arXiv subjects

Yael Roichman

Publications and source records attributed to Yael Roichman.

At least 19 recordsLinked to original sources

arXiv2026

Piston-Like Information Engine II: Boundary-Controlled Optimum in Active Matter

Information engines convert information into extractable work through measurements and feedback. We realize an engine that employs information to compress a gas of self-propelled bristle bots. Its work per measurement, $\bar{W}$, is controlled by the size of the detection region $Δx$ and the probability $p_1$ that this region is vacant. In thermal systems, $\bar{W}$ follows the universal form $-p_1 \ln p_1$, whereas the active engine shows a qualitatively modified relation. As particle density increases, the maximum of $\bar{W}(Δx)$ switches between two distinct operating regimes. Notably, this transition is also found in the solutions that maximize power output for finite-time cycles with dynamics affected by dry friction. We attribute this nonequilibrium feature to the accumulation of active particles near the boundaries.

cond-mat.stat-mech↗

arXiv2026

Stochastic Resetting: A Non-Equilibrium Framework for Prediction, Inference and Design

Stochastic resetting has evolved from a simple model of diffusive search acceleration into a general framework for predicting, inferring, and controlling stochastic dynamics far from equilibrium. Its defining features, i.e., the creation of non-equilibrium steady states and the acceleration of first-passage kinetics, are increasingly relevant across physical chemistry, from biological restart mechanisms to molecular simulations and colloidal experiments. We review the renewal theory underlying stochastic resetting and show how it enables prediction of reset dynamics from properties of the underlying process, while also allowing the latter to be inferred from the resetting-accelerated dynamics. We then discuss applications to state preparation, enhanced sampling, kinetic inference, and training and sampling of machine learning models. Finally, we review recent advances in adaptive resetting, environmental feedback, many-body dynamics, and thermodynamic costs of resetting. These developments establish new opportunities for controlling stochastic dynamics with resetting across theory, simulations, and experiments.

physics.chem-ph↗

arXiv2026

A particle-resolved rheological study of chirality transfer and odd transport

Chirality, or the breaking of mirror symmetry, appears across all scales in nature, from molecular conformations to the dynamics of bacterial collectives. Environments composed of such symmetry-breaking constituents can give rise to emergent physical phenomena, particularly in the transport and response of embedded tracers. Yet it remains unclear how chiral environments influence such tracers and through which microscopic mechanisms anomalous responses emerge. Here, we present a particle-resolved study of these systems, demonstrating chirality transfer and odd transport of an object embedded in a chiral active bath. In a rheological experiment, a symmetric passive tracer is driven through collisions with the particles of a non-equilibrium chiral bath. Combining table-top experiments, many-body simulations, and a reduced coarse-grained theory, we demonstrate that local collisions transfer chiral active dynamics to the tracer, which displays circular trajectories. We show that the same mechanism gives rise to a systematic transverse drift under a constant pulling force. Crucially, we identify nonlinear friction as an essential factor that rectifies these transferred chiral active fluctuations into a macroscopic odd response. Our results reveal a microscopic mechanism for odd transport in chiral active matter and provide general insights into transverse transport in driven non-equilibrium systems.

cond-mat.stat-mech↗

arXiv2026

Free chiral self-propelled robots compared to active Brownian circle swimmers

Macroscopic active matter systems, such as bristle bots, provide a compelling platform for investigating nonequilibrium dynamics at highly visible scales. To fully leverage their accessibility, accurate mathematical models are needed to corroborate experiments. In this work, we study the motion of a free chiral hexbug (Nano-Newton Series) via video tracking and compare the results to theoretical predictions from overdamped Langevin equations for active Brownian circle swimmers (ABCs). We find good agreement between the hexbug's dynamics and ABC model predictions, particularly for the mean-squared displacement and the intermediate scattering function (ISF). Deviations between the hexbug data and the ABC model arise primarily in the short-time behavior of the real-space propagator, where translational noise is most evident. Our results generally support the use of models based on overdamped Langevin equations as a robust framework for describing hexbug motion when the influence of translational noise is negligible. Moreover, they demonstrate the sensitivity of ISF- and propagator-based analyses in characterizing active systems. Our approach opens new avenues toward refining coarse-grained models and advancing the theoretical understanding of macroscopic active systems.

cond-mat.soft↗

arXiv2026

Probing the limits of effective temperature consistency in actively driven systems

We investigate the thermodynamic properties of a single inertial probe driven into a nonequilibrium steady-state by random collisions with self-propelled active walkers. The probe and walkers are confined within a gravitational harmonic potential. We evaluate the robustness of the effective temperature concept in this active system by comparing values of distinct, independently motivated definitions: a generalized fluctuation-dissipation relation, a kinetic temperature, and a work fluctuation relation. Our experiments reveal that, under specific conditions, these independent measurements yield a remarkably consistent effective temperature over a wide range of system configurations. Furthermore, we also identify regimes where this consistency breaks down, which delineates the fundamental limits of extending equilibrium-like thermodynamic concepts to athermal, actively driven systems.

cond-mat.stat-mech↗

arXiv2026

Fluctuation-response relation for a nonequilibrium system with resolved Markovian embedding

Fluctuation-response relations must be modified to describe nonequilibrium systems with non-Markovian dynamics. Here, we experimentally demonstrate that such relation is quantitatively recovered when the appropriate Markovian embedding of the dynamics is explicitly resolved. Using a colloidal particle optically trapped in a harmonic potential and driven out of equilibrium by a controlled colored noise, we study the response to a perturbation of the stiffness of the confining potential. While the reduced dynamics violates equilibrium fluctuation-response relations, we show that the dynamical response to the stiffness perturbation is fully determined by steady-state correlations involving the exact conjugate observable in the Markovian embedding.

cond-mat.stat-mech↗

arXiv2026

Consistent thermodynamics reconstructed from transitions between nonequilibrium steady-states

Constructing a thermodynamic framework for nonequilibrium systems remains a major challenge, as quantities such as temperature and free energy often become ambiguous when inferred solely from steady-state properties. Here we take a transformation-based approach and experimentally examine transitions between nonequilibrium steady states (NESS). Using an optically trapped microparticle driven by a tunable correlated stochastic force, we generate active-like steady states with controllable noise statistics. By abruptly changing the trap stiffness, we measure the stochastic work, heat, and entropy produced during NESS-to-NESS transformations. We identify a state-dependent effective temperature that restores the second law for these transitions, enabling the definition of a generalized work that incorporates the consequence of the nonequilibrium fluctuations. With this quantity, we derive and experimentally verify a Crooks-like fluctuation relation linking work distributions to a nonequilibrium free-energy difference defined through the effective temperature. Finally, we establish a fluctuation-response relation for the positional variance following stiffness changes. We demonstrate that this relation is key to distinguishing systems that can be described by a unique effective temperature (i.e., those under equilibrium or white-noise conditions) from those under colored-noise, where an equilibrium-like response cannot be restored. These results delineate the applicability and limits of effective-temperature thermodynamics in driven systems.

cond-mat.stat-mech↗

arXiv2025

Piston-Like Information Engine I: Universal Features in Equilibrium

The ability to measure the stochastic degrees of freedom of a thermal system enables the extraction of energy from an equilibrium heat bath. This is the underlying principle of Maxwell's demon and subsequent information engines. Here, we experimentally realize a microscopic information engine configured as a compressible piston containing a thermalized colloidal suspension. The particle positions are recorded to identify when a predefined region near the wall is empty, allowing the piston to compress the colloidal suspension without applying work on the system. We find that the stored compression energy is universally set by the probability of a positive measurement outcome, which in turn is controlled by parameters such as density and compression step size. We further demonstrate that mechanical work can be extracted during the decompression of the piston, thereby closing the engine's operating cycle.

cond-mat.stat-mech↗

arXiv2025

Stochastic Resetting vs. Thermal Equilibration: Faster Relaxation, Different Destination

Stochastic resetting is known for its ability to accelerate search processes and induce non-equilibrium steady states. Here, we compare the relaxation times and resulting steady states of resetting and thermal relaxation for Brownian motion in a harmonic potential. We show that resetting always converges faster than thermal equilibration, but to a different steady-state. The acceleration and the shape of the steady-state are governed by a single dimensionless parameter that depends on the resetting rate, the viscosity, and the stiffness of the potential. We observe a trade-off between relaxation speed and the extent of spatial exploration as a function of this dimensionless parameter. Moreover, resetting relaxes faster even when resetting to positions arbitrarily far from the potential minimum.

cond-mat.stat-mech↗

arXiv2025

Time-energy tradeoff in stochastic resetting using optimal control

Stochastic resetting is a driving mechanism that is known to minimize the first passage time to reach a target, at the cost of energy expenditure. The choice of the physical implementation of each resetting event determines the tradeoff between the acceleration of the search process and its energetic cost. Here, we present an optimal transport protocol that balances the duration and the energetic cost of each resetting event. This protocol drives a harmonically trapped Brownian particle between two equilibrium states within a finite time and with minimal energetic cost. An explicit comparison with other types of finite-time protocols further shows its specific thermodynamic properties. Its cost is both a lower bound on the cost of unoptimized shortcut protocols and an upper bound on the cost of optimal protocols which do not ensure final equilibrium. When applying the optimal transport protocol to implement stochastic resetting, a single lower time-energy bound is reached: this protocol allows to reach the best tradeoff between energetic cost and search time.

cond-mat.stat-mech↗

arXiv2025

Channel Formation Enhances Target Consumption by Chemotactic Active Brownian Particles

In many situations, simply finding a target during a search is not enough. It is equally important to be able to return to that target repeatedly or to enable a larger community to locate and utilize it. While first passage time is commonly used to measure search success, relatively little is known about increasing the average rate of target encounters over time. Here, using an active Brownian particle model with chemotaxis, we demonstrate that when a searcher has no memory and there is no communication among multiple searchers, encoding information about the target's location in the environment outperforms purely memoryless strategies by boosting the overall hit rate. We further show that this approach reduces the impact of target size on a successful search and increases the total utilization time of the target.

cond-mat.soft↗

arXiv2025

Diffusive contact between randomly driven colloidal suspensions

We study the relaxation process of two driven colloidal suspensions in diffusive contact to a steady state, similar to thermalization. We start by studying a single suspension, subjecting it to random driving forces via holographic optical tweezers, which agitate it to a higher effective temperature. Interestingly, the effective temperature of the suspension, defined by the Einstein relation, exhibits a non-monotonic dependence on the driving frequency. Next, we follow the flux of particles between two such suspensions in diffusive contact, starting from a uniform density and relaxing to a state with zero net particle flux. The density remains uniform for systems with different frequencies but equal effective temperatures. At high driving frequencies, we show that the density distribution at steady state is determined by equating the ratio of the chemical potential to the effective temperature in both systems, mirroring thermal equilibrium behavior.

cond-mat.soft↗

arXiv2025

Many-Body Colloidal Dynamics under Stochastic Resetting: Competing Effects of Particle Interactions on the Steady State Distribution

The random arrest of the diffusion of a single particle and its return to its origin has served as the paradigmatic example of a large variety of processes undergoing stochastic resetting. While the implications and applications of stochastic resetting for a single particle are well understood, less is known about resetting of many interacting particles. In this study, we experimentally and numerically investigate a system of six colloidal particles undergoing two types of stochastic resetting protocols: global resetting, where all particles are returned to their origin simultaneously, and local resetting, where particles are reset one at a time. Our particles interact mainly through hard-core repulsion and hydrodynamic flows. We find that the most substantial effect of interparticle interactions is observed for local resetting, specifically when particles are physically dragged to the origin. In this case, hard-core repulsion broadens the steady-state distribution, while hydrodynamic interactions significantly narrow the distribution. The combination results in a steady-state distribution that is wider compared to that of a single particle system both for global and local resetting protocols.

cond-mat.soft↗

arXiv2025

Harnessing non-equilibrium forces to optimize work extraction

While optimal control theory offers effective strategies for minimizing energetic costs in noisy microscopic systems over finite durations, a significant opportunity lies in exploiting the temporal structure of non-equilibrium forces. We demonstrate this by presenting exact analytical forms for the optimal protocol and the corresponding work for any driving force and protocol duration. We also derive a general quasistatic bound on the work, relying only on the coarse-grained, time-integrated characteristics of the applied forces. Notably, we show that the optimal protocols often automatically act as information engines that harness information about non-equilibrium forces and an initial state measurement to extract work. These findings chart new directions for designing adaptive, energy-efficient strategies in noisy, time-dependent environments, as illustrated through our examples of periodic driving forces and active matter systems. By exploiting the temporal structure of non-equilibrium forces, this largely unexplored approach holds promise for substantial performance gains in microscopic devices operating at the nano- and microscale.

cond-mat.stat-mech↗

arXiv2025

Experimental Realizations of Information Engines: Beyond Proof of Concept

Gathering information about a system enables greater control over it. This principle lies at the core of information engines, which use measurement-based feedback to rectify thermal noise and convert information into work. Originating from Maxwell's and Szilárd's thought experiments, the thermodynamics of information engines has steadily advanced, with recent experimental realizations both confirming established results and pushing the field forward. Coupled with technological advances and developments in nonequilibrium thermodynamics, novel implementations of information engines continue to challenge theoretical understanding. In this perspective, we discuss recent progress and highlight new opportunities, such as applying information engines to active, many-body, and inertial systems, and leveraging tools like optimal control to design their driving protocols.

cond-mat.stat-mech↗

arXiv2024

Numerical prediction of the steady-state distribution under stochastic resetting from measurements

A common and effective method for calculating the steady-state distribution of a process under stochastic resetting is the renewal approach that requires only the knowledge of the reset-free propagator of the underlying process and the resetting time distribution. The renewal approach is widely used for simple model systems such as a freely diffusing particle with exponentially distributed resetting times. However, in many real-world physical systems, the propagator, the resetting time distribution, or both are not always known beforehand. In this study, we develop a numerical renewal method to determine the steady-state probability distribution of particle positions based on the measured system propagator in the absence of resetting combined with the known or measured resetting time distribution. We apply and validate our method in two distinct systems: one involving interacting particles and the other featuring strong environmental memory. Thus, the renewal approach can be used to predict the steady state under stochastic resetting of any system, provided that the free propagator can be measured and that it undergoes complete resetting.

cond-mat.stat-mech↗

arXiv2024

The fluctuation-dissipation relation holds for a macroscopic tracer in an active bath

The fluctuation-dissipation relation (FDR) links thermal fluctuations and dissipation at thermal equilibrium through temperature. Extending it beyond equilibrium conditions in pursuit of broadening thermodynamics is often feasible, albeit with system-dependent specific conditions. We demonstrate experimentally that a generalized FDR holds for a harmonically trapped tracer colliding with self-propelled walkers. The generalized FDR remains valid across a large spectrum of active fluctuation frequencies, extending from underdamped to critically damped dynamics, which we attribute to a single primary channel for energy input and dissipation in our system.

cond-mat.stat-mech↗

arXiv2024

Smart Resetting: An Energy-Efficient Strategy for Stochastic Search Processes

Stochastic resetting, a method for accelerating target search in random processes, often incurs temporal and energetic costs. For a diffusing particle, a lower bound exists for the energetic cost of reaching the target, which is attained at low resetting rates and equals the direct linear transportation cost against fluid drag. Here, we study ``smart resetting," a strategy that aims to beat this lower bound. By strategically resetting the particle only when this benefits its progress toward the target, smart resetting leverages information to minimize energy consumption. We analytically calculate the energetic cost per mean first passage time and show that smart resetting consistently reduces the energetic cost compared to regular resetting. Surprisingly, smart resting achieves the minimum energy cost previously established for regular resetting, irrespective of the resetting rate. Yet, it fails to reduce this cost further. We extend our findings in two ways: first, by examining nonlinear energetic cost functions, and second, by considering smart resetting of drift-diffusion processes.

cond-mat.stat-mech↗