport in such active matter systems.

[1] A. Sokolov et al., Proc. Natl. Acad. Sci. USA 107, 969 (2010); R. Di Leonardo et al., ibid 107, 9541 (2010).

[2] See, e.g., Nature's collection "15 years of topological matter" (2023).

[3] Y. Uchida, D. Nishiguchi, and K. A. Takeuchi, to appear.

The Loschmidt echo represents a quantum return probability that can be interpreted as large deviation in quantum dynamics, and carries an information more subtle that local measurements of density or structure functions.  We shall study the case of non-interacting fermions in one dimension released from a double domain wall state. By analyzing the corresponding Coulomb gas model, we shall show that this model exhibits a dynamical quantum phase transition of order $2 +\epsilon$,  in contrast with the celebrated third order transition of random matrix theory.

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The epithelium acts as a protective boundary for many vital organs. The nonequilibrium relaxation dynamics of epithelial tissues play crucial roles in many biological processes, including wound healing, embryogenesis, and cancer progression. The epithelium is a densely packed monolayer of cells and lies at the intersection of liquid and solid states, often referred to as a 'jammed' or 'glassy state'. The glassy dynamics in these cellular systems exhibit significant differences from that of the particulate systems. For example, the two-point correlation function decays as a compressed exponential, the mean-square displacement shows no caging effect, and the dynamics often display sub-Arrhenius relaxation, etc. Biological systems are fundamentally different from passive systems due to their inherent activity. In my talk, I will share results from our studies of simplified particulate models of active glasses and vertex-based models, and demonstrate the nonequilibrium nature of glassy dynamics in these systems. The persistence time of cellular activity has a significant impact on the glassy dynamics, resulting in their unusual characteristics. I will also present experimental findings that further illustrate this nonequilibrium behaviour.

Being a classical interacting integrable system, the dynamics of a collection of hard rods in one dimension can be described by a gas of interacting quasiparticles, each tagged by its bare velocity. I will discuss recents results describing stochastic dynamics of such quasiparticles at microscopic level and show how their dynamics is governed by hydrodynamic density correlations on the Euler space-time scale.  In this scale the hard-rod gas develops a long-range hydrodynamic correlation even when it starts from a state with short range correlation. I will briefly show how one can obtain such correlations using ballistic macroscopic fluctuation theory and how the structure of such correlation depends on the initial state. 

Two of the major outstanding questions in theoretical physics are: how to describe (a) driven or active systems which are far from equilibrium and (b) disordered systems, typically characterized by glassy relaxation. I will talk about two canonical examples of such dense active matter -- systems that reside at the intersection of these two challenging domains. In the first example, which is a dense amorphous assembly of soft self propelled particles, at the large persistence time limit, we observe exciting non-equilibrium signatures such as intermittency, active turbulence etc. In the second example, I will describe an odd solid composed of spinning particles and show that pairwise transverse interactions can lead to distinct phenomenology ranging from self-healing dynamics of grain boundaries and chiral plastic vortices, to force-chains deflection in amorphous packings.