Collective Dynamics Research Articles

Longitudinal and transverse collective dynamics in water by simulation using the BK3 model.

Using a recent polarizable model for water (the BK3 model), we explore the collective dynamic modes in liquid water by molecular dynamics (MD) simulation. The dynamic structure factor and the longitudinal and transverse current correlation spectral densities are computed over the whole frequency range below intramolecular excitations. MD results of atom-atom partial correlation functions are fitted using the Generalized Collective Modes (GCMs) model, involving relaxing modes occurring in the longitudinal component and propagating modes occurring in both components. Three systems are studied as follows: (1) BK3 ambient water, (2) SPC/E ambient water, and (3) BK3 ambient heavy water. Comparison between the results of these systems reveals the influence of the polarizability, or the influence of the molecular mass, on the collective dynamics. Moreover, the GCM fitting allows a quantitative description of the excitation modes in terms of the frequencies, damping coefficients and possible coupling between longitudinal and transverse modes. The differences between the three situations are also clearly evidenced within this formalism.

Leveraging uncertainty in collective opinion dynamics with heterogeneity

Natural and artificial collectives exhibit heterogeneities across different dimensions, contributing to the complexity of their behavior. We investigate the effect of two such heterogeneities on collective opinion dynamics: heterogeneity of the quality of agents’ prior information and of degree centrality in the network. To study these heterogeneities, we introduce uncertainty as an additional dimension to the consensus opinion dynamics model, and consider a spectrum of heterogeneous networks with varying centrality. By quantifying and updating the uncertainty using Bayesian inference, we provide a mechanism for each agent to adaptively weigh their individual against social information. We observe that uncertainties develop throughout the interaction between agents, and capture information on heterogeneities. Therefore, we use uncertainty as an additional observable and show the bidirectional relation between centrality and information quality. In extensive simulations on heterogeneous opinion dynamics with Gaussian uncertainties, we demonstrate that uncertainty-driven adaptive weighting leads to increased accuracy and speed of consensus, especially with increasing heterogeneity. We also show the detrimental effect of overconfident central agents on consensus accuracy which can pose challenges in designing such systems. The opportunities for improved performance and observablility suggest the importance of considering uncertainty both for the study of natural and the design of artificial heterogeneous systems.

Learning Collective Cell Migratory Dynamics from a Static Snapshot with Graph Neural Networks

Multicellular self-assembly into functional structures is a dynamic process that is critical in the development of biological structures and diseases, including embryo development, organ formation, tumor invasion, and other processes. Being able to infer collective cell migratory dynamics from their static configuration is valuable for both understanding and predicting these complex behaviors. However, the identification of structural features that can indicate multicellular motion has been difficult, and existing metrics largely rely on physical instincts. Here we show that, through the use of a graph neural network, the motion of multicellular collectives can be inferred from a static snapshot of cell positions, in both experimental and synthetic datasets. Published by the American Physical Society 2024

Collective dynamics on constrained three-lane exclusion process

Collective dynamics on constrained three-lane exclusion process

Synchronization and chimeras in asymmetrically coupled memristive Tabu learning neuron network

The coupling between neuronal oscillators plays an intriguing role in understanding the dynamics of the biological neurons present in realistic situations. Importantly, when the coupling between these neurons assumes an asymmetric nature, it can lead to profound changes in their overall behavior. In order to explore the impact of asymmetrical coupling on neuron models subjected to magnetic flux induction, we employ a coupled Tabu learning neuron model. Specifically, we illustrate the interplay between flux coupling and asymmetric electrical synapses concerning the control parameters of the proposed system using phase portraits, time series, bifurcation analysis, and Lyapunov spectrum. In particular, we show the dynamics by taking into account asymmetric interactions between neurons, from a simple network of two coupled systems to a network of nodes. Primarily, we demonstrate that two coupled systems exhibit synchronization for a fixed magnitude of control parameter with increasing coupling strength. Furthermore, we discuss the collective dynamics for the distinct network connectivity including regular, small-world and random. For all network connections, an increase in coupling strength facilitates a transition from desynchronization to synchronization via chimera state. We believe that attaining synchronization in Tabu learning neuron can act as a pivotal factor for neuron activity, contributing to the realization of such behavior in the context of numerous cognitive processes.

Phonons reveal coupled cholesterol-lipid dynamics in ternary membranes

Experimental studies of collective dynamics in lipid bilayers have been challenging due to the energy resolution required to observe these low-energy phonon-like modes. However, inelastic X-ray scattering (IXS) measurements — a technique for probing vibrations in soft and biological materials — are now possible with sub-meV resolution, permitting direct observation of low energy, phonon-like modes in lipid membranes. Here, IXS measurements with sub-meV energy resolution reveal a low-energy optic-like phonon mode at roughly 3 meV in the liquid-ordered (Lo) and liquid-disordered (Ld) phases of a ternary lipid mixture. This mode is only observed experimentally at momentum transfers greater than 5 nm−1 in the (Lo) system. A similar gapped mode is also observed in all-atom molecular dynamics (MD) simulations of the same mixture, indicating that the simulations accurately represnt the fast, collective dynamics in the (Lo) phase. It’s optical nature and the Q range of the gap together suggest that the observed mode is due to the coupled motion of cholesterol-lipid pairs, separated by several hydrocarbon chains within the membrane plane. Analysis of the simulations provide molecular insight into the origin of the mode in transient, nanoscale substructures of hexagonally packed hydrocarbon chains. This nanoscale hexagonal packing was previously reported based on molecular dynamics simulations and later by NMR measurements. Here, however, the integration of IXS and MD simulations identifies a new signature of the L° substructure in the collective lipid dynamics, thanks to the recent confluence of IXS sensitivity and MD simulation capabilities.

Coupled dynamics in binary mixtures of model colloidal Yukawa systems.

The dynamical behavior of binary mixtures consisting of highly charged colloidal particles is studied by means of Brownian dynamics simulations. We investigate differently sized, but identically charged particles with nearly identical interactions between all species in highly dilute suspensions. Different short-time self-diffusion coefficients induce, mediated by electrostatic interactions, a coupling of both self and collective dynamics of differently sized particles: the long-time self-diffusion coefficients of a larger species are increased by the presence of a more mobile, smaller species and vice versa. Similar coupling effects are observed in collective dynamics where both time constant and functional form of intermediate scattering functions' initial decay are influenced by the presence of a differently sized species. We provide a systematic analysis of coupling effects in dependence on the ratio of sizes, number densities, and the strength of electrostatic interactions.

Collective dynamics of swarmalators driven by a mobile pacemaker.

Swarmalators, namely, oscillators with intrinsic frequencies that are able to self-propel to move in space, may undergo collective spatial swarming and meanwhile phase synchronous dynamics. In this paper, a swarmalator model driven by an external mobile pacemaker is proposed to explore the swarming dynamics in the presence of the competition between the external organization of the moving pacemaker and the intrinsic self-organization among oscillators. It is unveiled that the swarmalator system may exhibit a wealth of novel spatiotemporal patterns including the spindle state, the ripple state, and the trapping state. Transitions among these patterns and the mechanisms are studied with the help of different order parameters. The phase diagrams present systematic scenarios of various possible collective swarming dynamics and the transitions among them. The present study indicates that one may manipulate the formation and switching of the organized collective states by adjusting the external driving force, which is expected to shed light on applications of swarming performance control in natural and artificial groups of active agents.

Co-evolutionary dynamics for two adaptively coupled Theta neurons.

Natural and technological networks exhibit dynamics that can lead to complex cooperative behaviors, such as synchronization in coupled oscillators and rhythmic activity in neuronal networks. Understanding these collective dynamics is crucial for deciphering a range of phenomena from brain activity to power grid stability. Recent interest in co-evolutionary networks has highlighted the intricate interplay between dynamics on and of the network with mixed time scales. Here, we explore the collective behavior of excitable oscillators in a simple network of two Theta neurons with adaptive coupling without self-interaction. Through a combination of bifurcation analysis and numerical simulations, we seek to understand how the level of adaptivity in the coupling strength, a, influences the dynamics. We first investigate the dynamics possible in the non-adaptive limit; our bifurcation analysis reveals stability regions of quiescence and spiking behaviors, where the spiking frequencies mode-lock in a variety of configurations. Second, as we increase the adaptivity a, we observe a widening of the associated Arnol'd tongues, which may overlap and give room for multi-stable configurations. For larger adaptivity, the mode-locked regions may further undergo a period-doubling cascade into chaos. Our findings contribute to the mathematical theory of adaptive networks and offer insights into the potential mechanisms underlying neuronal communication and synchronization.

Collective buoyancy-driven dynamics in swarming enzymatic nanomotors

Enzymatic nanomotors harvest kinetic energy through the catalysis of chemical fuels. When a drop containing nanomotors is placed in a fuel-rich environment, they assemble into ordered groups and exhibit intriguing collective behaviour akin to the bioconvection of aerobic microorganismal suspensions. This collective behaviour presents numerous advantages compared to individual nanomotors, including expanded coverage and prolonged propulsion duration. However, the physical mechanisms underlying the collective motion have yet to be fully elucidated. Our study investigates the formation of enzymatic swarms using experimental analysis and computational modelling. We show that the directional movement of enzymatic nanomotor swarms is due to their solutal buoyancy. We investigate various factors that impact the movement of nanomotor swarms, such as particle concentration, fuel concentration, fuel viscosity, and vertical confinement. We examine the effects of these factors on swarm self-organization to gain a deeper understanding. In addition, the urease catalysis reaction produces ammonia and carbon dioxide, accelerating the directional movement of active swarms in urea compared with passive ones in the same conditions. The numerical analysis agrees with the experimental findings. Our findings are crucial for the potential biomedical applications of enzymatic nanomotor swarms, ranging from enhanced diffusion in bio-fluids and targeted delivery to cancer therapy.

Differential neural mechanisms underlie cortical gating of visual spatial attention mediated by alpha-band oscillations

Selective attention relies on neural mechanisms that facilitate processing of behaviorally relevant sensory information while suppressing irrelevant information, consistently linked to alpha-band oscillations in human M/EEG studies. We analyzed cortical alpha responses from intracranial electrodes implanted in eight epilepsy patients, who performed a visual spatial attention task. Electrocorticographic data revealed a spatiotemporal dissociation between attention-modulated alpha desynchronization, associated with the enhancement of sensory processing, and alpha synchronization, associated with the suppression of sensory processing, during the cue-target interval. Dorsal intraparietal areas contralateral to the attended hemifield primarily exhibited a delayed and sustained alpha desynchronization, while ventrolateral extrastriatal areas ipsilateral to the attended hemifield primarily exhibited an earlier and sustained alpha synchronization. Analyses of cross-frequency coupling between alpha phase and broadband high-frequency activity (HFA) further revealed cross-frequency interactions along the visual hierarchy contralateral to the attended locations. Directionality analyses indicate that alpha phase in early and extrastriatal visual areas modulated HFA power in downstream visual areas, thus potentially facilitating the feedforward processing of an upcoming, spatially predictable target. In contrast, in areas ipsilateral to the attended locations, HFA power modulated local alpha phase in early and extrastriatal visual areas, with suppressed interareal interactions, potentially attenuating the processing of distractors. Our findings reveal divergent alpha-mediated neural mechanisms underlying target enhancement and distractor suppression during the deployment of spatial attention, reflecting enhanced functional connectivity at attended locations, while suppressed functional connectivity at unattended locations. The collective dynamics of these alpha-mediated neural mechanisms play complementary roles in the efficient gating of sensory information.

Collective quantum dynamics with distant quantum emitters in slow-wave nanoplasmonic waveguides

We consider a slow-wave nanoplasmonic waveguide system with spatially separated (distant) quantum emitters. Based on a nanoplasmonic waveguide quantum electrodynamic theory the emerging non-Markovian collective plasmon-polariton dynamics directly reflects the spatial positioning of the quantum emitters. A phase-space analysis allows us to distinguish between collectivity and cooperativity and the transition between these regimes. For distant emitters, temporal decoherence is reflected in anomalous phase-space evolution. In the spectral domain, collectivity emerges as a resonant single Lorentzian peak with two weak sidebands, while cooperativity manifests as a Fano-like resonance normal-mode splitting. Remarkably, even for distant quantum emitters, we achieve collective multiple quantum emitter dynamics with non-vanishing excitation and vanishing instantaneous emission, establishing an interaction-based quantum nanoplasmonic memory with key relevance in quantum nanoplasmonic networks.

Exploring the soft pinning effect in the dynamics and the structure-dynamics correlation in multicomponent supercooled liquids.

We study multicomponent liquids by increasing the mass of 15% of the particles in a binary Kob-Andersen model. We find that the heavy particles have dual effects on the lighter particles. At higher temperatures, there is a significant decoupling of the dynamics between heavier and lighter particles, with the former resembling a pinned particle to the latter. The dynamics of the lighter particles slow down due to the excluded volume around the nearly immobile heavier particles. Conversely, at lower temperatures, there is a coupling between the dynamics of the heavier and lighter particles. The heavier particles' mass slows down the dynamics of both types of particles. This makes the soft pinning effect of the heavy particles questionable in this regime. We demonstrate that as the mass of the heavy particles increases, the coupling of the dynamics between the lighter and heavier particles weakens. Consequently, the heavier the mass of the heavy particles, the more effectively they act as soft pinning centers in both high and low-temperature regimes. A key finding is that akin to the pinned system, the self-dynamics and collective dynamics of the lighter particles decouple from each other as the mass of the heavy particles has a more pronounced impact on the latter. We analyze the structure-dynamics correlation by considering the system under the binary and modified quaternary framework, the latter describing the pinned system. Our findings indicate that whenever the heavy mass particles function as soft pinning centers, the modified quaternary framework predicts a higher correlation.

Collective behavior of active filaments with homogeneous and heterogeneous stiffness.

The collective dynamics of active biopolymers is crucial for many processes in life, such as cellular motility, intracellular transport, and division. Recent experiments revealed fascinating self-organized patterns of diverse active filaments, while an explicit parameter control strategy remains an open problem. Moreover, theoretical studies so far mostly dealt with active chains with uniform stiffness, which are inadequate in describing the more complicated class of polymers with varying stiffness along the backbone. Here, using Langevin dynamics simulations, we investigate the collective behavior of active chains with homogeneous and heterogeneous stiffness in a comparative manner. We map a detailed non-equilibrium phase diagram in activity and stiffness parameter space. A wide range of phase states, including melt, cluster, spiral, polar, and vortex, are demonstrated. The appropriate parameter combination for large-scale polar and vortex formation is identified. In addition, we find that stiffness heterogeneity can substantially modulate the phase behaviors of the system. It has an evident destructive effect on the long-ranged polar structure but benefits the stability of the vortex pattern. Intriguingly, we unravel a novel polar-vortex transition in both homogeneous and heterogeneous systems, which is closely related to the local alignment mechanism. Overall, we achieve new insights into how the interplay among activity, stiffness, and heterogeneity affects the collective dynamics of active filament systems.

The Countoscope: Measuring Self and Collective Dynamics without Trajectories

Driven by physical questions pertaining to quantifying particle dynamics, microscopy can now resolve complex systems at the single-particle level, from cellular organisms to individual ions. Yet, available analysis techniques face challenges reconstructing trajectories in dense and heterogeneous systems where accurately labeling particles is difficult. Furthermore, the inescapable finite field of view of experiments hinders the measurement of collective effects. Inspired by Smoluchowski, we introduce a broadly applicable analysis technique that probes dynamics of interacting particle suspensions based on a remarkably simple principle: counting particles in finite observation boxes. Using colloidal experiments, advanced simulations, and theory, we first demonstrate that statistical properties of fluctuating counts can be used to determine self-diffusion coefficients, so alleviating the hurdles associated with trajectory reconstruction. We also provide a recipe for practically extracting the diffusion coefficient from experimental data at variable particle densities, which is sensitive to steric and hydrodynamic interactions. Remarkably, by increasing the observation box size, counting naturally enables the study of collective dynamics in dense suspensions. Using our novel analysis of particle counts, we uncover a surprising enhancement of collective behavior due to hydrodynamics as well as a new length scale which can be connected with hyperuniform structure. Our counting framework, the “countoscope,” thus enables efficient measurements of self and collective dynamics in dense suspensions and opens the way to quantifying dynamics and identifying novel physical mechanisms in diverse complex systems where single particles can be resolved. Published by the American Physical Society 2024

The consequences of tritium mix for simulated ion cyclotron emission spectra from deuterium-tritium plasmas

Abstract Measurements of ion cyclotron emission (ICE) are obtained from most large magnetically confined fusion (MCF) plasma experiments, and may be used in future to quantify properties of the fusion-born alpha-particle population in deuterium-tritium (DT) plasmas in ITER. ICE is driven by spatially localised, strongly non-Maxwellian, minority energetic ion populations which relax collectively under the magnetoacoustic cyclotron instability (MCI). ICE spectral peaks are typically observed at, near, or separated by, integer harmonics of the energetic ion cyclotron frequency. Here, for the first time, we study how simulated ICE spectra from DT plasmas vary with tritium concentration and compare with an observed JET DT ICE spectrum. We incorporate a population of thermal tritons, in addition to thermal deuterons and an energetic minority population of fusion-born alpha-particles, in simulations with the kinetic particle-in-cell (PIC) code EPOCH. This code has previously been used extensively for interpretation of ICE observations in terms of the self-consistent gyro-resolved collective Maxwell-Lorentz dynamics of tens of millions of simulation particles. Our simulation parameters are relevant to the ICE-generative outer midplane edge region of JET DT plasma 26148, which included 11% tritium. Quantifying the variation of simulated ICE power spectra with tritium concentration reveals that our simulation with 11% tritium concentration most accurately represents the observed ICE spectrum from this plasma. This outcome is encouraging for the diagnostic application of ICE to fusion plasmas containing two thermal ion species, including future DT plasmas.

Analyzing narrative contagion through digital storytelling in social media conversations: An AI-powered computational approach

Despite the growing popularity of digital narratives, research on digital storytelling and its spread through social media interactions remains limited. Inspired by the social contagion theory, we introduce the concept of narrative contagion—where a story shared by a person or organization prompts others to share their stories—and investigate its process and outcome in online cancer communities. Utilizing a large dataset of 849 Facebook posts, 47,291 comments, and 14,466 replies, our artificial intelligence (AI)-based computational analysis provides evidence for narrative contagion among individual users in organization-hosted cancer communities on Facebook. It also reveals different organizational message characteristics, such as emotional arousal, post topic, and request for storytelling, that affect user storytelling and emotional support in comments and replies. Our results contribute to a deeper understanding of digital storytelling and its collective dynamics in online communities.

Enhanced Diffusion and Non-Gaussian Displacements of Colloids in Quasi-2D Suspensions of Motile Bacteria

In the real world, active agents interact with surrounding passive objects, thus introducing additional degrees of complexity. The relative contributions of far-field hydrodynamic and near-field contact interactions to the anomalous diffusion of passive particles in suspensions of active swimmers remain a subject of ongoing debate. We constructed a quasi-two-dimensional microswimmer–colloid mixed system by taking advantage of Serratia marcescens’ tendency to become trapped at the air–water interface to investigate the origins of the enhanced diffusion and non-Gaussianity of the displacement distributions of passive colloidal tracers. Our findings reveal that the diffusion behavior of colloidal particles exhibits a strong dependence on bacterial density. At moderate densities, the collective dynamics of bacteria dominate the diffusion of tracer particles. In dilute bacterial suspensions, although there are multiple dynamic types present, near-field contact interactions such as collisions play a major role in the enhancement of colloidal transport and the emergence of non-Gaussian displacement distributions characterized by heavy exponential tails in short times. Despite the distinct types of microorganisms and their diverse self-propulsion mechanisms, a generality in the diffusion behavior of passive colloids and their underlying dynamics is observed.

Thermodynamic inference of correlations in nonequilibrium collective dynamics

The theory of stochastic thermodynamics has revealed many useful fluctuation relations, with the thermodynamic uncertainty relation (TUR) being a theorem of major interest. When many nonequilibrium currents interact with each other, a naive application of the TUR to an individual current can result in an apparent violation of the TUR bound. Here, we explore how such an apparent violation can be used to put a lower bound on the strength of correlations C as well as the number N of interacting currents in collective dynamics. This lower bound is a combined bound on C(N−1) if only one current is measured, or a bound on N if two currents are measured. Our proposed protocol allows for the inference of hidden correlations in experiment, for example when a team of molecular motors pulls on the same cargo but only one or a subset of them is fluorescently tagged. By solving analytically and numerically several models of many-body nonequilibrium dynamics, we ascertain under which conditions this strategy can be applied and the inferred bound on correlations becomes tight. Published by the American Physical Society 2024

Fast Collective Hydrogen-Bond Dynamics in HexafluoroisopropanolRelated to its Chemical Activity.

Using fluorinated mono-alcohols, in particular hexafluoro-isopropanol (HFIP), as a solvent can enhance chemical reaction rates in a spectacular manner. Previous work has shown evidence that this enhancement is related to the hydrogen-bond structure of these liquids. Here, we investigate the hydrogen-bond dynamics of HFIP and compare it to thatof its non-fluorinated analog, isopropanol. Ultrafast infrared spectroscopy show that the dynamics of individual hydrogen-bonds is about twice as slow in HFIP as in isopropanol. Surprisingly, from dielectric spectroscopy we find the opposite behavior for the dynamics of hydrogen-bonded clusters: collective rearrangements are3 times faster in HFIP than in isopropanol. This difference indicates that the hydrogen-bonded clusters in HFIP are smaller than in isopropanol. The differences in cluster size can be traced to changes in the hydrogen-bond donor and acceptor strengths upon fluorination. The smaller cluster size canboostreaction rates in HFIP by increasing the concentration of reactive, terminal OH-groups of the clusters, whereas the fast collective dynamics can increase the rate of formation of hydrogen bonds with the reactants. The longer lifetime of the individual hydrogen bonds in HFIP can enhance the stability of the hydrogen-bonded clusters, and so increase the probability of reactant-solvent hydrogen bonding.