arXiv subjects

Liesbeth M. C. Janssen

Publications and source records attributed to Liesbeth M. C. Janssen.

At least 19 recordsLinked to original sources

arXiv2026

Theory of the $β$-Relaxation Beyond Mode-Coupling Theory: A Microscopic Treatment

We develop a systematic extension of mode-coupling theory (MCT) that incorporates critical dynamical fluctuations. Starting from a microscopic diagrammatic theory, we identify dominant classes of divergent diagrams near the mode-coupling transition and show that the corresponding asymptotic series dominates the mean-field below an upper critical dimension $d_c=8$. To resum these divergences, we construct a mapping to a stochastic dynamical process in which the order parameter evolves under random spatiotemporal fields. This reformulation provides a controlled, fully dynamical derivation of an effective theory for the $β$-relaxation which remarkably coincides with stochastic beta-relaxation theory [T. Rizzo, EPL 106, 56003 (2014)]. All coupling constants of the latter theory are expressed microscopically in terms of the liquid static structure factor and are computed for the paradigmatic hard-sphere system. The analysis demonstrates that fluctuations alone restore ergodicity and replace the putative mean-field transition by a smooth crossover. Our results establish a predictive framework for structural relaxation beyond mean-field.

cond-mat.stat-mech↗

arXiv2026

Microscopic Theory of a Fluctuation-Induced Dynamical Crossover in Supercooled Liquids

Mean-field theories of the glass transition predict a phase transition to a dynamically arrested state, yet no such transition is observed in experiments or simulations of finite-dimensional systems. We resolve this long-standing discrepancy by incorporating critical dynamical fluctuations into a microscopic mode-coupling framework. We show that these fluctuations round off the mean-field singularity and restore ergodicity at all finite densities (or temperatures) without invoking activated dynamics or facilitation. The resulting effective theory describes the order parameter as a stochastic process with self-induced, annealed disorder, determined self-consistently at the mean-field level. In the $β$-relaxation regime it reduces to stochastic beta-relaxation theory, thereby unifying mode-coupling and replica-based approaches beyond mean-field. All parameters of the stochastic $β$-relaxation theory are fixed by the static structure, enabling parameter-free predictions that extend mean-field theory into finite dimensions.

cond-mat.soft↗

arXiv2026

Non-Monotonic Dynamical Correlations Across The Glass Crossover

The dramatic slowing down of structural relaxation in supercooled liquids is accompanied by the emergence of dynamic heterogeneity. A monotonically increasing dynamical correlation length, measured at the $α$-timescale, is one of the remarkable features of this phenomenon. Here we show that this picture is incomplete: the dynamical correlation length measured in the $β$-relaxation regime exhibits a striking non-monotonic temperature dependence, reaching a maximum near the mode-coupling crossover temperature $T_c$ and decreasing upon further cooling, even as local dynamical fluctuations continue to intensify. This behavior suggests a crossover from spatially extended, maximally cooperative motion near $T_c$ to increasingly compact and localized relaxation events below it. We demonstrate that this evolution is quantitatively captured by stochastic beta-relaxation theory, an extension of mode-coupling theory beyond mean-field that explicitly predicts an avoided dynamical transition in finite dimensions. Our results provide the first direct spatial evidence in favor of the avoided-transition picture of the mode-coupling crossover, and establish the peak of the $β$-regime correlation length as a robust indicator of the mode-coupling crossover.

cond-mat.soft↗

arXiv2026

Nuclear mechanics controls the temporal dynamics of cell unjamming

Cell unjamming in dense tissues is a complex but essential process in embryogenesis and cancer metastasis. Increasing evidence suggests that nuclear mechanics and density effects play a vital role in collective cell unjamming. However, state-of-the-art cell-shape-based theories fail to include nuclear and density effects, while computer models featuring rigid nuclei disagree with experimental observations of elongated nuclei promoting unjamming. Here, we introduce a computational model of confluent cells with explicitly deformable nuclei to study the dynamics of cell unjamming. Our simulations show an unjamming transition controlled by nuclear size and shape, reconciling conflicting theories of density-driven versus shape-driven mechanisms. We predict general relations connecting cellular and nuclear shape to collective cell motion, verified experimentally in distinct monolayers of MCF-10A and MDA-MB-436 breast cells, with striking accuracy. Our work establishes a rational connection between nuclear mechanics and tissue-scale rigidity transitions, highlighting the nucleus's key role in collective cell unjamming.

physics.bio-ph↗

arXiv2026

Persistence-Driven Void Formation in Dense Active-Passive Mixtures

It is well established that dilute active dopants can melt an arrested amorphous solid by enhancing cage breaking and accelerating structural relaxation. Yet it remains unclear whether increasing persistence simply amplifies this effective melting or instead reorganizes the fluidization mechanism itself. Here we show that, in a minimal active-passive mixture, increasing persistence drives a crossover from homogeneous fluidization to a localized mechanical instability, demonstrating that sustained active forcing restructures relaxation in space rather than merely strengthening it. Persistent dopants accumulate stress and nucleate voids as their mechanically perturbed regions overlap. In this regime, rearrangements localize at void boundaries, and active and passive particles exhibit comparable mobility, producing dynamics reminiscent of crowd mosh pits. Persistence therefore reorganizes fluidization through stress accumulation and confinement, revealing a distinct nonequilibrium localization mechanism in disordered solids.

cond-mat.soft↗

arXiv2025

Fundamental measure theory for predicting many-body correlation functions

We study many-body correlation functions within various Fundamental Measure Theory (FMT) formulations and compare their predictions to Monte Carlo simulations of hard-sphere fluids. FMT accurately captures the qualitative behavior of three- and four-body structure, particularly at low and intermediate wavevectors. At higher wavevectors, the predictions of FMT vary in quantitative accuracy. We show that the dominant contributions to the four-point structure factor arise from direct triplet correlations, allowing the evaluation of four-point correlations to be greatly simplified. In glass-forming liquids at high volume fractions, FMT correctly reproduces deviations from the convolution approximation, highlighting FMT's ability to capture growing structural multipoint correlations upon supercooling.

physics.chem-ph↗

arXiv2025

Glassy dynamics in deep neural networks: A structural comparison

Deep Neural Networks (DNNs) share important similarities with structural glasses. Both have many degrees of freedom, and their dynamics are governed by a high-dimensional, non-convex landscape representing either the loss or energy, respectively. Furthermore, both experience gradient descent dynamics subject to noise. In this work we investigate, by performing quantitative measurements on realistic networks trained on the MNIST and CIFAR-10 datasets, the extent to which this qualitative similarity gives rise to glass-like dynamics in neural networks. We demonstrate the existence of a Topology Trivialisation Transition as well as the previously studied under-to-overparameterised transition analogous to jamming. By training DNNs with overdamped Langevin dynamics in the resulting disordered phases, we do not observe diverging relaxation times at non-zero temperature, nor do we observe any caging effects, in contrast to glass phenomenology. However, the weight overlap function follows a power law in time, with exponent $\approx -0.5$, in agreement with the Mode-Coupling Theory of structural glasses. In addition, the DNN dynamics obey a form of time-temperature superposition. Finally, dynamic heterogeneity and ageing are observed at low temperatures. These results highlight important and surprising points of both difference and agreement between the behaviour of DNNs and structural glasses.

physics.comp-ph↗

arXiv2024

Formation of motile cell clusters in heterogeneous model tumors: the role of cell-cell alignment

Circulating tumor cell clusters play an important role in the metastatic cascade. These clusters can acquire a migratory and more invasive phenotype, and coordinate their motion to migrate as a collective. Before such clusters can form by collectively detaching from a primary tumor, however, the cluster must first aggregate in the tumor interior. The mechanism of this cluster formation process is still poorly understood. One of the possible ways for cells to cluster is by aligning their direction of motion with their neighboring cells. This work aims to investigate the role of this cell-cell alignment interaction on the formation of motile cell clusters inside the bulk of a tumor using computer simulations. We employ a Cellular Potts model in which we model a two-dimensional heterogeneous confluent layer containing both motile and non-motile cells. Our results indicate that the degree of clustering is governed by two distinct processes: the formation of clusters due to the presence of cell-cell alignment interactions among motile cells, and the suppression of clustering due to the presence of the dynamic cellular environment (comprised of the non-motile cells). We find that the largest motile clusters are formed for intermediate alignment strengths, contrary to what is observed for motile cells in free space (that is, unimpeded by a dense cellular environment), in which case stronger cell-cell alignment always leads to larger clustering. Our findings suggest that the presence of a densely-packed cellular environment and strong cell-cell alignment inhibits the formation of large migratory clusters within the primary tumor, providing physical insight into potential factors at play during the early stages of metastasis.

physics.bio-ph↗

arXiv2024

Shape matters: Inferring the motility of confluent cells from static images

Cell motility in dense cell collectives is pivotal in various diseases like cancer metastasis and asthma. A central aspect in these phenomena is the heterogeneity in cell motility, but identifying the motility of individual cells is challenging. Previous work has established the importance of the average cell shape in predicting cell dynamics. Here, we aim to identify the importance of individual cell shape features, rather than collective features, to distinguish between high-motility (active) and low-motility (passive) cells in heterogeneous cell layers. Employing the Cellular Potts Model, we generate simulation snapshots and extract static features as inputs for a simple machine-learning model. Our results show that when the passive cells are non-motile, this machine-learning model can accurately predict whether a cell is passive or active using only single-cell shape features. Furthermore, we explore scenarios where passive cells also exhibit some degree of motility, albeit less than active cells. In such cases, our findings indicate that a neural network trained on shape features can accurately classify cell motility, particularly when the number of active cells is low, and the motility of active cells is significantly higher compared to passive cells. This work offers potential for physics-inspired predictions of single-cell properties with implications for inferring cell dynamics from static histological images.

physics.bio-ph↗

arXiv2024

Dissecting mode-coupling theory for supercooled liquids

The mode-coupling theory (MCT) of the glass transition ranks among the most successful first-principles kinetic theories to describe glassy dynamics. However, MCT does not fully account for crucial aspects of the dynamics near the glass transition. To facilitate improving the theory, we critically test the approximations inherent in MCT for a supercooled mixture using Brownian dynamics simulations. Although each MCT approximation significantly impacts the predicted dynamics, our findings show that long-time cancellations of errors occur due to static and dynamic approximations of four-point correlation functions, with the validity of these approximations remaining relatively constant across different temperatures. Notably, the MCT form of the memory functional maintains remarkably high accuracy even in the supercooled regime when evaluated with the intermediate scattering function from simulations. The primary discrepancies between theoretical predictions and experimental results arise from the self-consistent nature of the MCT equations, which amplify minor errors in the memory kernel. This suggests that minor corrections to the memory functional could substantially enhance the theory's predictive accuracy.

cond-mat.soft↗

arXiv2024

Simple fluctuations in simple glass formers

Critical single-particle fluctuations associated with particle displacements are inherent to simple glass-forming liquids in the limit of large dimensions and leave a pseudo-critical trace across all finite dimensions. This characteristic could serve as a crucial test for distinguishing between theories of glass formation. We here examine these critical fluctuations, as captured by the well-established non-Gaussian parameter, within both mode-coupling theory (MCT) and dynamical mean-field theory (DMFT) across dimensions for hard sphere liquids and for the minimally structured Mari--Kurchan model. We establish general scaling laws relevant to any liquid dynamics theory in large dimensions and show that the dimensional scalings predicted by MCT are inconsistent with those from DMFT. Simulation results for hard spheres in moderately high dimensions align with the DMFT scenario, reinforcing the relevance of mean-field theory for capturing glass physics in finite dimensions. We identify potential adjustments to MCT to account for certain mean-field physics. Our findings also highlight that local structure and spatial dimensionality can affect single-particle critical fluctuations in non-trivial ways.

cond-mat.dis-nn↗

arXiv2024

Comparison of integral equation theories of the liquid state

The Ornstein-Zernike equation is a powerful tool in liquid state theory for predicting structural and thermodynamic properties of fluids. Combined with a suitable closure, it has been shown to reproduce e.g. the static structure factor, pressure, and compressibility of liquids to a great degree of accuracy. However, out of the multitude of closures that exist for the Ornstein-Zernike equation, it is hard to predict a priori which closure will give the most accurate predictions for the system at hand. To alleviate this problem, we compare the predictive power of many closures on a curated set of representative benchmark systems, including those with hard-sphere, inverse power-law, Gaussian core, and Lennard-Jones particles, in three and two dimensions. For example, we find that the well-known and highly used Percus-Yevick closure gives significantly worse predictions than lesser-known closures of equal complexity in all cases studied. We anticipate that the trends observed in our results will aid in making more informed decisions regarding closure choices. To facilitate the adoption of more modern closure theories, we also have packaged, documented, and distributed the code necessary to numerically solve the equations for a given closure and pair interaction potential.

cond-mat.soft↗

arXiv2024

Classifying the age of a glass based on structural properties: A machine learning approach

It is well established that physical aging of amorphous solids is governed by a marked change in dynamical properties as the material becomes older. Conversely, structural properties such as the radial distribution function exhibit only a very weak age dependence, usually deemed negligible with respect to the numerical noise. Here we demonstrate that the extremely weak age-dependent changes in structure are in fact sufficient to reliably assess the age of a glass with the support of machine learning. We employ a supervised learning method to predict the age of a glass based on the system's instantaneous radial distribution function. Specifically, we train a multilayer perceptron for a model glassformer quenched to different temperatures, and find that this neural network can accurately classify the age of our system across at least four orders of magnitude in time. Our analysis also reveals which structural features encode the most useful information. Overall, this work shows that through the aid of machine learning, a simple structure-dynamics link can indeed be established for physically aged glasses.

cond-mat.soft↗

arXiv2023

Microscopic theory for nonequilibrium correlation functions in dense active fluids

One of the key hallmarks of dense active matter in the liquid, supercooled, and solid phases is so-called equal-time velocity correlations. Crucially, these correlations can emerge spontaneously, i.e., they require no explicit alignment interactions, and therefore represent a generic feature of dense active matter. This indicates that for a meaningful comparison or possible mapping between active and passive liquids one not only needs to understand their structural properties, but also the impact of these velocity correlations. This has already prompted several simulation and theoretical studies, though they are mostly focused on athermal systems and thus overlook the effect of translational diffusion. Here we present a fully microscopic method to calculate nonequilibrium correlations in systems of thermal active Brownian particles (ABPs). We use the integration through transients (ITT) formalism together with (active) mode-coupling theory (MCT) and analytically calculate qualitatively consistent static structure factors and active velocity correlations. We complement our theoretical results with simulations of both thermal and athermal ABPs which exemplify the disruptive role that thermal noise has on velocity correlations.

cond-mat.soft↗

arXiv2023

Competing Relaxation Channels in Continuously Polydisperse Fluids: A Mode-Coupling Study

Systems with a high degree of size polydispersity are becoming standard in the computational study of deeply supercooled liquids. In this work we perform a systematic analysis of continuously polydisperse fluids as a function of the degree of polydispersity within the framework of the Mode-Coupling Theory of the glass transition (MCT). Our results show that a high degree of polydispersity tends to stabilize the liquid phase against vitrification, the magnitude of which depends on the shape of the polydispersity distribution. Further, we report on a separation between the localization lengths of the smallest and largest particles. A diameter-resolved analysis of the intermediate scattering functions reveals that this separation significantly stretches the relaxation patterns, which we quantitatively study by an analysis of the dynamical exponents predicted by the theory. Our observations have strong implications for our understanding of the nature of dynamical heterogeneities and localization lengths in continuously polydisperse systems. These results suggest that the dynamics of the smallest particles is of central importance to understand structural relaxation of continuously size polydisperse fluids, already in the mildly supercooled regime where MCT is usually applicable.

cond-mat.soft↗

arXiv2023

Influence of polydispersity on the relaxation mechanisms of glassy liquids

State-of-the-art techniques for simulating deeply supercooled liquids require a high degree of size polydispersity to be effective. While these techniques have enabled great insight into the microscopic dynamics near the glass transition, the effect of the large polydispersity on the dynamics has remained largely unstudied. Here we show that a particle's size not only has a strong correlation with its mobility, but we also observe that, as the mode-coupling temperature is crossed and the system becomes more deeply supercooled, a dynamic separation between small mobile and larger quiescent particles emerges at timescales corresponding to cage escape. Our results suggest that the cage escape of this population of mobile particles facilitates the later structural relaxation of the quiescent particles. In the deep supercooled regime, we show that particles of different sizes display varying degrees of breakdown of the Stokes-Einstein relation and have different activation energy barriers. Overall, this indicates that it is important to account for particle-size effects when generalizing results to other glass-forming systems.

cond-mat.soft↗

arXiv2023

Rejuvenation and memory effects in active glasses induced by thermal and active cycling

It has recently been shown that thermal active glasses can display physical aging behavior comparable to that of passive glasses, although there are some notable distinctions due to the intrinsic non-equilibrium nature of active matter. The question whether active disordered materials can also exhibit rejuvenation and memory effects, akin to the phenomenology of e.g.\ spin glasses, has thus far remained unexplored. Here we address this question by numerical simulations of active glasses composed of active Brownian particles that are subjected to a thermal or active cycling protocol. We find that an active system undergoing thermal cycling indeed shows rejuvenation and memory effects, with the strength of rejuvenation depending on the persistence time. In contrast, however, a passive Brownian system subjected to the same thermal cycle lacks the rejuvenation effect. We attribute this to the enhanced motility of active particles, which enables them to escape from their cages and restart aging at the new temperature, thus rejuvenating the material. Finally, we also demonstrate that both rejuvenation and memory effects can be induced by an activity cycle which quenches the material from an active to passive glass and back, providing a unique means to rejuvenate active matter.

cond-mat.soft↗

arXiv2023

A deep learning approach to the measurement of long-lived memory kernels from Generalised Langevin Dynamics

Memory effects are ubiquitous in a wide variety of complex physical phenomena, ranging from glassy dynamics and metamaterials to climate models. The Generalised Langevin Equation (GLE) provides a rigorous way to describe memory effects via the so-called memory kernel in an integro-differential equation. However, the memory kernel is often unknown, and accurately predicting or measuring it via e.g. a numerical inverse Laplace transform remains a herculean task. Here we describe a novel method using deep neural networks (DNNs) to measure memory kernels from dynamical data. As proof-of-principle, we focus on the notoriously long-lived memory effects of glassy systems, which have proved a major challenge to existing methods. Specifically, we learn a training set generated with the Mode-Coupling Theory (MCT) of hard spheres. Our DNNs are remarkably robust against noise, in contrast to conventional techniques which require ensemble averaging over many independent trajectories. Finally, we demonstrate that a network trained on data generated from analytic theory (hard-sphere MCT) generalises well to data from simulations of a different system (Brownian Weeks-Chandler-Andersen particles). We provide a general pipeline, KernelLearner, for training networks to extract memory kernels from any non-Markovian system described by a GLE. The success of our DNN method applied to glassy systems suggests deep learning can play an important role in the study of dynamical systems that exhibit memory effects.

cond-mat.dis-nn↗