arXiv subjects

Zhonghuai Hou

Publications and source records attributed to Zhonghuai Hou.

At least 37 records · Page 2Linked to original sources

arXiv2020

Design principles for biochemical oscillations with limited energy resources

As biochemical systems may frequently suffer from limited energy resources so that internal molecular fluctuation has to be utilized to induce random rhythm, it is still a great theoretical challenge to understand the elementary principles for biochemical systems with limited energy resources to maintain phase accuracy and phase sensitivity. Here, we address the issue by deriving the energy accuracy and the sensitivity-accuracy trade-off relations for a general biochemical model, analytically and numerically. We find that, biochemical systems consume much lower energy cost by noise-induced oscillations to keep almost equal efficiency to maintain precise processes than that by normal oscillations, elucidating clearly the survival mechanism when energy resources are limited. Moreover, an optimal system size is predicted where both the highest sensitivity and accuracy can be reached at the same time, providing a new strategy for the design of biological networks with limited energy sources.

physics.chem-ph↗

arXiv2019

Configuration Dynamics of a Flexible Polymer Chain in a Bath of Chiral Active Particles

We investigate configuration dynamics of a flexible polymer chain in a bath of active particles with dynamic chirality, i.e., particles rotate with a deterministic angular velocity $ω$ besides self-propulsion,by Langevin dynamics simulations in two dimensional space. Particular attentions are paid to how the gyration radius $R_{g}$ changes with the propulsion velocity $v_{0}$,angular velocity $ω$ and chain length. We find that in a chiral bath with a typical nonzero $ω$, the chain first collapses into a small compact cluster and swells again with increasing $v_{0}$, in quite contrast to the case for a normal achiral bath $(ω=0)$ wherein a flexible chain swells with increasing $v_{0}$. More interestingly, the polymer can even form a closed ring if the chain length is large enough,which may oscillate with the cluster if $v_{0}$ is large. Consequently, the gyration radius $R_{g}$ shows nontrivial non-monotonic dependences on $v_{0}$, i.e., it undergoes a minimum for relatively short chains, and two minima with a maximum in between for longer chains. Our analysis shows that such interesting phenomena are mainly due to the competition between two roles played by the chiral active bath: while the persistence motion due to particle activity tends to stretch the chain, the circular motion of the particle may lead to an effective osmotic pressure that tends to collapse the chain. In addition, the size of the circular motion $R_{0}=v_{0}/ω$ shows an important role in that the compact clusters and closed-rings are both observed at nearly the same values of $R_{0}$ for different $ω$.

cond-mat.soft↗

arXiv2019

Tunable Sorting of Mesoscopic Chiral Structures by External Noise in Achiral Periodic Potentials

Efficient chirality sorting is now highly demanded to separate assembled mesoscopic chiral structures which are of very special physical properties rather than their achiral counterparts or those at the single-particle level. However, the efficiency of conventional methods usually suffers from the thermal or external noise. Here, we propose a mechanism utilizing external noise to attain a tunable sorting of mesoscopic chiral particles in an achiral periodic potential. The complete chirality-separation stems from the path selection by a noise-induced biased flux in a nonequilibrium landscape. Such mechanism provides a practicable way to control the motion of chiral particles by simply adjusting the noise intensity, which is demonstrated by simultaneous separation of several kinds of enantiomorphs with different degrees of chirality. The robustness and generalizability of noise-tuned chirality sorting is further verified in systems with other types of periodic potentials or spatially/temporally correlated noise.

cond-mat.soft↗

arXiv2019

Self-assembly of active core corona particles into highly ordered and self-healing structures

Formation of highly ordered structures usually needs to overcome a high free-energy barrier that is greatly beyond the ability of thermodynamic fluctuation, such that the system would be easily trapped into a state with many defects and the annealing process of which often occurs on unreachable long time-scales. Here we report a fascinating example theoretically that active core corona particles can successfully self-assemble into a large-scaled and highly ordered stripe or trimer lattice, which is hardly achieved in a non-driven equilibrium system. Besides, such an activity-induced ordered structure shows an interesting self-healing feature of defects. In addition, there exists an optimal level of activity that most favorably enhance the formation of ordered self-assembly structures. Since core corona particles act as important units for self-assembly in real practice, we believe our study opens a new design-strategy for highly ordered materials.

cond-mat.mtrl-sci↗

arXiv2019

Disordered hyperuniform obstacles enhance sorting of dynamically chiral microswimmers

Disordered hyperuniformity, a brand new type of arrangements with novel physical properties, provides various practical applications in extensive fields. To highlight the great potential of applying disordered hyperuniformity to active systems, a practical example is reported here by an optimal sorting of dynamically chiral microswimmers in disordered hyperuniform obstacle environments in comparison with regular or disordered ones. This optimal chirality sorting stems from a competition between advantageous microswimmer-obstacle collisions and disadvantageous trapping of microswimmers by obstacles. Based on this mechanism, optimal chirality sorting is also realized by tuning other parameters including the number density of obstacles, the strength of driven force and the noise intensity. Our findings may open a new perspective on both theoretical and experimental investigations for further applications of disordered hyperuniformity in active systems.

cond-mat.soft↗

arXiv2018

Study of Active Brownian Particle Diffusion in Polymer Solutions

The diffusion behavior of an active Brownian particle (ABP) in polymer solutions is studied using Langevin dynamics simulations. We find that the long time diffusion coefficient $D$ can show a non-monotonic dependence on the particle size $R$ if the active force $F_{a}$ is large enough, wherein a bigger particle would diffuse faster than a smaller one which is quite counterintuitive. By analyzing the short time dynamics in comparison to the passive one, we find that such non-trivial dependence results from the competition between persistence motion of the ABP and the length-scale dependent effective viscosity that the particle experienced in the polymer solution. \textcolor{black}{We have also introduced an effective viscosity $η_{\text{eff}}$ experienced by the ABP phenomenologically. Such an active $η_{\text{eff}}$ is found to be larger than a passive one and strongly depends on $R$ and $F_{a}$}\textcolor{magenta}{.} In addition, we find that the dependence of $D$ on propelling force $F_{a}$ presents a well scaling form at a fixed $R$ and the scaling factor changes non-monotonically with $R$. Such results demonstrate that active issue plays rather subtle roles on the diffusion of nano-particle in complex solutions.

cond-mat.soft↗

arXiv2017

Optimal Allocation of Resources for Suppressing Epidemic Spreading on Networks

Efficient allocation of limited medical resources is crucial for controlling epidemic spreading on networks. Based on the susceptible-infected-susceptible model, we solve an optimization problem as how best to allocate the limited resources so as to minimize the prevalence, providing that the curing rate of each node is positively correlated to its medical resource. By quenched mean-field theory and heterogeneous mean-field (HMF) theory, we prove that epidemic outbreak will be suppressed to the greatest extent if the curing rate of each node is directly proportional to its degree, under which the effective infection rate $λ$ has a maximal threshold $λ_c^{opt}=1/\left\langle k \right\rangle$ where $\left\langle k \right\rangle$ is average degree of the underlying network. For weak infection region ($λ\gtrsimλ_c^{opt}$), we combine a perturbation theory with Lagrange multiplier method (LMM) to derive the analytical expression of optimal allocation of the curing rates and the corresponding minimized prevalence. For general infection region ($λ>λ_c^{opt}$), the high-dimensional optimization problem is converted into numerically solving low-dimensional nonlinear equations by the HMF theory and LMM. Counterintuitively, in the strong infection region the low-degree nodes should be allocated more medical resources than the high-degree nodes to minimize the prevalence. Finally, we use simulated annealing to validate the theoretical results.

q-bio.PE↗

arXiv2017

Mode Coupling Theory for Nonequilibrium Glassy Dynamics of Thermal Self-Propelled Particles

We present a promising mode coupling theory study for the relaxation and glassy dynamics of a system of strongly interacting self-propelled particles, wherein the self-propulsion force is described by Ornstein-Uhlenbeck colored noise and thermal noises are included. Our starting point is an effective Smoluchowski equation governing the distribution function of particle's positions, from which we derive a memory function equation for the time dependence of density fluctuations in nonequilibrium steady states. With the basic assumption of absence of macroscopic currents and standard mode coupling approximation, we can obtain expressions for the irreducible memory function and other relevant dynamic terms. With these equations obtained, we study the glassy dynamics of this thermal self-propelled particles system by investigating the Debye-Waller factor f_{q} and relaxation time τ_α as functions of the persistence time τ_{p} of self-propulsion, the single particle effective temperature T_{\text{eff}} as well as the number density ρ. Consequently, we find the critical density ρ_{c} for given τ_{p} shifts to larger values with increasing magnitude of propulsion force or effective temperature, in good accordance with previous reported simulation works. In addition, the theory facilitates us to study the critical effective temperature T_{\text{eff}}^{c} for fixed ρas well as its dependence on τ_{p}. We find that T_{\text{eff}}^{c} increases with τ_{p} and in the limit τ_{p}\to0, it approaches the value for a simple passive Brownian system as expected. Our theory also well recovers the results for passive systems and can be easily extended to more complex systems such as active-passive mixtures.

cond-mat.soft↗

arXiv2017

Emergence of collective dynamical chirality for achiral active particles

Emergence of collective dynamical chirality (CDC) at mesoscopic scales plays a key role in many formation processes of chiral structures in nature, which may also provide possible routines for people to fabricate complex chiral architectures. So far, most of reported CDCs are found in systems of active objects with individual structure chirality or/and dynamical chirality, and whether CDC can arise from simple and achiral units is still an attractive mystery. Here, we report a spontaneous formation of CDC in a system of both dynamically and structurally achiral particles motivated by active motion of cells. Active moving, confinement and hydrodynamic interaction are found to be the three key factors. Detailed analysis shows that the system can support abundant collective dynamical behaviors, including rotating droplet, rotating bubble, CDC oscillation, array of collective rotation, as well as interesting transitions such as chirality transition, structure transition and state reentrance.

cond-mat.soft↗

arXiv2016

Critical noise of majority-vote model on complex networks

The majority-vote model with noise is one of the simplest nonequilibrium statistical model that has been extensively studied in the context of complex networks. However, the relationship between the critical noise where the order-disorder phase transition takes place and the topology of the underlying networks is still lacking. In the paper, we use the heterogeneous mean-field theory to derive the rate equation for governing the model's dynamics that can analytically determine the critical noise $f_c$ in the limit of infinite network size $N\rightarrow \infty$. The result shows that $f_c$ depends on the ratio of ${\left\langle k \right\rangle }$ to ${\left\langle k^{3/2} \right\rangle }$, where ${\left\langle k \right\rangle }$ and ${\left\langle k^{3/2} \right\rangle }$ are the average degree and the $3/2$ order moment of degree distribution, respectively. Furthermore, we consider the finite size effect where the stochastic fluctuation should be involved. To the end, we derive the Langevin equation and obtain the potential of the corresponding Fokker-Planck equation. This allows us to calculate the effective critical noise $f_c(N)$ at which the susceptibility is maximal in finite size networks. We find that the $f_c-f_c(N)$ decays with $N$ in a power-law way and vanishes for $N\rightarrow \infty$. All the theoretical results are confirmed by performing the extensive Monte Carlo simulations in random $k$-regular networks, Erdös-Rényi random networks and scale-free networks.

physics.soc-ph↗

arXiv2016

Explosive Phase Transition in a Majority-Vote Model with Inertia

We generalize the original majority-vote model by incorporating an inertia into the microscopic dynamics of the spin flipping, where the spin-flip probability of any individual depends not only on the states of its neighbors, but also on its own state. Surprisingly, the order-disorder phase transition is changed from a usual continuous type to a discontinuous or an explosive one when the inertia is above an appropriate level. A central feature of such an explosive transition is a strong hysteresis behavior as noise intensity goes forward and backward. Within the hysteresis region, a disordered phase and two symmetric ordered phases are coexisting and transition rates between these phases are numerically calculated by a rare-event sampling method. A mean-field theory is developed to analytically reveal the property of this phase transition.

physics.soc-ph↗

arXiv2016

A hybrid multiscale coarse-grained method for dynamics on complex networks

Brute-force simulations for dynamics on very large networks are quite expensive. While phenomenological treatments may capture some macroscopic properties, they often ignore important microscopic details. Fortunately, one may be only interested in the property of local part and not in the whole network. Here, we propose a hybrid multiscale coarse-grained(HMCG) method which combines a fine Monte Carlo(MC) simulation on the part of nodes of interest with a more coarse Langevin dynamics on the rest part. We demonstrate the validity of our method by analyzing the equilibrium Ising model and the nonequilibrium susceptible-infected-susceptible model. It is found that HMCG not only works very well in reproducing the phase transitions and critical phenomena of the microscopic models, but also accelerates the evaluation of dynamics with significant computational savings compared to microscopic MC simulations directly for the whole networks. The proposed method is general and can be applied to a wide variety of networked systems just adopting appropriate microscopic simulation methods and coarse graining approaches.

physics.soc-ph↗

arXiv2015

Large-scale Epitaxial Growth Kinetics of Graphene: A Kinetic Monte Carlo Study

Epitaxial growth via chemical vapor deposition is considered to be the most promising way towards synthesizing large area graphene with high quality. However, it remains a big theoretical challenge to reveal growth kinetics with atomically energetic and large-scale spatial information included. Here, we propose a minimal kinetic Monte Carlo model to address such an issue on an active catalyst surface with graphene/substrate lattice mismatch, which facilitates us to perform large scale simulations of the growth kinetics over two dimensional surface with growth fronts of complex shapes. A geometry-determined large-scale growth mechanism is revealed, where the rate-dominating event is found to be $C_{1}$-attachment for concave growth front segments and $C_{5}$-attachment for others. This growth mechanism leads to an interesting time-resolved growth behavior which is well consistent with that observed in a recent scanning tunneling microscopy experiment.

physics.chem-ph↗

arXiv2015

Heterogeneous nucleation on complex networks with mobile impurities

We study the heterogeneous nucleation of Ising model on complex networks under a non-equilibrium situation where the impurities perform degree-biased motion controlled by a parameter α. Through the forward flux sampling and detailed analysis on the nucleating clusters, we find that the nucleation rate shows a nonmonotonic dependence on αfor small number of impurities, in which a maximal nucleation rate occurs at α=0 corresponding to the degree-uncorrelated random motion. Furthermore, we demonstrate the distinct features of the nucleating clusters along the pathway for different preference of impurities motion, which may be used to understand the resonance-like dependence of nucleation rate on the motion bias of impurities. Our theoretical analysis shows that the nonequilibrium diffusion of impurities can always induce a positive energy flux that can facilitate the barrier-crossing nucleation process. The nonmonotonic feature of the average value of the energy flux with αmay be the origin of our simulation results.

physics.soc-ph↗

arXiv2015

Nonequilibrium Glass Transition in Mixtures of Active-Passive Particles

We develop a mode coupling theory(MCT) to study the nonequilibrium glass transition behavior of a mono-disperse mixture of active-passive hard-sphere particles. The MCT equations clearly demonstrate that the glass transition is shifted to higher values of total volume fraction when doping a passive system with active particles. Interestingly, we find that the glass transition point may show a non-monotonic dependence on the effective diffusivity of the active component, indicating a nontrivial type of activity induced reentrance behavior. Analysis based on the nonergodic parameters suggest that the glassy state at small activity is due to the caging effect, while that at high activity could result from activity induced dynamic clustering.

cond-mat.soft↗

arXiv2014

Complex activated transition in a system of two coupled bistable oscillators

We study the fluctuation-activated transition process in a system of two coupled bistable oscillators, in which each oscillator is driven by one constant force and an independent Gaussian white noise. The transition pathway has been identified and the transition rate has been computed as the coupling strength $μ$ and the mismatch $σ$ in the force constants are varied. For identical oscillators ($σ=0$), the transition undergoes a change from a two-step process with two candidate pathways to a one-step process with also two candidate pathways to a one-step process with a single pathway as $μ$ is increased. For nonidentical oscillators ($σ\neq0$), a novel transition emerges that is a mixture of a two-step pathway and a one-step pathway. Interestingly, we find that the total transition rate depends nonmonotonically on $μ$: a maximal rate appears in an intermediate magnitude of $μ$. Moreover, in the presence of weak coupling the rate also exhibits an unexpected maximum as a function of $σ$. The results are in an excellent agreement with our numerical simulations by forward flux sampling.

cond-mat.stat-mech↗

arXiv2013

An Efficient Self-optimized Sampling Method for Rare Events in Nonequilibrium Systems

Rare events such as nucleation processes are of ubiquitous importance in real systems. The most popular method for nonequilibrium systems, forward flux sampling (FFS), samples rare events by using interfaces to partition the whole transition process into sequence of steps along an order parameter connecting the initial and final states. FFS usually suffers from two main difficulties: low computational efficiency due to bad interface locations and even being not applicable when trapping into unknown intermediate metastable states. In the present work, we propose an approach to overcome these difficulties, by self-adaptively locating the interfaces on the fly in an optimized manner. Contrary to the conventional FFS which set the interfaces with euqal distance of the order parameter, our approach determines the interfaces with equal transition probability which is shown to satisfy the optimization condition. This is done by firstly running long local trajectories starting from the current interface $ł_i$ to get the conditional probability distribution $P_c$, and then determining $ł_{i+1}$ by equalling $P_c$ to a give value $p_0$. With these optimized interfaces, FFS can be run in a much efficient way. In addition, our approach can conveniently find the intermediate metastable states by monitoring some special long trajectories that nither end at the initial state nor reach the next interface, the number of which will increase sharply from zero if such metastable states are encountered. We apply our approach to a model two-state system and a two-dimensional lattice gas Ising model. Our approach is shown to be much more efficient than the conventional FFS method without losing accuracy, and it can also well reproduce the two-step nucleation scenario of the Ising model with easy identification of the intermidiate metastable state.

stat.ME↗

arXiv2013

Orientation Sensitive Nonlinear Growth of Graphene: A Geometry-determined Epitaxial Growth Mechanism

Although the corresponding carbon-metal interactions can be very different, a similar nonlinear growth behavior of graphene has been observed for different metal substrates. To understand this interesting experimental observation, a multiscale $\lq\lq$standing-on-the-front" kinetic Monte Carlo study is performed. An extraordinary robust geometry effect is identified, which solely determines the growth kinetics and makes the details of carbon-metal interaction not relevant at all. Based on such a geometry-determined mechanism, epitaxial growth behavior of graphene can be easily predicted in many cases. As an example, an orientation-sensitive growth kinetics of graphene on Ir(111) surface has been studied. Our results demonstrate that lattice mismatch pattern at the atomic level plays an important role for macroscopic epitaxial growth.

cond-mat.mtrl-sci↗