Soft Condensed Matter

The dramatic slowing down of liquids approaching the glass transition remains unresolved, partly because competing theoretical explanations are difficult to discriminate. Indeed, several mature, contrasting frameworks account for the main features of glassy phenomenology, making agreement with familiar observations a weak basis for identifying the microscopic mechanism responsible for the slowdown. Recent numerical advances are changing this situation in two complementary ways. Deep equilibration extends controlled equilibrium studies into regimes previously accessible only by extrapolation, allowing established predictions to be stress-tested over a broader dynamical range. At the same time, new numerical methods increasingly provide direct access to theory-specific quantities such as configurational entropy, static lengths, elastic properties, and local activation barriers. I discuss how these developments constrain thermodynamic, facilitation-based, elastic, and excitation-based descriptions. The emerging picture is not yet a unique theory, but a set of increasingly discriminating tests that can reveal where apparently successful laws cease to be universal and identify the microscopic ingredients that causally control the activation barrier.

Filaments are incredible material building blocks that, when entangled together, form robust structures with remarkable adaptive mechanics, from stiff cables to stretchable knits. Basic questions about the mechanism that holds together such structures, especially assemblies like rope, without attractive physical or chemical bonds between constituents are among the oldest considered in materials science. Contradicting a long-standing picture that structural integrity of rope-like assemblies rely on a combination of external loading and inter-filament friction to prevent spontaneous detangling (e.g. fraying of a cut rope), we demonstrate that assemblies of intrinsically-curved filaments can achieve structural integrity purely from the competition of elastic forces that resist shape-change and (traction-free) repulsive forces that prevent overlap. We analyze a friction-free model of assembly of intrinsically-helical filaments, and show that depending on filament number and preferred filament shape, overcrowding in the assemblies generate elastic pre-stresses that give rise to either self-locking entanglement or spontaneous detanglement through propagation of a soliton-like detanglement front through assemblies. We test a generalized fracture criterion for spontaneous detangling and confirm the role of shape-elastic costs to self-locking via experiment and simulation of frictional filaments, demonstrating a range of bundled structures which cannot be made self-locking for any value of friction. The mechanism of ``purely elastic cohesion" illuminated here sheds light on self-locking in a broader class of entangled structures, and further, offers design principles for new classes of active filament materials capable of stimuli-responsive assembly and disassembly.

Tribocharging of granular materials is a widespread phenomenon ranging from volcanic eruptions and dust storms to pharmaceutical processing, yet its underlying mechanism remains elusive. Recent thermoelectric models have provided a quantitative framework for triboelectric charge transfer, but they have difficulty accounting for charging between identical materials. In this Letter, we propose a theory of granular tribocharging based on the interplay between particle geometry and thermoelectric response. Collision-induced frictional heating produces a size-dependent temperature asymmetry between particles, generating a thermoelectric voltage that drives charge transfer. The sign of the Seebeck coefficient determines the charging polarity, while particle size and thermal properties set its magnitude. Our multi-collision theory explains the experimentally observed size- and material-dependent reversal of charging polarity, as in quartz and nylon.

Active materials are driven out of equilibrium at the microscopic scale and display diverse phenomenology; non-reciprocity is a subclass of activity in which action-reaction symmetry is also broken. These models are often grouped by the order of symmetry of their components, such as polar bird flocks or hexatic cellular tissues. However, many biological examples exist in which the components appear to have two or more symmetries simultaneously. To study this effect we introduce two independently controllable symmetries to the non-reciprocal XY model. This lattice model demonstrates wide ranging behavior dictated by the chosen symmetries. We derive a full hydrodynamic description of our lattice model that highlights the mechanisms driving its behavior. Each combination of symmetries can lead to a different hydrodynamic model with one or two simultaneous order parameters with different levels of orientational order that can appear active or not.

Mechanical stress is one of the main characteristics of viscoelastic molecular systems, including vitrifying liquids and amorphous solids which are known to show long-range dynamical heterogeneities. It is therefore important to deal with time- and space-resolved local stress whose correlations can be used to obtain viscoelastic memory functions of such systems, in particular, their wave-vector dependent shear and compression relaxation moduli. However, such memory functions are not directly accessible within standard molecular dynamics (MD) simulations. Here we discuss a new method called the constrained molecular dynamics (CMD) which allows for a direct evaluation of the wave-vector and time dependent viscoelastic moduli. The study is focused, in particular, on the stability analysis of different variants of the CMD, revealing the most efficient stabilization algorithm. Our theoretical predictions for the time-dependence of the most important parameters (like the wave-vector resolved mass current) during the stabilization stage are compared with the simulation data using different CMD algorithms. An excellent agreement is observed, thus confirming the validity of our theoretical approach and its ability to accurately predict the most important initial part of CMD trajectories.

The structural accessibility and perimeter shell properties of branched macromolecules are studied using Monte Carlo simulations on a simple cubic lattice. Constituent polymer chains of these macromolecules are constructed in an algorithmic way as: a set of collinear monomers; a self-avoiding walk (SAW); tigthly packed monomers. These mimic the ground states for a rigid rod, flexible chain, and collapsed chain conformational states, respectively. We analyzed the set of observables that are related to adsorption, such as perimeter shell size (number of available sites for particle adsorption) per monomer $\langle \bar{n}_a \rangle$, mean coordination number of perimeter shell $\langle n_{\rm bond} \rangle$ (average number of contacts per adsorbed particle), and coordination accessibility per monomer $\langle \bar{n}_s \rangle$. Our results demonstrate that branching reduces the perimeter shell size while it increases mean coordination number of perimeter shell across all conformational states, due to higher local monomer density near branching cores and formation of shared adsorption sites or microcages. Stiff extended structures yield the highest values for $\langle \bar{n}_a \rangle$ (approaching $4$ in the limit of long chains), whereas the flexible ones provide the largest values for $\langle n_{\rm bond} \rangle$. We can note that the collinearity of monomers in constituent chains works in the same direction as reduction of branching ($\langle \bar{n}_a \rangle$ grows), whereas the { tight packing of them} -- in the same direction as the increase of branching ($\langle \bar{n}_a \rangle$ decreases)

We carried out molecular dynamics simulations of a mixture of self-propelled particles (SPPs) and passive particles. When the self-propulsion speed was sufficiently high, the system separated into two phases. One phase consists of ordered passive particles, and the other is a disordered mixture of both particle types. The separation arises from the interplay between the self-propelled motion of SPPs and thermal motion of the passive particles, since phase separation does not appear with either particle type alone. As the self-propulsion speed increases, the SPPs push surrounding particles away, effectively increasing their excluded size. As a result, the increase in the effective excluded sizes promotes the accumulation of passive particles and the segregation of SPPs into the dilute phase through a mechanism analogous to the depletion interaction. This mechanism explains the phase separation observed in the present study. Our findings provide insights into the potential applications of SPPs and contribute to our understanding of natural systems composed of elements with different levels of motility.

The dielectric constant of polar liquids depends not only on the magnitude of the molecular dipole moment but also on short range orientational correlations between neighboring dipoles. How molecular structure determines these correlations, quantified by the Kirkwood correlation factor $g_\mathrm{K}$, remains unclear. Using molecular dynamics simulations of model polar liquids without hydrogen bonding, we show that molecular shape plays a central role. We interpret $g_\mathrm{K}$ as resulting from the combination of dipolar interactions and steric effects, which together favor either alignment or antialignment of neighboring molecules. Depending on molecular shape, $g_\mathrm{K}$ can either increases or decreases upon cooling, or change monotony. A minimal model of dipoles on a cubic lattice reproduces these three regimes and attributes the nonmonotonic case to a competition between the first layer of neighbors, which favors alignment, and the second layer, which favors antialignment.

Using an activation model reproducing facilitation mechanisms, we found recently a fluidization transition in supercooled water triggered by the motion of a few active molecules. We investigate here the effect of the activation force on the transition. The activation force increases the probability for active molecules to overcome the energy barrier induced by caging. The induced molecular motions result in an intermittent loose structure leading to a decrease of energy barriers around the moving molecule, thus facilitating the motion of nearby molecules. Therefore we expect the force, if small enough, to increase the facilitation mechanism, and as a result cooperative motions induced by it. As a result the activation force permits to tune facilitation and cooperative motions in active glass-formers. We find an optimum force for cooperative motions that we evaluate to be around 3.5pN, a value in the range of protein motor forces. As expected for a phase transition we observe peaks of cooperative motions (dynamic heterogeneity) on the transition. This result shows that dynamical heterogeneity are the cooperative motions of physical importance for the transition.

Much of the water that matters most - flowing through biological cells and membranes, percolating through soils and rocks, or processed by filtration and desalination technologies - is not bulk water. It is water in nano- and molecular-scale confinement or at interfaces. Nanoscale water is therefore central to many challenges of our time, from the global water crisis and the water-energy nexus to catalysis, electrochemistry, and health. Beyond its applications, nanoscale water has emerged as a rich scientific field in its own right: discoveries are accumulating rapidly, revealing that confinement and interfacial forces produce a range of behaviours absent in the bulk. Sometimes this amounts to a renormalization of bulk properties; in other cases, new and non-intuitive phenomena emerge - exotic phases, anomalous transport, and dielectric responses that defy continuum expectations. A convergence of atomically precise nanofluidic devices, novel spectroscopies and microscopy approaches, and machine-learning-accelerated simulations is now exposing the molecular mechanisms behind these behaviours. This Perspective argues that the recent findings should not be viewed as isolated nanoscale anomalies, but as evidence for a molecular regime of water in which continuum descriptions must be rethought and molecular-level understanding becomes essential. Recognising and exploring this regime should have consequences ranging from fundamental physics to the rational design of future technologies that rely on water and dissolved ions.

We present a native implementation of the Two/Three-Phase Thermodynamics (xPT, $x \in \{2,3\}$) method within the LAMMPS molecular dynamics framework, termed FixXPT. xPT is an approach for obtaining absolute, quantum-corrected thermodynamics (entropy, enthalpy, free energy, heat capacity, diffusivity) from the density of states function, obtained from the velocity autocorrelation function of an equilibrium trajectory. FixXPT addresses non-trivial integration challenges, including atom reordering, compute scheduling, FFT backends, unit systems, and partitioning of atomic velocities into molecular modes. Two autocorrelators are provided: a Wiener-Khinchin FFT correlator with one cumulative window-end operation, and an in-line multi-tau correlator whose memory footprint grows only logarithmically with the window length, removing the principal scaling bottleneck for long analysis windows. The velocity history of each window is distributed across the MPI ranks, so the memory the fix adds to a node stays nearly constant as ranks are added and its cost falls with the number of GPUs. Replaying identical velocity frames through FixXPT and the stand-alone pyxPT post-processor reproduces every thermodynamic observable of well-sampled groups to within 0.1 to 0.5% across the full analysis-mode matrix; sparsely populated ionic species agree within their statistical uncertainty, and the multi-tau VACF converges monotonically to the FFT result as its buffer grows, from 0.8% to 0.1% drift across a 16-fold buffer sweep. The code is distributed as a drop-in LAMMPS package.

Dynamical coarse-graining of neural networks gives macroscopic observables and enables training to be predicted, and controlled in a state space far smaller than the parameter space. In this paper, we first define the learning operator following the training law of neural networks and establish a descent theorem for the learning operator, which serves to determine whether the operator is sufficient to characterize the learning dynamics. Then the exact closure theorem and the dynamic coarse-graining theorem are presented in sequence. The three theorems established in this paper provide a systematic framework for constructing and assessing dynamical coarse-grained models of neural-network training. Finally, we demonstrate the applicability of the theorems with a finite-width nonlinear ReLU network. Its dynamic coarse-grained model for a fixed pattern is exactly derived and composed of five independent moments. The model exactly predict the training outputs and evolving learning operator. The framework may also guide the development of approximate coarse-grained models for more general neural networks when exact closure is unavailable.

Swarmalators are particles which have internal degrees of freedom in the form of phase oscillators that can synchronize and change the dynamics of the particles. We explore the active matter disk swarmalator model proposed by Adorjáni [Phys. Rev. E 109, 024607 (2024)] where there is an additional attraction or repulsion determined by whether the phases of particles within a certain radius synchronize or anti-synchronize. This model in the absence of swarmalator rules forms a motility-induced phase-separated (MIPS) state at high activity, but when the swarmalator rules are present, a number of additional states appear including an active gel, labyrinth state, frustrated MIPS, and frustrated fluid states. Here we examine ratchet effects for the Adorjáni model coupled to asymmetric substrates. We find that swarmalator interactions can strongly enhance or suppress active matter ratcheting depending on the structural state of the system, and show that the ratchet efficiency depends on the substrate strength, substrate lattice spacing, and the motor force on the particles. The gel state exhibits the weakest ratcheting for small substrate spacing and can even produce a reversed ratchet motion, but shows the strongest ratcheting for large motor forces. The labyrinth state exhibits the strongest ratcheting over a wide range of parameters since the quasi-one-dimensional labyrinth morphology can match the substrate geometry well.

Understanding the fluctuations and response of particles in potential landscapes is a fundamental problem in nonequilibrium statistical physics. Here, we investigate how negative mobility reshapes the stochastic behavior of a particle moving in a mechanical potential landscape. We consider a one-dimensional overdamped particle subject to additive noise, with mobility that is negative at weak forces and positive at strong forces. In a potential well, negative mobility destabilizes the mechanical potential minimum and produces bimodal localization at two off-center positions. The same stability reversal transforms conventional Kramers escape from a potential well into a two-stage escape process involving an intermediate metastable state. When an additional spatially uniform force is applied, the mechanical perturbation translates rather than tilts the effective double well, yielding a positive response dominated at weak noise by intrawell relaxation, even though slow interwell switching dominates spontaneous positional fluctuations. These findings reveal how negative mobility reshapes the interplay among mechanical landscapes, stochastic stability, and response.

Interactions shape the properties of many physical and biological nonequilibrium systems, but whether collective operation raises or lowers entropy production remains difficult to predict. Here, we show that strong coupling suppresses irreversible circulation associated with the directly coupled degrees of freedom. In identical two-dimensional systems, this suppression is monotonic for nonzero coupling modes and guarantees a monotonic decrease of the entropy production rate (EPR), with the corresponding probability currents vanishing in the strong-coupling limit. In higher dimensions, suppressing the circulation associated with the coupled degrees of freedom does not generally eliminate all irreversible circulation, since the remaining degrees of freedom can still sustain nonequilibrium currents. The EPR of these residual dynamics sets the strong-coupling limit, providing a criterion for whether a given choice of coupled variables leads to lower dissipation than the uncoupled system. The approach to this limit depends on how the finite-coupling current correction aligns with the residual current. Further, we show that convergence to residual dynamics in the strong-coupling limit also applies to coupled non-identical systems. Finally, we demonstrate the predicted EPR reduction in nonlinear models of vertebrate hair-cell bundles and bacterial chemotaxis.

Odd slip at solid-liquid interfaces, characterized by a velocity component perpendicular to the applied shear stress, introduces a novel class of odd transport phenomena with transverse momentum transfer at boundaries, distinct from odd viscosity, odd elasticity, or odd diffusivity. This effect arises on chiral active surfaces with persistent energy input and defined handedness, such as carpets of rotating biological or synthetic cilia with flexible anchoring. Although odd slip itself is dissipationless, its maintenance in active systems comes at an energetic cost, which we estimate from the microscopic model. With respect to its orientation dependence, odd slip can be classified as pseudo-scalar or pseudo-vectorial. We use a perturbative expansion to determine the resistance tensors of problems with different levels of broken symmetries. They reveal how the surface chirality and body asymmetry together violate the Onsager symmetry, but uphold the Onsager-Casimir reciprocity. Strikingly, suspensions of particles with odd slip embedded in a conventional Newtonian bulk fluid exhibit odd viscosity, providing a new minimal mechanism for its emergence beyond current microscopic models.

Compression can enhance or suppress cell motion and induce coherent rotation in tissue spheroids, but the physical factors governing these responses remain unclear. Using a minimal three-dimensional active vertex model, we examine how tissue mechanics, active driving, and directional alignment shape the response to uniaxial compression. Without alignment, compression enhances transverse motion in solid-like spheroids throughout the tested activity range, whereas fluid-like spheroids switch from acceleration at low activity to slowing at high activity. Changes in contact persistence connect these responses to neighbor exchange. Passive five-cell calculations show that compression reshapes local rearrangement barriers depending on geometry and loading direction; a high-strain increase in the fluid-like mean coexists with many low-barrier paths. With strong alignment, compression induces coherent rotation in solid-like spheroids, while fluid-like spheroids show no comparable rotation. Rotating solids retain more persistent cell contacts than fluids and develop tangential organization of peripheral cells. Controlled perturbations reveal that compression softens a differential-twist mode and activity slows its relaxation. Together, these results link activity-dependent reversal of the speed response and compression-induced coherent rotation to contact dynamics, local rearrangement costs, and active driving. Our findings demonstrate that one minimal model, with parameters representing effective mechanical and active tissue properties, captures diverse collective responses to compression and provides a framework for predicting how different tissues respond to mechanical loading.

We study nonlinear fluctuating hydrodynamic theories with charge and energy conservation in and above two dimensions that describe the large-scale behavior of the Hamiltonian XY model in the disordered and ordered phases. Using renormalization group analysis at one-loop order, we show that while Fourier's law holds in the ordered phase above two dimensions and in the disordered phase in any dimension, the energy diffusivity in the ordered phase exactly in two dimensions, the Berezinskii-Kosterlitz-Thouless phase, exhibits a logarithmic divergence in the thermodynamic limit. This divergence arises from elastic energy transport induced by spin-wave excitations.

We present a lattice gas model with random interactions quenched in space and spins quenched to the particles. The vacancy-mediated diffusion is studied in equilibrium. A two-stage slowdown is witnessed in both the mean squared displacement and the spatial overlaps. We demonstrate that the plateaus are due to anti-correlation at the corresponding length scale. The anti-correlation diminishes quickly with increasing lag time. A broad and yet finite distribution of the survival function of vacancies suggests their normal diffusion at large time limit. Our model can serve as a new playground for understanding hierarchical dynamics in glassy-like dynamical systems.

The Discrete Element Method (DEM) is an indispensable tool for modeling and understanding the basic mechanical behaviors of (dry) granular media, a class of disordered material systems of significant interest to multiple scientific domains from (geo-)physics to (chemical) engineering. At the core of all present DEM frameworks is a phenomenological friction law--commonly chosen to be of the Amontons-Coulomb type--that models the frictional interaction among constituent grains. Here, inspired by research in the tribology community, we present an alternative, fundamentally different framework, termed roughness-resolved DEM (Rough-DEM), in which contact friction is handled in a more physics-based manner. In this framework, instead of using a friction law, we explicitly resolve the surface topography of each grain and let friction emerge from collective frictionless interactions of surface asperities. In essence, we trade phenomenology for physics at the cost of an increased computational demand. To mitigate the cost, we introduce an FE$^2$-like algorithm and a feature-wise linear modulation (FiLM)-based neural network surrogate, both of which are parallelized on GPU to accelerate contact computation. Crucially, using this framework in 2D, we (1) demonstrate the possibility of recovering a two-body-based Amontons-Coulomb-type friction behavior using a \textit{simple} representation of surface topography, and (2) identify many-body-based frictional behaviors--more specifically, the angle of repose values--that \textit{cannot always} be captured by current DEM frameworks under the same level of model complexity. We conclude by discussing future directions for Rough-DEM, as well as exciting new opportunities it could unlock for a wide range of emerging applications, including (designing) suspension rheology and metamaterial mechanics.