We show that non-Brownian suspensions of repulsive spheres below jamming display a slow relaxational dynamics with a characteristic timescale that diverges at jamming. This slow timescale is fully encoded in the structure of the unjammed packing and can be readily measured via the vibrational density of states. We show that the corresponding dynamic critical exponent is the same for randomly generated and sheared packings. Our results show that a wide variety of physical situations, from suspension rheology to algorithmic studies of the jamming transition are controlled by a unique diverging timescale, with a universal critical exponent.

Can a bath of isotropic but active particles promote ordering of anisotropic but passive particles? In this Letter, we uncover a fluctuation-driven mechanism by which this is possible. Somewhat counterintuitively, we show that the passive particles tend to be more ordered upon increasing the noise strength of the active isotropic bath. We first demonstrate this in a general dynamical model for a nonconserved order parameter (model $A$) coupled to an active isotropic field and then concentrate on two examples: (i) a collection of polar rods on a substrate in an active isotropic bath and (ii) a passive apolar suspension in a momentum conserved, actively forced but isotropic fluid, which are relevant for current research in active systems. Our theory, which is relevant for understanding ordering transitions in out-of-equilibrium systems can be tested in experiments, for instance, by introducing a low concentration of passive rodlike objects in active isotropic fluids and, since it is applicable to any nonconserved dynamical field, may have applications far beyond active matter.

Experiments have suggested that bacterial mechanosensitive channels separate into 2D clusters, the role of which is unclear. By developing a coarse-grained computer model we find that clustering promotes the channel closure, which is highly dependent on the channel concentration and membrane stress. This behaviour yields a tightly regulated gating system, whereby at high tensions channels gate individually, and at lower tensions the channels spontaneously aggregate and inactivate. We implement this positive feedback into the model for cell volume regulation, and find that the channel clustering protects the cell against excessive loss of cytoplasmic content.

Recurrent neural networks (RNN) are powerful tools to explain how attractors may emerge from noisy, high-dimensional dynamics. We study here how to learn the $\sim N^2$ pairwise interactions in a RNN with $N$ neurons to embed $L$ manifolds of dimension $D\ll N$. We show that the capacity, i.e., the maximal ratio $L/N$, decreases as $|\log \epsilon {|}^{-D}$, where $\epsilon$ is the error on the position encoded by the neural activity along each manifold. Hence, RNN are flexible memory devices capable of storing a large number of manifolds at high spatial resolution. Our results rely on a combination of analytical tools from statistical mechanics and random matrix theory, extending Gardner’s classical theory of learning to the case of patterns with strong spatial correlations.

We study the capacity of active matter to rise in thin tubes against gravity and other related phenomena like wetting of vertical plates and spontaneous imbibition, where a wetting liquid is drawn into a porous medium. This capillary action or capillarity is well known in classical fluids and originates from attractive interactions between the liquid molecules and the container walls, and from the attraction of the liquid molecules among each other. We observe capillarity in a minimal model for scalar active matter with purely repulsive interactions, where an effective attraction emerges due to slowdown during collisions between active particles and between active particles and walls. Simulations indicate that the capillary rise in thin tubes is approximately proportional to the active sedimentation length $\lambda$ and that the wetting height of a vertical plate grows superlinear with $\lambda$. In a disordered porous medium the imbibition height scales as $⟨h⟩\propto \lambda {\varphi }_m$, where ${\varphi }_m$ is its packing fraction. These predictions are highly relevant for suspensions of sedimenting active colloids or motile bacteria in a porous medium under the influence of a constant force field.

Catalytic colloidal swimmers that propel due to self-generated fluid flows exhibit strong affinity for surfaces. Here, we report experimental measurements of a significant dependence of such microswimmers’ speed on the nearby substrate material. We find that speeds scale with the solution contact angle $\theta$ on the substrate, which relates to the associated hydrodynamic substrate slip length, as $V\propto (\cos \theta +1{)}^{-3/2}$. We show that such dependence can be attributed to osmotic coupling between swimmers and substrate. Our work points out that hydrodynamic slip at nearby walls, though often unconsidered, can significantly impact microswimmer self-propulsion.

Microbial cells generally leak various metabolites including those necessary to grow. Why cells secrete even essential chemicals so often is, however, still unclear. Based on analytical and numerical calculations, we show that if the intracellular metabolism includes multibody (e.g., catalytic) reactions, leakage of essential metabolites can promote the leaking cell’s growth. This advantage is typical for most metabolic networks via “flux control” and “growth-dilution” mechanisms, as a general consequence of the balance between synthesis and growth-induced dilution with autocatalytic reactions. We further argue that this advantage may lead to a novel form of symbiosis among diverse cells.

Echo chambers and opinion polarization recently quantified in several sociopolitical contexts and across different social media raise concerns on their potential impact on the spread of misinformation and on the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena remain unclear. We propose a model that introduces the dynamics of radicalization as a reinforcing mechanism driving the evolution to extreme opinions from moderate initial conditions. Inspired by empirical findings on social interaction dynamics, we consider agents characterized by heterogeneous activities and homophily. We show that the transition between a global consensus and emerging radicalized states is mostly governed by social influence and by the controversialness of the topic discussed. Compared with empirical data of polarized debates on Twitter, the model qualitatively reproduces the observed relation between users’ engagement and opinions, as well as opinion segregation in the interaction network. Our findings shed light on the mechanisms that may lie at the core of the emergence of echo chambers and polarization in social media.

The mechanical response of packings of purely repulsive, spherical particles to athermal, quasistatic simple shear near jamming onset is highly nonlinear. Previous studies have shown that, at small pressure $p$, the ensemble-averaged static shear modulus $⟨G-G_0⟩$ scales with $p^{\alpha }$, where $\alpha \approx 1$, but above a characteristic pressure $p^{**}$, $⟨G-G_0⟩\sim p^{\beta }$, where $\beta \approx 0.5$. However, we find that the shear modulus $G^i$ for an individual packing typically decreases linearly with $p$ along a geometrical family where the contact network does not change. We resolve this discrepancy by showing that, while the shear modulus does decrease linearly within geometrical families, $⟨G⟩$ also depends on a contribution from discontinuous jumps in $⟨G⟩$ that occur at the transitions between geometrical families. For $p>p^{**}$, geometrical-family and rearrangement contributions to $⟨G⟩$ are of opposite signs and remain comparable for all system sizes. $⟨G⟩$ can be described by a scaling function that smoothly transitions between two power-law exponents $\alpha$ and $\beta$. We also demonstrate the phenomenon of compression unjamming, where a jammed packing unjams via isotropic compression.

Under extensional strain, fiber networks can exhibit an anomalously large and nonlinear Poisson effect accompanied by a dramatic transverse contraction and volume reduction for applied strains as small as a few percent. We demonstrate that this phenomenon is controlled by a collective mechanical phase transition that occurs at a critical uniaxial strain that depends on network connectivity. This transition is punctuated by an anomalous peak in the apparent Poisson’s ratio and other critical signatures such as diverging nonaffine strain fluctuations.

Cholesterol is a crucial component of mammalian cell membranes that takes part in many vital processes. It is generally accepted that cholesterol stabilizes the membrane and induces transitions into ordered states. In contrast to expectations, we demonstrate that cholesterol can destabilize the membrane by creating a nanodomain around a perpendicularly embedded ultrashort carbon nanotube (CNT), and we show that cholesterol triggers the translocation of an ultrashort CNT through the cell membrane. Using atomistic simulations, we report the existence of a nanoscale domain around an ultrashort carbon nanotube within a crossover distance of 0.9 nm from the surface of the nanotube, where the properties of the bilayer are different from the bulk: the domain is characterized by increased fluctuations, increased thickness, and increased order of the lipids with respect to the bulk. Cholesterol decreases the thickness and order of lipids and increases the fluctuations with respect to a pure lipid bilayer. Experimentally, we confirm that cholesterol nanodomains provoke spontaneous translocation of nanotubes through a lipid bilayer even for low membrane tensions. A specially designed microfluidic device allows us to trace the kinetic pathway of the translocation process and establish the threshold cholesterol concentration of 20% for translocation. The reported nanoscale cholesterol-induced membrane restructuring near the ultrashort CNT in lipid membranes enables precise control and specific targeting of a membrane using cholesterol. As an example, it may allow for specific targeting between cholesterol-rich mammalian cells and cholesterol-poor bacterial cells.

We present a comprehensive theory of the dynamics and fluctuations of a two-dimensional suspension of polar active particles in an incompressible fluid confined to a substrate. We show that, depending on the sign of a single parameter, a state with polar orientational order is anomalously stable (or anomalously unstable), with a nonzero relaxation (or growth) rate for angular fluctuations, not parallel to the ordering direction, at zero wave number. This screening of the broken-symmetry mode in the stable state does lead to conventional rather than giant number fluctuations as argued by Bricard et al., Nature 503, 95 (2013), but their bend instability in a splay-stable flock does not exist and the polar phase has long-range order in two dimensions. Our theory also describes confined three-dimensional thin-film suspensions of active polar particles as well as dense compressible active polar rods, and predicts a flocking transition without a banding instability.

Molecular dynamics simulations confirm recent extensional flow experiments showing ring polymer melts exhibit strong extension-rate thickening of the viscosity at Weissenberg numbers $\mathrm{Wi}\ll 1$. Thickening coincides with the extreme elongation of a minority population of rings that grows with Wi. The large susceptibility of some rings to extend is due to a flow-driven formation of topological links that connect multiple rings into supramolecular chains. Links form spontaneously with a longer delay at lower Wi and are pulled tight and stabilized by the flow. Once linked, these composite objects experience larger drag forces than individual rings, driving their strong elongation. The fraction of linked rings depends nonmonotonically on Wi, increasing to a maximum when $\mathrm{Wi}\sim 1$ before rapidly decreasing when the strain rate approaches $1/{\tau }_e$.

The attractive tail of the intermolecular interaction affects very weakly the structural properties of liquids, while it affects dramatically their dynamical ones. Via the numerical simulations of model systems not prone to crystallization, both in three and in two spatial dimensions, here we demonstrate that the nonperturbative dynamical effects of the attractive forces are tantamount to a rescaling of the activation energy by the glass transition temperature $T_g$: systems only differing in their attractive interaction have the same structural and dynamical properties if compared at the same value of $T/T_g$.

Cells estimate concentrations of chemical ligands in their environment using a limited set of receptors. Recent work has shown that the temporal sequence of binding and unbinding events on just a single receptor can be used to estimate the concentrations of multiple ligands. Here, for a network of many ligands and many receptors, we show that such temporal sequences can be used to estimate the concentration of a few times as many ligand species as there are receptors. Crucially, we show that the spectrum of the inverse covariance matrix of these estimates has several universal properties, which we trace to properties of Vandermonde matrices. We argue that this can be used by cells in realistic biochemical decoding networks.

We consider a three-layer Sejnowski machine and show that features learnt via contrastive divergence have a dual representation as patterns in a dense associative memory of order $P=4$. The latter is known to be able to Hebbian store an amount of patterns scaling as $N^{P-1}$, where $N$ denotes the number of constituting binary neurons interacting $P$ wisely. We also prove that, by keeping the dense associative network far from the saturation regime (namely, allowing for a number of patterns scaling only linearly with $N$, while $P>2$) such a system is able to perform pattern recognition far below the standard signal-to-noise threshold. In particular, a network with $P=4$ is able to retrieve information whose intensity is $\mathcal{O}(1)$ even in the presence of a noise $\mathcal{O}(\sqrt{N})$ in the large $N$ limit. This striking skill stems from a redundancy representation of patterns—which is afforded given the (relatively) low-load information storage—and it contributes to explain the impressive abilities in pattern recognition exhibited by new-generation neural networks. The whole theory is developed rigorously, at the replica symmetric level of approximation, and corroborated by signal-to-noise analysis and Monte Carlo simulations.

By performing extensive simulations with unprecedentedly large system sizes, we unveil how rigidity influences the fracture of disordered materials. We observe the largest damage in networks with connectivity close to the isostatic point and when the rupture thresholds are small. However, irrespective of network and spring properties, a more brittle fracture is observed upon increasing system size. Differently from most of the fracture descriptors, the maximum stress drop, a proxy for brittleness, displays a universal nonmonotonic dependence on system size. Based on this uncommon trend it is possible to identify the characteristic system size $L^{*}$ at which brittleness kicks in. The more the disorder in network connectivity or in spring thresholds, the larger $L^{*}$. Finally, we speculate how this size-induced brittleness is influenced by thermal fluctuations.