arXiv subjects

Pierre Illien

Publications and source records attributed to Pierre Illien.

At least 19 recordsLinked to original sources

arXiv2026

Bath-modes quantitatively capture the nonlinear microrheology of micellar solutions

Active microrheology experiments, in which a probe is driven through a complex fluid, often exhibit nonlinear responses that cannot be captured by generalized Langevin equations. Models that couple the probe to a Gaussian field reproduce such nonlinear effects qualitatively, but their large number of parameters hinders direct comparison with experiments. Here, we restrict these models to a small number of field modes and demonstrate that this reduced description quantitatively reproduces a broad range of active microrheology experiments in a micellar solution using a single set of parameters. We further show that the same framework extends naturally to multi-probe systems, such as colloidal dumbbells.

cond-mat.soft↗

arXiv2026

A Brownian dynamics study of liquid-liquid phase separation in multi-scale chromatin networks

In living cells, proteins involved in specialized biochemical functions are often spatially organized within biomolecular condensates. Increasing evidence suggests that some of these condensates, including DNA repair condensates, emerge through liquid-liquid phase separation (LLPS). In the nucleus, however, condensates form within a highly heterogeneous environment composed of chromatin fibers, RNA, and additional protein scaffolds such as PAR chains, all of which may interact with phase-separating proteins. Moreover, condensate formation is frequently associated with specific chromatin conformations; for instance, loop extrusion has been proposed as a mechanism promoting DNA repair condensates. Here, we investigate how the surrounding fibrous environment controls the morphology and spatial organization of phase-separated condensates. Using Brownian dynamics simulations of minimal models combining Lennard-Jones particles with fixed fibrous substrates, we examine the respective roles of local fiber geometry and large-scale network organization, reflecting the multiscale architecture of chromatin. We show that protein-fiber interactions strongly influence droplet positioning relative to the substrate, in a manner analogous to wetting transitions in soft condensed matter systems. Both local geometric constraints and global network organization markedly affect droplet size, morphology, and multiplicity. In addition, large-scale asymmetries in fiber organization can induce robust spatial localization of the dense phase. Our results thus highlight how multiscale structural heterogeneity of the nuclear environment can regulate the emergence and organization of biomolecular condensates.

cond-mat.soft↗

arXiv2026

Solvent-induced memory effects in a model electrolyte

The fluctuations of ions in polar solvents remain poorly understood theoretically due to the complex coupling between ionic motion and solvent polarization. Indeed, while all-atom resolution can be achieved in numerical simulations, analytical approaches require suitable levels of coarse-graining. In this work, we describe ions and solvent molecules as interacting Brownian particles and use stochastic density functional theory to derive a generalized Langevin equation for the ionic charge density, explicitly accounting for solvent-mediated memory effects. In the regime where there is a clear timescale separation between fast solvent and slow ion dynamics, we obtain simple expressions for dynamical charge structure factors, which are validated by BD simulations. For slow solvents, we predict an emerging two-step relaxation in ionic dynamics. These results provide a mesoscopic approach for ion-solvent dynamics and open pathways to study fluctuation-induced phenomena in electrolytes.

cond-mat.soft↗

arXiv2026

Controlling microalgae populations by phototactic memory

Understanding how microorganisms navigate in complex environments is a central question in active matter and biological physics. Phototaxis - the ability to use light as a navigation cue - is a widespread strategy in motile microalgae to optimise photosynthesis and avoid light-induced stress. The microalga Chlamydomonas reinhardtii is a model system for studying this behaviour, where navigation is classically attributed to a photosensitive organelle named eyespot. While this mechanism enables cells to sense the direction of incoming light, their response to light intensity gradients remains less understood. Here we show that structured light landscapes can guide microalgae populations and localise them in defined spatial regions. By analysing single-cell trajectories, we find that cells actively steer relative to the local light gradient, and a comparison with a minimal theoretical model shows that a short-time memory of light exposure acting on the transition between positive and negative phototaxis is necessary to reproduce the observed accumulation. At longer times, we observe a gradual decrease in cell number density within the trapping region, consistent with phototactic adaptation. Beyond controlling population dynamics, our results reveal new aspects of phototactic behaviour, highlighting gradient-aligned steering together with temporal integration as central mechanisms for navigation in structured environments.

cond-mat.soft↗

arXiv2025

Closures of moment expansion of anisotropic active Brownian particles

We study analytically the dynamics of anisotropic active Brownian particles (ABPs), and more precisely their intermediate scattering function (ISF). To this end, we develop a systematic closure scheme for the moment expansion of their Fokker-Planck equation. Starting from the coupled evolution of translational and orientational degrees of freedom, we derive equations for the density, polarization, and nematic tensor fields, which naturally generate an infinite hierarchy of higher-order moments. To obtain explicit solutions, we investigate truncation strategies and analyze closures at different orders. While the closure at lowest order yields Gaussian dynamics with an effective translational diffusion, closures at higher orders incorporate orientational correlations and reproduce non-Gaussian features in the ISF. By confronting these approximations with exact solutions based on spheroidal wave functions and with Brownian dynamics simulations, we identify their range of validity in terms of P\'eclet number, wavenumber, and observation timescales. An advantage of this method is its ability to yield approximate yet explicit expressions not only for the ISF but also for polarization and nematic fields, which are often neglected but relevant in scattering experiments and theoretical modeling. Beyond providing a practical guide to select the appropriate closure according to the spatiotemporal regime, our framework highlights the efficiency of moment-based approaches compared to exact yet implicit formulations. This strategy can be systematically extended to more complex situations, such as propulsion switching, confinement, or external fields, where functional bases for exact solutions are generally unavailable.

cond-mat.soft↗

arXiv2025

Dynamic correlations in a polar fluid: confronting stochastic density functional theory to simulations

Understanding the dynamic behavior of polar fluids is essential for modeling complex systems such as electrolytes and biological media. In this work, we develop and apply a Stochastic Density Functional Theory (SDFT) framework to describe the polarization dynamics in the Stockmayer fluid, a prototypical model of dipolar liquids consisting of Lennard-Jones particles with embedded point dipoles. Starting from the overdamped Langevin dynamics of dipolar particles, we derive analytical expressions for the intermediate scattering functions and dynamic structure factors of the longitudinal and transverse components of the polarization field, within linearized SDFT. To assess the theory's validity, we compare its predictions with results from Brownian Dynamics simulations of the Stockmayer fluid. We find that SDFT captures the longitudinal polarization fluctuations accurately, while transverse fluctuations are underestimated due to the neglect of dipolar correlations. By incorporating the Kirkwood factor into a modified SDFT, we recover quantitative agreement for both components across a range of dipole strengths. This study highlights the utility of SDFT as a coarse-grained description of polar fluid dynamics and provides insights into the role of collective effects in polarization relaxation.

cond-mat.soft↗

arXiv2025

Chemically active droplets in crowded environments

Biomolecular condensates are essential for cellular organization and result from phase separation in systems far from thermodynamic equilibrium. Among various models, chemically active droplets play a significant role, consisting of proteins that switch between attractive and repulsive states via nonequilibrium chemical reactions. While field-based simulations have provided insights into their behavior, these coarse-grained approaches fail to capture molecular-scale effects, particularly in crowded cellular environments. Macromolecular crowding, a key feature of intracellular organization, strongly influences molecular transport within condensates, yet its quantitative impact remains underexplored. This study investigates the interplay between chemically active droplets and crowders by using particle-based models, that provide molecular insight, and a field-based model, that complements this picture. Surprisingly, crowding reduces droplet size while expanding the overall dense phase volume, challenging equilibrium-based expectations. This effect arises from the interplay between depletion interactions, diffusion hindrance, and nonequilibrium particle fluxes. Our findings provide a step towards a more comprehensive understanding of chemically active droplets in complex, realistic cellular environments.

cond-mat.soft↗

arXiv2025

Control of encounter kinetics by chemically active droplets

Biomolecular condensates play a crucial role in the spatial organization of living matter. These membrane-less organelles, resulting from liquid-liquid phase separation, operate far from thermodynamic equilibrium, with their size and stability influenced by non-equilibrium chemical reactions. While condensates are frequently considered optimized nanoreactors that enhance molecular encounters, their actual impact on reaction kinetics remains unclear due to competing effects such as diffusion hindrance, and random trapping in non-specific condensates. In this study, we develop a microscopic, stochastic model for chemically active droplets, incorporating reaction-driven modulation of protein interactions. Using Brownian dynamics simulations, we investigate how protein interactions and active coupling to a free energy reservoir influence phase separation, molecular transport and reaction kinetics. We demonstrate that the intensity of the chemical drive governs surface dynamics, generating fluxes that modulate bimolecular reaction rates. Comparing active emulsions to homogeneous systems, we reveal that condensates can either accelerate or decelerate molecular encounters. Our findings provide key insights into the role of biomolecular condensates as potential regulators of intracellular reaction kinetics.

cond-mat.soft↗

arXiv2025

Dynamics of soft interacting particles on a comb

We study the dynamics of overdamped Brownian particles interacting through soft pairwise potentials on a comb-like structure. Within the linearized Dean-Kawasaki framework, we characterize the particle density fluctuations by computing their one- and two-point correlation functions. For a tracer particle constrained to move along the comb backbone, we determine the spatial correlation profile between its position and the density of surrounding bath particles. Furthermore, we derive the correction to the diffusion coefficient of the tracer due to interactions with other particles, validating our results through numerical simulations.

cond-mat.stat-mech↗

arXiv2025

Non-Gaussian density fluctuations in the Dean-Kawasaki equation

Computing analytically the $n$-point density correlations in systems of interacting particles is a long-standing problem of statistical physics, with a broad range of applications, from the interpretation of scattering experiments in simple liquids, to the understanding of their collective dynamics. For Brownian particles, i.e. with overdamped Langevin dynamics, the microscopic density obeys a stochastic evolution equation, known as the Dean-Kawasaki equation. In spite of the importance of this equation, its complexity makes it very difficult to analyze the statistics of the microscopic density beyond simple Gaussian approximations. In this work, resorting to a path-integral description of the stochastic dynamics and relying on a saddle-point analysis in the limit of high density and weak interactions between the particles, we go beyond the usual linearization of the Dean-Kawasaki equation, and we compute exactly the three- and four-point density correlation functions. This result opens the way to using the Dean-Kawasaki equation beyond the simple Gaussian treatments, and could find applications to understand many fluctuation-related effects in soft and active matter systems.

cond-mat.stat-mech↗

arXiv2024

The Dean-Kawasaki equation and stochastic density functional theory

The Dean-Kawasaki (DK) equation, which is at the basis of stochastic density functional theory (SDFT), was proposed in the mid-nineties to describe the evolution of the density of interacting Brownian particles, which can represent a large number of systems such as colloidal suspensions, supercooled liquids, polymer melts, biological molecules, active or chemotactic particles, or ions in solution. This theoretical framework, which can be summarized as a mathematical reformulation of the coupled overdamped Langevin equations that govern the dynamics of the particles, has attracted a significant amount of attention during the past thirty years. In this review, I present the context in which this framework was introduced, and I recall the main assumptions and calculation techniques that are employed to derive the DK equation. Then, in the broader context of statistical mechanics, I show how SDFT is connected to other theories, such fluctuating hydrodynamics, macroscopic fluctuation theory, or mode-coupling theory. The mathematical questions that are raised by the DK equation are presented in a non-specialist language. In the last parts of the review, I show how the original result was extended in several directions, I present the different strategies and approximations that have been employed to solve the DK equation, both analytically and numerically. I finally list the different situations where SDFT was employed to describe the fluctuations of Brownian suspensions, from the physics of active matter to the description of charged particles and electrolytes.

cond-mat.soft↗

arXiv2024

Active droplets controlled by enzymatic reactions

The formation of condensates is now considered as a major organization principle of eukaryotic cells. Several studies have recently shown that the properties of these condensates are affected by enzymatic reactions. We propose here a simple generic model to study the interplay between two enzyme populations and a two-state protein. In one state, the protein forms condensed droplets through attractive interactions, while in the other state, the proteins remain dispersed. Each enzyme catalyzes the production of one of these two protein states only when reactants are in its vicinity. A key feature of our model is the explicit representation of enzyme trajectories, capturing the fluctuations in their local concentrations. The spatially dependent growth rate of droplets naturally arises from the stochastic motion of these explicitly modeled enzymes. Using two complementary numerical methods, (1) Brownian Dynamics simulations, and (2) a hybrid method combining Cahn-Hilliard-Cook diffusion equations with Brownian Dynamics for the enzymes, we investigate how enzyme concentration and dynamics influence the evolution with time, and the steady-state number and size of droplets. Our results show that the concentration and diffusion coefficient of enzymes govern the formation and size-selection of biocondensates.

cond-mat.soft↗

arXiv2024

Universal scale-free decay of tracer-bath correlations in $d$-dimensional interacting particle systems

Quantifying the correlations between the position of a tagged tracer and the density of surrounding bath particles is crucial for understanding tracer diffusion in interacting particle systems, and for characterizing the response properties of the bath. We address this problem analytically for both hard-core and soft-core interactions, using minimal yet paradigmatic models in $d$ spatial dimensions. In both cases, we derive analytical expressions for the spatial correlation profiles in the reference frame of the tracer. We reveal unexpected universal features in their large-distance behavior, characterized by power-law tails with exponents that depend solely on the spatial dimensionality of the system. Beyond these simple models, we demonstrate the robustness of our results across different regimes using particle-based numerical simulations.

cond-mat.stat-mech↗

arXiv2024

Stochastic density functional theory for ions in a polar solvent

In recent years, the theoretical description of electrical noise and fluctuation-induced effects in electrolytes has gained a renewed interest, enabled by stochastic field theories like stochastic density functional theory (SDFT). Such models, however, treat solvents implicitly, ignoring their generally polar nature. In the present study, starting from microscopic principles, we derive a fully explicit SDFT theory that applies to ions in a polar solvent. These equations are solved to compute observables like dynamic charge structure factors and dielectric susceptibilities. We unveil the relative importance of the different contributions (solvent, ions, cross terms) to the dynamics of electrolytes, which are key to understand the couplings between ions and the fluctuations of their microscopic environment.

cond-mat.soft↗

arXiv2024

Microscopic and stochastic simulations of chemically active droplets

Biomolecular condensates play a central role in the spatial organization of living matter. Their formation is now well understood as a form of liquid-liquid phase separation that occurs very far from equilibrium. For instance, they can be modeled as active droplets, where the combination of molecular interactions and chemical reactions result in microphase separation. However, so far, models of chemically active droplets are spatially continuous and deterministic. Therefore, the relationship between the microscopic parameters of the models and some crucial properties of active droplets (such as their polydispersity, their shape anisotropy, or their typical lifetime) is yet to be established. In this work, we address this question computationally, using Brownian dynamics simulations of chemically active droplets: the building blocks are represented explicitly as particles that interact with attractive or repulsive interactions, depending on whether they are in a droplet-forming state or not. Thanks to this microscopic and stochastic view of the problem, we reveal how driving the system away from equilibrium in a controlled way determines the fluctuations and dynamics of active emulsions.

cond-mat.soft↗

arXiv2024

Chemotactic particles as strong electrolytes: Debye-Hückel approximation and effective mobility law

We consider a binary mixture of chemically active particles, that produce or consume solute molecules, and that interact with each other through the long-range concentrations fields they generate. We analytically calculate the effective phoretic mobility of these particles when the mixture is submitted to a constant, external concentration gradient, at leading order in the overall concentration. Relying on a analogy with the modeling of strong electrolytes, we show that the effective phoretic mobility decays with the square-root of the concentration: our result is therefore a nonequilibrium counterpart to the celebrated Kohlrausch and Debye-Hückel-Onsager conductivity laws for electrolytes, which are extended here to particles with long-range nonreciprocal interactions. The effective mobility law we derive reveals the existence of a regime of maximal mobility, and could find applications in the description of nanoscale transport phenomena in living cells.

cond-mat.soft↗

arXiv2024

From Particle Currents to Tracer Diffusion: Universal Correlation Profiles in Single-File Dynamics

Single-file transport refers to the motion of particles in a narrow channel, such that they cannot bypass each other. This constraint leads to strong correlations between the particles, described by correlation profiles, which measure the correlation between a generic observable and the density of particles at a given position and time. They have recently been shown to play a central role in single-file systems. Up to now, these correlations have only been determined for diffusive systems in the hydrodynamic limit. Here, we consider a model of reflecting point particles on the infinite line, with a general individual stochastic dynamics. We show that the correlation profiles take a simple universal form, at arbitrary time. We illustrate our approach by the study of the integrated current of particles through the origin, and apply our results to representative models such as Brownian particles, run-and-tumble particles and Lévy flights. We further emphasise the generality of our results by showing that they also apply beyond the 1d case, and to other observables.

cond-mat.stat-mech↗

arXiv2023

Enhanced diffusion of tracer particles in nonreciprocal mixtures

We study the diffusivity of a tagged particle in a binary mixture of Brownian particles with non-reciprocal interactions. Numerical simulations reveal that, for a broad class of interaction potentials, non-reciprocity can significantly increase the effective diffusion coefficient of tracer particles, and that this diffusion enhancement is associated with a breakdown of the Einstein relation. These observations are quantified and confirmed via two different and complementary analytical approaches: (i) a linearized stochastic density field theory, which is particularly accurate in the limit of soft interactions; (ii) a reduced two-body description, which is exact at leading order in the density of particles. The latter reveals that diffusion enhancement can be attributed to the formation of transiently propelled dimers of particles, whose cohesion and speed are controlled by the non-reciprocal interactions.

cond-mat.soft↗