arXiv subjects

Benno Liebchen

Publications and source records attributed to Benno Liebchen.

At least 19 recordsLinked to original sources

arXiv2026

Quantization of the classical Mpemba effect

The Mpemba effect refers to the counterintuitive phenomenon that an initially hot system can freeze faster than an initially warm one. Recent years have brought major advances in both classical and quantum realizations, with the asymmetric bistable potential emerging as the paradigmatic classical benchmark because its mechanism is especially transparent and controllable. Yet for precisely this benchmark problem, the impact of quantization remains unexplored. Here we show that quantization shifts Mpemba behavior to qualitatively new regimes, moving it to ultra-cold temperatures that are orders of magnitude lower than those relevant for classical thermal barrier crossing. In addition, quantization produces inverse and double inverse Mpemba effects that are absent in the corresponding classical dynamics. Our results establish quantization as a robust route to quantum-enabled Mpemba effects inaccessible in classical regimes.

quant-ph↗

arXiv2026

Quantum-enabled active matter at the atomic scale

Active matter comprises particles that extract energy from their local environment and convert it into motion. Although active particles have been miniaturized down to the nanoscale, realizing activity at the fundamentally smaller scale of individual atoms remains an open challenge, where quantum effects become increasingly relevant. Here, we experimentally demonstrate that individual Cs-133 atoms confined in an optical dipole trap extract energy from an ultracold bath of Rb-87 atoms via quantum-mechanical spin interactions and convert it into active motion. We quantitatively reproduce the resulting dynamics using a parameter-free active Langevin model derived from kinetic theory and support it with event-driven Monte Carlo collision simulations. The microscopic origin of activity is identified as quantum spin exchange, which transfers discrete internal spin energy into kinetic motion. Our work establishes a quantum-enabled route to active matter at the fundamental size limit of single atoms and opens perspectives for exploring the interplay of activity, quantum physics, and mesoscopic non-equilibrium thermodynamics.

quant-ph↗

arXiv2026

Anomalous Mean-Squared Displacement in Quantum Active Matter from a Wigner Phase-Space Framework

Active matter is driven out of equilibrium by a local influx of energy. While classical active matter has been extensively studied, the extension of active matter concepts to quantum systems has been explored far less. In this work we develop a full quantum description based on the Wigner function. By introducing a hybrid Wigner master equation that incorporates classical active motion and quantum degrees of freedom, we compute the quantum mean-squared displacement (MSD) using established techniques from classical active matter. We analytically derive the time dependence of the MSD and clarify the conditions under which the characteristic scaling with time $\mathrm{MSD}\sim t^{6}$ emerges, namely the regime of long persistence time and large active noise strength. We also show that, for certain parameter and initial conditions, the MSD can exhibit an even steeper scaling regime $\mathrm{MSD}\sim t^{7}$. In addition, explicit expressions are derived that precisely predict the onset times of $t^6$ and $t^7$ scaling behaviors. Finally, we examine the robustness of these behaviors against quantum fluctuations of the initial state.

cond-mat.soft↗

arXiv2026

Learning microstructure in active matter

Understanding microstructure in terms of closed-form expressions is an open challenge in nonequilibrium statistical physics. We propose a simple and generic method that combines particle-resolved simulations, deep neural networks and symbolic regression to predict the pair-correlation function of passive and active particles. Our analytical closed-form results closely agree with Brownian dynamics simulations, even at relatively large packing fractions and for strong activity. The proposed method is broadly applicable, computationally efficient, and can be used to enhance the predictive power of nonequilibrium continuum theories and for designing pattern formation.

cond-mat.soft↗

arXiv2026

Learning Effective Hydro-Phoretic Interactions in Active Matter

In the quest to understand large-scale collective behavior in active matter, the complexity of hydrodynamic and phoretic interactions remains a fundamental challenge. To date, most works either focus on minimal models that do not (fully) account for these interactions, or explore relatively small systems. The present work develops a generic method that combines high-fidelity simulations with symmetry-preserving descriptors and neural networks to predict hydro-phoretic interactions directly from particle coordinates (effective interactions). This method enables, for the first time, self-contained particle-only simulations and theories with full hydro-phoretic two-body interactions.

cond-mat.soft↗

arXiv2026

Automated decision-making by chemical echolocation in active droplets

Motile microorganisms, like bacteria and algae, unify abilities like self-propulsion, autonomous navigation, and decision-making on the micron scale. While recent breakthroughs have led to the creation of synthetic microswimmers and nanoagents that can also self-propel, they still lack the functionality and sophistication of their biological counterparts. This study pioneers a mechanism enabling synthetic agents to autonomously navigate and make decisions, allowing them to solve mazes and transport cargo through complex environments without requiring external cues or guidance. The mechanism exploits chemo-hydrodynamic signals, produced by agents like active droplets or colloids, to remotely sense and respond to their environment - similar to echolocation. Our research paves the way for endowing autonomous, motile synthetic agents with functionalities that have been so far exclusive to biological organisms.

cond-mat.soft↗

arXiv2025

Quantifying surfactant adsorption at fluid interfaces by combining X-ray reflectivity and simulations

Adsorption of surfactants to fluid interfaces occurs in numerous daily-life and technological contexts. The surfactant surface coverage $\Gamma$ governs interface characteristics like tension $\gamma$, viscoelastic properties, and the stability of thin foam films. Directly measuring $\Gamma$ as a function of the bulk concentration $c$ is highly desirable but challenging, particularly for non-ionic surfactants that lack easily detectable labels. Neutron reflectometry is currently the only generally applicable method, but it is not available for routine experiments. Here, we propose a simulation-assisted approach to deduce the adsorption isotherm $\Gamma(c)$ from X-ray reflectivity data: As a first step, we use atomistic molecular dynamics simulations of surfactant-loaded air/water interfaces with prespecified $\Gamma$ to obtain interfacial electron density profiles. From these profiles, we compute theoretical X-ray reflectivity curves and compare them with experimental measurements to determine the matching bulk concentration. We focus on two non-ionic surfactants (C$_{12}$EO$_6$ and $\beta$-C$_{12}$G$_2$}) with previously established force fields to illustrate how this combined approach of experiments and simulations can determine the adsorption isotherm. Additional insights are gained through comparison with the measured surface tension isotherms $\gamma(c)$, based on the equation of state $\gamma(\Gamma)$ from simulations.

cond-mat.soft↗

arXiv2025

Modeling dissipation in quantum active matter

Active matter is characterized by a constant influx and dissipation of energy that gives rise to directed motion. Dissipation requires interactions with an external environment, such that extending the paradigm of active matter to a quantum framework requires an appropriate description of this environment. In this work, we consider a driven quantum particle undergoing noise and dissipation, with external driving exhibiting characteristics of classical activity. We model the non-unitary dynamics with time-local master equations and analyze the particle motion at different time scales for different forms of the master equations, satisfying different criteria. We systematically compare predictions on the dynamics of particle trajectories and thereby we uncover how the particle motion evolves under the interplay of quantum effects, dissipation, and active-like dynamics. These results are essential for guiding possible experiments aimed at realizing quantum analogues of classical active systems.

quant-ph↗

arXiv2025

Active thermodynamics of inertial chiral active gases: equation of state and edge currents

One of the most fundamental quests in the physics of active matter concerns the existence of a comprehensive theory for its macroscopic properties, i.e. an ``active thermodynamics''. Here, we derive and experimentally verify key elements of the active thermodynamics of ideal chiral active gases, unveiling edge currents and odd diffusivity as their peculiar features. Our main results are the derivation of an equation of state relating density and pressure via a chirality-dependent effective temperature, the derivation of Fick's law including the full diffusion matrix predicting odd diffusion, and the exact prediction of edge currents at container walls that nonmonotonically depend on chirality.

cond-mat.soft↗

arXiv2025

Jetting with gels: Soft microgel networks stabilize and extend nozzle-free water jets

The stability of high-speed liquid jets is crucial for applications ranging from precision printing to needle-free drug delivery, yet it is fundamentally limited by capillary-driven breakup. A common strategy to stabilize jets is to use surfactants to lower surface tension. However, in nozzle-free jetting driven by surface acoustic waves (SAWs), extreme deformation rates cause conventional surfactants to desorb, calling for alternative strategies to stabilize jets. In particular, we find that tuning the nanoscale softness of PNIPAM microgels provides a robust, biocompatible strategy to overcome this limitation. Soft, low-cross-linker-density microgels form elastic interfacial networks at the air-water interface that suppress surface tension recovery, delay Rayleigh-Plateau instabilities, and extend SAW-driven jet lengths by up to 44%. In contrast, stiffer microgels lose network cohesion under strain, leading to rapid jet breakup. To gain molecular-level insights, we perform dissipative particle dynamics simulations, which reveal that polymer bridges in soft microgels remain entangled during elongation, maintaining a reduced effective surface tension. Finally, a simple scaling analysis, balancing the SAW-driven kinetic energy imparted to the droplet against the surface energy required to form a jet, quantitatively predicts the observed length enhancement. This surfactant-free, biocompatible approach lays the foundation for long-lived jets, enabling precision needle-free drug delivery, high-speed printing, and other high-strain interfacial flow applications.

cond-mat.soft↗

arXiv2025

What exactly is 'active matter'?

As the study of active matter has developed into one of the most rapidly growing subfields of condensed matter physics, more and more kinds of physical systems have been included in this framework. While the word 'active' is often thought of as referring to self-propelled particles, it is also applied to a large variety of other systems such as non-polar active nematics or certain particles with non-reciprocal interactions. Developing novel forms of active matter, as attempted, e.g., in the framework of quantum active matter, requires a clear idea of what active matter is. Here, we critically discuss how the understanding of active matter has changed over time, what precisely a definition of 'active matter' can look like, and to what extent it is (still) possible to define active matter in a way that covers all systems that are commonly understood as active matter while distinguishing them from other driven systems. Moreover, we discuss the definition of an 'active field theory', where 'active' is used as an attribute of a theoretical model rather than of a physical system. We show that the usage of the term 'active' requires agreement on a coarse-grained viewpoint. We discuss the meaning of 'active' both in general terms and via the specific examples of chemically driven particles, ultrasound-driven particles, active nematics, particles with non-reciprocal interactions, intracellular phase separation, and quantum active matter.

cond-mat.soft↗

arXiv2025

A general model for frictional contacts in colloidal systems

In simulations of colloidal matter, frictional contacts between particles are often neglected. For spherical colloids, such an approximation can be problematic, since frictional contacts couple translational and rotational degrees of freedom, which may affect the collective behavior of, e.g., colloids under shear and chiral active matter. Deterministic models for frictional contacts have been proposed in the granular matter community. On the colloidal scale, however, thermal fluctuations are important and should be included in a thermodynamically consistent manner. Here, we derive the correct fluctuation-dissipation relation for linear and nonlinear instantaneous frictional contact interactions. Among other, this generates a new generalized class of dissipative particle dynamics (DPD) thermostats with rotation-translation coupling. We demonstrate effects of frictional contact interactions using the examples of Poiseuille flow and motility induced phase separation in active Langevin particles.

cond-mat.soft↗

arXiv2025

Fluctuation induced network patterns in active matter with spatially correlated noise

Fluctuations play a central role in many fields of physics, from quantum electrodynamics to statistical mechanics. In active matter physics, most models focus on thermal fluctuations due to a surrounding solvent. An alternative but much less explored noise source can occur due to fluctuating external fields, which typically feature certain spatial correlations. In this work, we introduce a minimal model to explore the influence of spatially correlated but temporally uncorrelated noise on the collective behavior of active particles. We find that specifically in chiral active particles such fluctuations induce the formation of network patterns, which neither occur for spatially (uncorrelated) thermal noise, nor in the complete absence of fluctuations. These networks show (i) a percolated structure, (ii) local alignment of the contained particles, but no global alignment, and (iii) hardly coarsen. We perform a topological data analysis to systematically characterize the topology of the network patterns. Our work serves as a starting point to explore the role of spatially correlated fluctuations and presents a route towards noise-induced phenomena in active matter.

cond-mat.soft↗

arXiv2025

Towards Intelligent Active Particles

In this book chapter we describe recent applications of artificial intelligence and in particular machine learning to active matter systems. Active matter is composed of agents, or particles, that are capable of propelling themselves. While biological agents like bacteria, fish or birds naturally possess a certain degree of "intelligence", synthetic active particles like colloidal microswimmers and electronic robots can be equipped with different levels of artificial intelligence, either internally (as for robots) or via a dynamic external control system. This book chapter briefly discusses existing approaches to make synthetic particles increasingly "intelligent" and then focuses on the usage of machine learning to approach navigation and communication problems of active particles. Basic questions are how to steer a single active agent through a complex environment to reach or discover a target in an optimal way and how active particles need to cooperate to efficiently collect a distribution of targets (e.g. nutrients or toxins) from their complex environment.

cond-mat.soft↗

arXiv2024

The 2024 Motile Active Matter Roadmap

Activity and autonomous motion are fundamental aspects of many living and engineering systems. Here, the scale of biological agents covers a wide range, from nanomotors, cytoskeleton, and cells, to insects, fish, birds, and people. Inspired by biological active systems, various types of autonomous synthetic nano- and micromachines have been designed, which provide the basis for multifunctional, highly responsive, intelligent active materials. A major challenge for understanding and designing active matter is their inherent non-equilibrium nature due to persistent energy consumption, which invalidates equilibrium concepts such as free energy, detailed balance, and time-reversal symmetry. Furthermore, interactions in ensembles of active agents are often non-additive and non-reciprocal. An important aspect of biological agents is their ability to sense the environment, process this information, and adjust their motion accordingly. It is an important goal for the engineering of micro-robotic systems to achieve similar functionality. With many fundamental properties of motile active matter now reasonably well understood and under control, the ground is prepared for the study of physical aspects and mechanisms of motion in complex environments, of the behavior of systems with new physical features like chirality, of the development of novel micromachines and microbots, of the emergent collective behavior and swarming of intelligent self-propelled particles, and of particular features of microbial systems. The vast complexity of phenomena and mechanisms involved in the self-organization and dynamics of motile active matter poses major challenges, which can only be addressed by a truly interdisciplinary effort involving scientists from biology, chemistry, ecology, engineering, mathematics, and physics.

cond-mat.soft↗

arXiv2024

How to define temperature in active systems?

We are used to measure temperature with a thermometer and we know from everyday life that different types of thermometers measure the same temperature. This experience can be based on equilibrium thermodynamics, which explains the equivalence of different possibilities to define temperature. In contrast, for systems out of equilibrium such as active matter, measurements performed with different thermometers can generally lead to different temperature values. In the present work, we systematically compare different possibilities to define temperature for active systems. Based on simulations and theory for inertial active Brownian particles, we find that different temperatures generally lead to different temperature values, as expected. Remarkably, however, we find that different temperatures not only lead to the same values near equilibrium (low P\'eclet number or high particle mass), but even far from equilibrium, several different temperatures approximately coincide. In particular, we find that the kinetic temperature, the configurational temperature, and temperatures based on higher moments of the velocity distribution constitute a class of temperatures that all assume very similar values over a wide parameter range. Notably, the effective temperature and temperatures exploiting the virial theorem, the Stokes-Einstein relation, or a harmonic confinement form a second class of temperatures whose values approximately coincide with each other but which strongly differ from those of the first class. Finally, we identify advantages and disadvantages of the different possibilities to define temperature and discuss their relevance for measuring the temperature of active systems.

cond-mat.soft↗

arXiv2024

Self-reverting vortices in chiral active matter

There is currently a strong interest in the collective behavior of chiral active particles that can propel and rotate themselves. In the presence of alignment interactions for many chiral particles, chiral self-propulsion can induce vortex patterns in the velocity fields. However, these emerging patterns are non-permanent, and do not induce global vorticity. Here we combine theoretical arguments and computer simulations to predict a so-far unknown class of collective behavior. We show that, for chiral active particles, vortices with significant dynamical coherence emerge spontaneously. They originate from the interplay between attraction interactions and chirality in the absence of alignment interactions. Depending on parameters, the vortices can either feature a constant vorticity or a vorticity that oscillates periodically in time, resulting in self-reverting vortices. Our results may guide future experiments to realize customized collective phenomena such as spontaneously rotating gears and patterns with a self-reverting order.

cond-mat.soft↗

arXiv2024

AMEP: The Active Matter Evaluation Package for Python

The Active Matter Evaluation Package (AMEP) is a Python library for analyzing simulation data of particle-based and continuum simulations. It provides a powerful and simple interface for handling large data sets and for calculating and visualizing a broad variety of observables that are relevant to active matter systems. Examples range from the mean-square displacement and the structure factor to cluster-size distributions, binder cumulants, and growth exponents. AMEP is written in pure Python and is based on powerful libraries such as NumPy, SciPy, Matplotlib, and scikit-image. Computationally expensive methods are parallelized and optimized to run efficiently on workstations, laptops, and high-performance computing architectures, and an HDF5-based data format is used in the backend to store and handle simulation data as well as analysis results. AMEP provides the first comprehensive framework for analyzing simulation results of both particle-based and continuum simulations (as well as experimental data) of active matter systems. In particular, AMEP also allows it to analyze simulations that combine particle-based and continuum techniques such as used to study the motion of bacteria in chemical fields or for modeling particle motion in a flow field. AMEP is available at https://amepproject.de and can be installed via conda and pip.

cond-mat.soft↗