391 research outputs found
Delay induced bifurcation of dominant transition pathways
We investigate delay effects on dominant transition pathways (DTP) between
metastable states of stochastic systems. A modified version of the Maier-Stein
model with linear delayed feedback is considered as an example. By a stability
analysis of the {"on-axis"} DTP in trajectory space, we find that a bifurcation
of DTPs will be induced when time delay is large enough. This finding is
soon verified by numerically derived DTPs which are calculated by employing a
recently developed minimum action method extended to delayed stochastic
systems. Further simulation shows that, the delay-induced bifurcation of DTPs
also results in a nontrivial dependence of the transition rate constant on the
delay time. Finally, the bifurcation diagram is given on the
plane, where measures the non-conservation of the original Maier-Stein
model.Comment: 14 pages, 6 figure
Flexibility Induced Motion Transition of Active Filament: Rotation without Long-range Hydrodynamic Interaction
We investigate the motion of active semiflexible filament with shape
kinematics and hydrodynamic interaction including. Three types of filament
motion are found: Translation, snaking and rotation. Change of flexibility will
induce instability of shape kinematics and further result in asymmetry of shape
kinematics respect to the motion of mass center, which are responsible to a
continuous-like transition from translation to snaking and a first-order-like
transition from snaking to rotation, respectively. Of particular interest, we
find that long-range hydrodynamic interaction is not necessary for filament
rotation, but can enhance remarkably the parameter region for its appearance.
This finding may provide an evidence that the experimentally found collective
rotation of active filaments is more likely to arise from the individual
property even without the long-range hydrodynamic interaction.Comment: 5 pages, 4figure
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 -attachment for concave growth
front segments and -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.Comment: 5 pages, 3 figure
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 \l_i to get the conditional probability distribution ,
and then determining \l_{i+1} by equalling to a give value . 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.Comment: 6 pages, 6 figure
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.Comment: 7 figure
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.Comment: 6 pages, 5 figure
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.Comment: 11 pages, 2 figure
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 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.Comment: 5 pages, 3 figures, 2 table
Atomistic Mechanisms of Nonlinear Graphene Growth on Ir Surface
As a two-dimensional material, graphene can be naturally obtained via
epitaxial growth on a suitable substrate. Growth condition optimization usually
requires an atomistic level understanding of the growth mechanism. In this
article, we perform a mechanistic study about graphene growth on Ir(111)
surface by combining first principles calculations and kinetic Monte Carlo
(kMC) simulations. Small carbon clusters on the Ir surface are checked first.
On terraces, arching chain configurations are favorable in energy and they are
also of relatively high mobilities. At steps, some magic two-dimensional
compact structures are identified, which show clear relevance to the nucleation
process. Attachment of carbon species to a graphene edge is then studied. Due
to the effect of substrate, at some edge sites, atomic carbon attachment
becomes thermodynamically unfavorable. Graphene growth at these difficult sites
has to proceed via cluster attachment, which is the growth rate determining
step. Based on such an inhomogeneous growth picture, kMC simulations are made
possible by successfully separating different timescales, and they well
reproduce the experimentally observed nonlinear kinetics. Different growth
rates and nonlinear behaviors are predicted for different graphene
orientations, which is consistent with available experimental results.
Importantly, as a phenomenon originated from lattice mismatch, inhomogeneity
revealed in this case is expected to be quite universal and it should also make
important roles in many other hetero-epitaxial systems
Inertial Effects on Kinetics of Motility-Induced Phase Separation
Motility-induced phase separation (MIPS) is of great importance and has been
extensively researched in overdamped systems, nevertheless, what impacts
inertia will bring on kinetics of MIPS is lack of investigation. Here, we find
that, not only the phase transition changes from continuous to discontinuous,
but also the formation of clusters exhibits a nucleation-like process without
any coarsening regime, different from spinodal decomposition in the overdamped
case. This remarkable kinetics stems from a competition between
activity-induced accumulation of particles and inertia-induced suppression of
clustering process. More interestingly, the discontinuity of MIPS still exists
even when the ratio of particle mass to the friction coefficient reduces to be
very small such as 0.0001. Our findings emphasize the importance of inertia in
kinetics of MIPS, and may open a new perspective on understanding the nature of
MIPS in active systems.Comment: 5 pages, 4 figure
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