The Localization Transition of the Two-Dimensional Lorentz Model
ABSTRACT We investigate the dynamics of a single tracer particle performing Brownian motion in a two-dimensional course of randomly distributed hard obstacles. At a certain critical obstacle density, the motion of the tracer becomes anomalous over many decades in time, which is rationalized in terms of an underlying percolation transition of the void space. In the vicinity of this critical density the dynamics follows the anomalous one up to a crossover time scale where the motion becomes either diffusive or localized. We analyze the scaling behavior of the time-dependent diffusion coefficient D(t) including corrections to scaling. Away from the critical density, D(t) exhibits universal hydrodynamic long-time tails both in the diffusive as well as in the localized phase. Comment: 13 pages, 7 figures.
- [Show abstract] [Hide abstract]
ABSTRACT: A ubiquitous observation in cell biology is that the diffusive motion of macromolecules and organelles is anomalous, and a description simply based on the conventional diffusion equation with diffusion constants measured in dilute solution fails. This is commonly attributed to macromolecular crowding in the interior of cells and in cellular membranes, summarizing their densely packed and heterogeneous structures. The most familiar phenomenon is a sublinear, power-law increase of the mean-square displacement (MSD) as a function of the lag time, but there are other manifestations like strongly reduced and time-dependent diffusion coefficients, persistent correlations in time, non-Gaussian distributions of spatial displacements, heterogeneous diffusion and a fraction of immobile particles. After a general introduction to the statistical description of slow, anomalous transport, we summarize some widely used theoretical models: Gaussian models like fractional Brownian motion and Langevin equations for visco-elastic media, the continuous-time random walk model, and the Lorentz model describing obstructed transport in a heterogeneous environment. Particular emphasis is put on the spatio-temporal properties of the transport in terms of two-point correlation functions, dynamic scaling behaviour, and how the models are distinguished by their propagators even if the MSDs are identical. Then, we review the theory underlying commonly applied experimental techniques in the presence of anomalous transport like single-particle tracking, fluorescence correlation spectroscopy (FCS) and fluorescence recovery after photobleaching (FRAP). We report on the large body of recent experimental evidence for anomalous transport in crowded biological media: in cyto- and nucleoplasm as well as in cellular membranes, complemented by in vitro experiments where a variety of model systems mimic physiological crowding conditions. Finally, computer simulations are discussed which play an important role in testing the theoretical models and corroborating the experimental findings. The review is completed by a synthesis of the theoretical and experimental progress identifying open questions for future investigation.Reports on Progress in Physics 03/2013; 76(4):046602. · 13.23 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: ... JP Wittmer ( ) · JE Zabel · P. Polinska · N. Schulmann · H. Meyer · J . Farago · A. Johner · J . Baschnagel Institut Charles Sadron, Université de Strasbourg, CNRS, 23 rue du Loess, 67037 Strasbourg Cedex, France e-mail: joachim. wittmer @ics-cnrs.unistra.fr ... JP Wittmer et al. ...Journal of Statistical Physics 11/2011; 145(4):1017-1126. · 1.40 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: The dynamics of two-dimensional fluids confined within a random matrix of obstacles is investigated using both colloidal model experiments and molecular dynamics simulations. By varying fluid and matrix area fractions in the experiment, we find delocalized tracer particle dynamics at small matrix area fractions and localized motion of the tracers at high matrix area fractions. In the delocalized region, the dynamics is subdiffusive at intermediate times, and diffusive at long times, while in the localized regime, trapping in finite pockets of the matrix is observed. These observations are found to agree with the simulation of an ideal gas confined in a weakly correlated matrix. Our results show that Lorentz gas systems with soft interactions are exhibiting a smoothening of the critical dynamics and consequently a rounded delocalization-to-localization transition.Physical Review Letters 09/2013; 111(12):128301. · 7.73 Impact Factor
arXiv:1003.2918v1 [cond-mat.soft] 15 Mar 2010
EPJ manuscript No.
(will be inserted by the editor)
The Localization Transition of the Two-Dimensional
Teresa Bauer1, Felix H¨ ofling2,3, Tobias Munk1, Erwin Frey1, and Thomas Franosch4,1
1Arnold Sommerfeld Center for Theoretical Physics (ASC) and Center for NanoScience (CeNS),
Fakult¨ at f¨ ur Physik, Ludwig-Maximilians-Universit¨ at M¨ unchen, Theresienstraße 37, 80333 M¨ unchen,
2Rudolf Peierls Centre for Theoretical Physics, 1 Keble Road, Oxford OX1 3NP, England, United
3Max Planck Institute for Metals Research, Heisenbergstraße 3, 70569 Stuttgart, Germany
4Institut f¨ ur Theoretische Physik, Universit¨ at Erlangen-N¨ urnberg, Staudtstraße 7, 91058 Erlangen,
Abstract. We investigate the dynamics of a single tracer particle performing
Brownian motion in a two-dimensional course of randomly distributed hard ob-
stacles. At a certain critical obstacle density, the motion of the tracer becomes
anomalous over many decades in time, which is rationalized in terms of an under-
lying percolation transition of the void space. In the vicinity of this critical density
the dynamics follows the anomalous one up to a crossover time scale where the
motion becomes either diffusive or localized. We analyze the scaling behavior
of the time-dependent diffusion coefficient D(t) including corrections to scaling.
Away from the critical density, D(t) exhibits universal hydrodynamic long-time
tails both in the diffusive as well as in the localized phase.
Understanding transport in heterogeneous media is fundamental for a variety of applications
ranging from material sciences, porous catalysts, oil recovery, and even biological system. Often,
the motion of particles inside such materials is strongly hindered due to the presence of slowly
rearranging or immobilized obstacles of many different length scales. Many heterogeneous ma-
terials, e.g. rocks, soils, cements, foams and ceramics, consist of solid frames permeated by a
network of pores [1, 2], and a mobile agent can meander through this static course of obstacles
and display long-range transport. Likewise, transport in densely packed systems is strongly ob-
structed by the presence of surrounding particles via their excluded volume effect. In many cases
a separation of time scales naturally occurs, for example in strongly heterogeneous mixtures
such as sodium ions in silicates [3–5] or size-disparate soft or Yukawa spheres [6–8], leading to
a much slower diffusion of one component. Similarly, the dense packing of differently sized pro-
teins, lipids and sugars in the cell cytoplasm leads to strongly suppressed transport known as
molecular crowding [9–12]. Again the motion of a smaller sized molecule is much faster than of
surrounding macromolecules and the small molecule explores a quasi-static array of obstacles.
Molecular crowding is also relevant in quasi two-dimensional systems such as protein diffu-
sion in lipid bilayers as studied by single molecule fluorescence microscopy  or fluorescence
correlation spectroscopy [14–16].
The motion of a tracer in these materials often displays anomalous transport as manifested
in a subdiffusive increase of the mean-square displacement (MSD). This behavior is displayed
in a finite window of time and a crossover to ordinary diffusion occurs at sufficiently long times.
2Will be inserted by the editor
The exponent characterizing the subdiffusive behavior often appears to depend on the details
of the system and even changes as the experimental parameters are varied.
The Lorentz model consisting of a single tracer exploring a course of randomly distributed
frozen obstacles constitutes a minimal model for transport in heterogeneous media . In its
simplest variant the obstacles are spheres or disks and the positions are Poisson distributed.
Hence, they may overlap and form clusters which restrict the motion of a pointlike tracer
particle to the remaining void space. Above a certain obstacle density, the void space no longer
permeates the entire system and a percolation transition occurs . It has been shown that
this transition is accompanied with a divergence of a characteristic length scale ξ, known as
correlation length. The dynamics close to the transition displays critical behavior and the mean-
square displacement exhibits behavior similar to experimental observations for heterogeneous
media. It has been shown that the Lorentz model generically leads to large crossover windows
explaining the apparent drift in characteristic exponents [19–21]. Recently, the two-dimensional
Lorentz model has been introduced in the context of lateral diffusion of proteins in the plasma
Systematic studies on the two-dimensional Lorentz model were mostly restricted to low
densities focusing on the algebraic decay of the velocity autocorrelation functions (VACF) and
the non-analytic dependence of the diffusion coefficient on the obstacle density [25–30]. The
recurrent collisions with the obstacles lead to infinite memory resulting in a negative long-time
tail ∼ −t−2of the VACF [17, 31, 32]; yet close to the transition, the critical behavior shifts
the onset of the hydrodynamic tail to longer and longer times . The critical behavior of
the Lorentz model in two-dimensions is expected to be qualitatively different from the three-
dimensional case since in the latter the conductances through narrow channels determines the
dynamic exponent from pure geometric reasons [34, 35]. In the former the narrow gaps are
expected to be less relevant and the universality of transport on a percolating lattice should
be recovered. Beyond universality one would like to know the range of validity of the universal
behavior, the size of the crossover region, and the importance of corrections to scaling.
In this work we present simulation results for the two-dimensional Lorentz model for Brown-
ian tracer particles, in particular for densities close to the percolation transition. We have mea-
sured the mean-square displacement, the time-dependent diffusion coefficient, and the VACF,
and analyze their respective critical behavior. Then we compare the subdiffusive behavior as
well as the diffusion coefficient with the predicted power-law behavior. The non-algebraic de-
cay of the VACF at long times emerges also for the case of a Brownian tracer corroborating
the notion that the frozen configuration space alone gives rise to persistent correlations in the
dynamics. A scaling theory that includes the leading corrections to scaling is developed and
tested against the simulation data by suitable rectification plots.
2 Lorentz Model
The Lorentz model constitutes the minimal model for particle transport through a disordered
material. In its simplest variant, a single classical tracer particle traverses a d-dimensional array
of frozen hard-core obstacles of density n. Each obstacle acts as a scattering center of radius σ
restricting the motion of the tracer to the void space. For independently distributed scatterers
the only control parameter characterizingthe structure is then the dimensionless number density
n∗= nσd. Equivalently, one may employ the porosity ϕ, i.e., the volume fraction accessible to
the tracer due to the possibly overlapping obstacles. In the planar problem (d = 2) which we
address in this work, one easily calculates
ϕ = exp(−πnσ2). (1)
Already at intermediate obstacle density, the void space decomposes into many pockets of
different sizes, and long-range particle transport occurs only through the void space that is
percolating through the entire system. At a certain obstacle density n∗
component ceases to exist and all particles are trapped in finite pockets. The problem of con-
tinuum percolation constitutes a critical phenomenon of purely geometric origin , and a
c≈ 0.359, the infinite
Will be inserted by the editor3
series of predictions has been made for the characteristic behavior in the close vicinity of the
critical density n∗
c. The linear dimension of the largest finite cluster (of the void space) defines
the correlation length ξ, which is expected to diverge as ξ ∼ |n∗−n∗
is approached. Below the length scale ξ the geometric structures appear, in a statistical sense,
indistinguishable to the ones at n∗
cforming the basis for the notion of self-similarity. Simulta-
neously, the root-mean-square size ℓ of all finite clusters diverges, yet with a smaller exponent
ℓ ∼ |nc− n∗
infinite component as ncis increased towards the percolation threshold, P∞∼ (n∗
respect to geometric properties, continuum percolation shares the same universality class as
lattice percolation, and in two dimensions the exact values of the exponents are known from a
mapping to the Baxter line of the eight-vertex model ν = 4/3,β = 5/36 [35–37].
Transport of a single particle is expected to become anomalous and universal close to the
percolation threshold independent of the details of the dynamics at the microscale. Here we
consider a particle undergoing Brownian motion confined to the void space. Then the short-
time diffusion coefficient D0 fixes the microscopic time scale t0 := σ2/D0, i.e., the typical
time needed for the particle to diffuse the distance of one obstacle radius without obstruction.
The simplest quantity characterizing the motion of the tracer is the mean-square displacement
δr2(t) = ?[R(t) − R(0)]2?, where the brackets indicate averaging both over all initial positions
of the particle as well as different realizations of the disorder. In particular, particles that
are initially in a finite pocket will remain there forever and do not contribute to long-range
Directly at the percolation threshold (n∗= n∗
ics of a walker becomes subdiffusive δr2(t) ∼ t2/zfor long times t ≫ t0. The dynamic exponent
z is independent of the geometric exponents of the percolation problem, but is determined from
the universality class of random resistor networks. For obstacle densities above n∗
are trapped in finite clusters and correspondingly the mean-square displacement is expected to
saturate at the mean-square cluster size δr2(t → ∞) = ℓ2. However, close to the transition the
subdiffusive behavior should be visible in a finite time window t0≪ t ≪ txwhere txdenotes
the crossover time to localization. These arguments suggest that for small separation parameter
ǫ := (n∗− n∗
cthe mean-square displacement should obey the scaling law
c|−νas the critical density
c|−ν+β/2. The same exponent β governs the vanishing of the relative weight of the
c) the void space is self-similar and the dynam-
c, all particles
δr2(t;ǫ) ≃ t2/zδˆ r2
ˆt = t/tx
for ǫ↓0 and t ≫ t0and a scaling function δˆ r2
the diffusive side (ǫ ↑ 0) with a corresponding scaling function δˆ r2
time scale tx. To describe the crossover from critical dynamics to localization/diffusion, the
scaling functions should exhibit the following asymptotics: δˆ r2
∞) ∼ˆt−2/z, δˆ r2
localized side one infers for the crossover scaling time tx∼ ℓz∼ |ǫ|z(−ν+β/2). Interestingly, the
relevant length scale that determines the divergence of the inherited time is given by the mean
cluster size ℓ rather than the correlation length ξ. Since tx also marks the crossover to the
diffusive regime for ǫ < 0, one immediately concludes that the long-time diffusion coefficient
should vanish as D ∼ (−ǫ)µfor ǫ ↑ 0 with the conductivity exponent µ = (z − 2)(ν − β/2).
We use the value determined by Grassberger  in high-precision computer simulations for
the electrical conductivity of a percolating lattice, µ = 1.310 ± 0.001, as reference value and
calculate the anomalous dimension to z = 3.036.
+(·). We anticipate that scaling is also obeyed on
−(·) and the same crossover
±(ˆt → 0) = const. and δˆ r2
−(ˆt → ∞) ∼ˆt1−2/z, respectively. From the known long-time behavior on the
3 Simulation Results
We have performed Brownian dynamics simulations for a single particle moving in a fixed array
of hard disks of radius σ. The obstacles are distributed independently with a fixed average
density n. We have employed periodic boundary conditions for system sizes of L/σ = 10,000
and generated Brownian trajectories for very long times following an algorithm discussed in
 which was also employed recently for the three-dimensional Lorentz model . In essence,
4Will be inserted by the editor
Fig. 1. Mean-square displacement
δr2(t) of the Lorentz model for Brow-
nian particles. The obstacle density
n∗increases from top to bottom; the
thick black line indicates the long-
time asymptote for anomalous trans-
port at criticality δr2(t) ∼ t2/zwith
z = 3.036. The inset displays the time-
dependent diffusion coefficient D(t) at
obstacle density n∗= 0.30 for varying
algorithmic parameter τB/t0 = 0.25,
0.015625, 0.0025, and 0.0005625 .
particles are propagated along a deterministic, straight trajectory with specular scattering every
time the tracer hits an obstacle, yet this dynamics is interrupted at regular time intervals τB,
where new velocities are drawn from a Maxwell distribution of variance v2. Then on time scales
longer compared to τB and length scales larger than vτB, a free particle undergoes Brownian
motion with diffusion coefficient D0= v2τB/4. In the presence of the obstacles the particle can
still be considered as Brownian walker at the microscale with short-time diffusion coefficient
D0, provided τB is small relative to the inverse collision rate τc= 1/2πnσv. We shall use the
characteristic time t0 = σ2/D0 as our basic unit of time, i.e., the time a free particle needs
to traverse an obstacles radius. The algorithmic condition to mimic Brownian dynamics at
the microscale is thus given by τB/t0 ≪ 1/(2n∗)2. We have verified that τB/t0 = 0.0025 is
sufficiently small in order that the long-time behavior is independent of the microparameters
v2and τBas exemplified in the inset of Fig. 1 for a moderate obstacle density n∗= 0.3. There
the time-dependent diffusion coefficient is displayed for different τB and the curves neatly
superimpose for τB < 0.015625t0. For the production runs we have fixed τB = 0.0025t0and
have calculated mean-square displacements as running-time averages over several trajectories
for at least 100 different realizations of the disorder resulting in more than 775 trajectories in
total for each density n∗to generate high-accuracy data. The trajectories extend over huge
time windows of typically 2.5 × 106t0, yet close to the percolation threshold and for densities
n∗≤ 0.15 15 times longer trajectories have used. With current computing resources, a single
trajectory at the critical density and for the longest simulation times runs approximately 40
hours on one core of a Quad Core Intel(R) Xeon(R) CPU X5365 (3.00GHz).
Results for the mean-square displacement δr2(t) for all obstacle densities are exhibited in
Fig. 1 on double logarithmic scales. First one should notice, that the data display almost no noise
even for the longest times. For short times, all data start from the short-time diffusive motion,
δr2(t) = 4D0t, and only at times t ≃ t0the presence of the obstacles suppresses the motion and
the curves fan out. For low obstacle densities, the long-time behavior is again diffusive yet with
a suppressed diffusion coeffficient D(n∗). On the other hand, the mean-square displacements
for high n∗saturate at long times, reflecting the fact that all particles are localized.
The localized and diffusive curves are nicely discriminated by a critical density n∗
where the MSD behaves subdiffusively over at least six decades in time, i.e., it extends to
our longest observation times. This critical density coincides with the numerical estimate
c= 0.359072(4) for continuum percolation [40, 41]. The value z = 3.036, as inferred from
the exponent µ determined by finite-size scaling of the conductivity at the critical point 
provides an excellent description of the long-time behavior of the critical MSD δr2(t) ∼ t2/z.
Our simulations provide the first quantitative test that the 2d Lorentz model shares the uni-
versality class of two-dimensional random resistor networks.
Will be inserted by the editor5
Time t/t0= tD0/σ2
Fig. 2. The time-dependent diffusion
coefficient D(t) := (1/4)dδr2(t)/dt.
The obstacle density n∗increases from
top to bottom; the arrows indicate
the long-time diffusion coefficient D.
For the critical density, the time-
dependent diffusion coefficient van-
ishes as a power law, D(t) ∼ t2/z−1.
The thick line indicates the power law
t−2expected as long-time asymptote
in the localized regime due to the cul-
For densities close to n∗
cross over to either diffusive or localized behavior. By naked eye one infers already that this
crossover time increases as the critical density is gradually approached. Let us mention that in
the three-dimensional Lorentz model, the curves off the critical point deviate much more from
the critical one, than in the planar Lorentz model. Nevertheless they still display subdiffusion
in a finite time window, yet with apparent density-dependent exponents [19, 20].
c, the data follow the critical one up to some finite time where they
3.1 Time-dependent diffusion coefficients
A quantity more sensitive to the anomalous transport behavior is given in terms of the time-
dependent diffusion coefficient
where the dimension is d = 2 for the planar problem. We have taken numerical derivatives
of the MSDs taking advantage of the fact that δr2(t) varies significantly only on logarithmic
scales. Since the MSDs are calculated using our standard blocking scheme , the numerical
derivatives essentially do not introduce new noise to the data. The time-dependent diffusion
coefficient D(t) is displayed in Fig. 2 for the same densities considered above. First, one notices
that all curves start from the short-time diffusion constant D0, corroborating that our numerical
algorithm reproduces Brownian motion at small time and length scales. For increasing time D(t)
is gradually suppressed reflecting that obstacles can only slow down the overdamped dynamics.
For densities below n∗
cthe time-dependent diffusion coefficient approaches a nonzero limit D
for long-times. The values of the long-time diffusion constant D decrease rapidly as the critical
density is approached from below. Directly at the critical point, D(t) reaches a power-law
long-time asymptote D(t) ∼ t2/z−1corresponding to a subdiffusive mean-square displacement.
For densities above the critical one, the time-dependent diffusion coefficient vanishes even more
rapidly. Following the argument of persistent correlations due to power-lawdistributed exit rates
of the cul-de-sacs, one should expect a universal long-time tail D(t) ∼ t−2in the entire localized
phase . Such a behavior has indeed been observed recently in molecular dynamics simulations
for the two-dimensional Lorentz model for ballistic particles , though they considered the
velocity autocorrelation function rather than D(t). Our simulations exhibit clear evidence that
this tail remains present for Brownian particles too as we shall argue below.
The diffusion constants D extracted as long-time limits of D(t) are displayed in Fig. 3
for varying obstacle density. Over the investigated range of densities, the diffusion constant
is suppressed by a factor of 100. It vanishes as the critical density is approached and follows
amazingly well the scaling prediction D ∼ (−ǫ)µ. Even for the lowest density considered, where
the motion is practically unobstructed by the obstacles, D(n∗= 0.01) = 0.97D0deviates by only
6 Will be inserted by the editor
Diffusion constant D/D0
ε = (n∗−n∗
D ∼ |ε|µ
Fig. 3. Scaling behavior of the long-
time diffusion coefficient D with sep-
aration parameter ǫ = (n∗− n∗
The straight line in the rectification
plot (inset) confirms the value of the
conductivity exponent µ = 1.309.
8.4% from the scaling asymptote. It appears as a coincidence that the critical regime connects
down to the low-density asymptote without an intermediate region of moderate obstructed
motion. For the corresponding three-dimensional system the convergence towards the scaling
behavior is approached slowlier, however, since the corresponding conductivity exponent is much
higher µ3d= 2.88, the diffusion vanishes much more rapidly and a suppression by five orders
of magnitude can be observed . The rectification plot in the inset of Fig. 3 corroborates
that µ = 1.310 obtained by measuring the conductivity on a lattice close to percolation is
indeed the correct value. Our simulations for Brownian particles provide an independent test
that two-dimensional random resistor networks and the planar Lorentz model indeed share the
same universality class. The critical density has been determined by extrapolating the straight
line in the rectification plot to zero diffusivity, yielding n∗
value of n∗
cwas used throughout this work to simulate the critical dynamics.
c= 0.359±0.001. The thus determined
3.2 Velocity autocorrelation functions
Let us also discuss the velocity autocorrelation function (VACF), Z(t) = ?v(t) · v(0)?/d, for
the Brownian particle. Although the notion of velocity for Brownian particles is conceptually
questionable, their correlation function is well defined for times t > 0. Here we rely again on
numerical derivatives, i.e., we employ
as definition. Then the relation to the time-dependent diffusion coefficient is provided by
D(t) = D0+
where the integral is evaluated excluding the time t = 0. This form constitutes the analog of
the Green-Kubo relation, alternatively one can include a δ-distribution in the VACF to account
for the Brownian motion at the microscale.
Figure 4 displays the VACF, and one first observes that it is negative for all times, except
on time scales associated with our algorithmic microparameter τB. This fact is consistent with
the notion that obstacles can only slow down the diffusion, Eq. (5), and in the case of Brownian
motion one can show that the VACF is a completely monotone function, see Appendix A. The
long-time behavior for the diffusive regime (n < nc) is characterized by persistent correlations
that slowly decay as a power-law. The low densities display a tail Z(t) ∼ −t−2consistent
Will be inserted by the editor7
Fig. 4. Velocity autocorrelation func-
tion Z(t)= (1/4)d2δr2(t)/dt2for
Lorentz model. The thick lines indi-
cate the hydrodynamic tail t−2and
the critical behavior t2/z−2, respec-
tively. The inset displays a rectifica-
tion plot −t2Z(t)/σ2/n∗as a function
Fig. 5. Scaling behavior of the time-
dependent diffusion coefficient for the
densities below n∗
able reads tx = t0|ǫ|β−2ν−µThe in-
set includes the leading correction to
scaling with a correction amplitude
CD = −0.14ty
c. The scaling vari-
0consistent with the
with the theoretical prediction for kinetic theory for ballistic particles . It has been antici-
pated earlier  that also Brownian particles exhibit the same behavior, since the long-time
correlations originate from repeated encounters of the same frozen heterogeneities. Indeed the
Lorentz model for Brownian tracers can be solved analytically to lowest order in the scattering
density n∗and the time-dependence of the VACF including its long-time tail can be worked
out exactly . Nevertheless, to the best of our knowledge, our simulation results provide the
first direct evidence for this universality at all densities. As the density is gradually increased,
the overall signal in the VACF becomes larger and the exponent of the power-law tail appears
to drift. A rectification plot shows that the t−2behavior is assumed for all densities as the
late-time relaxation. The critical asymptote appears in an intermediate window which extends
to longer and longer times as the critical density is approached. Our data also show that the
amplitude of the hydrodynamic tail diverges close to n∗
same arguments as in the ballistic case .
cwhich can be rationalized using the
4 Dynamic scaling analysis
The power-law behavior in both the mean-square displacement or the time-dependent diffusion
coefficient at criticality and the vanishing of the diffusion constant as a power law upon ap-
proaching the critical density is merely one aspect of the critical behavior. Yet, the universality
8Will be inserted by the editor
Fig. 6. Rectification of the time-
dependent diffusion coefficient D(t)
at the critical density n∗
cal amplitude is determined to AD =
. The inset displays the
approach of the MSD towards the crit-
ical law. The thick line indicates a
power laws with the universal correc-
tion exponent y = 0.49.
c. The criti-
hypothesis suggests a much more sensitive test in terms of scaling. For example, the mean-
square displacement δr2(t;ǫ) for small separation parameters |ǫ| ≪ 1 and long times t ≫ t0is
expected to fulfill Eq. (2). Here we show that the time-dependent diffusion coefficient D(t;ǫ)
can be used equivalently to test the scaling prediction. Taking derivatives, we suggest
D(t;ǫ) ≃ t2/z−1ˆD±(ˆt),
ˆt = t/tx
with the scaling time tx := t0|ǫ|−(2ν−β+µ). The connection with the scaling function for the
mean-square displacement is easily established,
For short rescaled times,ˆD±(ˆt → 0) = const. =: ADand the critical behavior is recovered. For
long timesˆD−(ˆt) ∼ˆt1−2/zsuch that diffusion is reached for ǫ < 0. On the localized side, the
mean-square displacements saturate and the leading behavior δr2
Eq. 7, thusˆD+(ˆt) = o(ˆt−2/z).
Figure 5 displays a rectification plot for D(t) for obstacle densities below the critical ones.
For large rescaled times the curves nicely superimpose, though this reflects merely the fact that
the diffusion regime is reached for all cases and that the long-time diffusion coefficient obeys the
scaling prediction D ∼ (−ǫ)µ. For short rescaled times the curves converge to a constant which
is given by the long-time behavior of the time-dependent diffusion coefficient at the critical
point. The fanning out of the curves arises due to corrections to scaling and eventually because
of the crossover to the microscopic regime.
To gain further insight into the scaling behavior we extend our scaling hypothesis by a
generic irrelevant scaling variable. Then it has been shown recently within a cluster-resolved
scaling theory  that the mean-square displacement should obey
+(ˆt) ∼ˆt−2/zcancels exactly in
δr2(t;ǫ) = t2/zδˆ r2
±(ˆt)?1 + t−y∆±(ˆt)?,(8)
where y is another universal exponent characterizing the approach of the critical dynamical
behavior. It is connected to a correction-to-scaling exponent Ω for the cluster-size distribution
via the scaling relation y = Ω(νd − β)/[z(ν − β/2)]. For the two-dimensional case the value
y = 0.49(3)  was determined for random walks on a lattice, which we shall use in the
following. Taking derivatives with respect to time, the corresponding prediction for the time-
dependent diffusion coefficient is
D(t;ǫ) ≃ t2/z−1ˆD±(ˆt)?1 + t−y∆D
Will be inserted by the editor 9
where the new scaling function ∆D
±(ˆt) is connected to the one of the mean-square displacement
For small rescaled times the correction-to-scaling function reduces to a constant ∆D
const. =: CD. and one easily infers the relation to the correction-to-scaling constant C :=
∆±(ˆt → 0) for the mean-square displacement: CD= C(2 − yz)/2. In particular at criticality,
the time-dependent diffusion coefficient displays a power-law correction for long times
±(ˆt → 0) =
D(t;ǫ = 0) ≃ ADt2/z−1?1 + CDt−y?. (11)
For long rescaled timesˆt → ∞, the correction to scaling function behaves as a power-law again
D(ǫ) ∼ (−ǫ)µ?
The time-dependent diffusion coefficient D(t) at the critical density is displayed in Fig. 6 in
a rectification plot. Within the statistical errors of our simulation one observes a saturation at
long times provided the established value for z is used, indicating that the asymptotic behavior
is reached. At very long times the curve starts to deviate again due to statistical fluctuations,
possibly finite-size effects, and the uncertainty of the value for the critical obstacle density.
The amplitude of the critical relaxation AD= limt→∞t1−2/zD(t;ǫ = 0) has been determined
to AD = 0.508D0t1−2/z
from our numerical data to optimize data collapse for the scaling in
the diffusive and localized regime, see below. The approach towards this power-law behavior
is consistent with a power law according to Eq. (11) with a correction to scaling amplitude
It appears that for a deeper analysis of the correction to scaling behavior as suggested by
Eqs. (8) and (9), the precise form of the scaling function ∆D
arguments, we know that the correction function for the MSD, ∆±(ˆt), smoothly interpolates
between a constant ∆±(ˆt → 0) =: C and power laws ∆±(ˆt → ∞) ≃˜C±ˆty. Then the correction
term for the time-dependent diffusion coefficient behaves for large rescaled timesˆt → ∞ as
Upon closer inspection of the correction behavior, Eq. (12), and the simulation results, Fig. 3,
the corrections are very small for large rescaled times. Then one may approximate ∆D
its short-time asymptote ∆D
diffusive/localized time regime. The result of this procedure is displayed in the inset of Fig. 5 for
the diffusive side with a quite impressive improvement of the scaling behavior. Let us emphasize
that the only new parameter CDis in principle fixed by the critical behavior, such that no free
parameters enter the scaling plot.
The scaling behavior for the time-dependent diffusion coefficient D(t) is tested for the lo-
calized regime, n∗> n∗
c, in Fig. 7. Again the curves approach a constant at short rescaled times
as the critical density is approached. Accounting for the correction by the same procedure
as for the diffusive side yields an almost perfect data collapse without any new parameters.
The scaling behavior at large times reflects that also the approach of the saturation in the
MSDs is universal. From our general discussion, the scaling function is known to vanish rapidly
ˆD+(ˆt) = o(ˆt−2/z). Following Machta and Moore  there should universal power-law correla-
tions in the entire localized phase, D(t) ∼ t−2for t → ∞, due to the meandering of the particle
in the self-similar cul-de-sacs. Assuming that the crossover from the critical law to these univer-
sal hydrodynamic tails is again given by txas we have argued earlier for ballistic particles ,
leads to a scaling prediction ofˆD+(ˆt) ∼ˆt−2/z−1. In particular, one expects a divergence of the
prefactor of the tail in the localized phase according to t2/z+1
Fig. 7 and provides a nice description of the data.
−(ˆt → ∞) ≃˜CD
±ˆty, yielding corrections for the asymptotic behavior of the long-time diffusion
0, see inset of Fig. 6.
±(ˆt) has to be known. From general
−(ˆt) ∼ˆty−2/zon the diffusive side andˆD(ˆt)∆D
−(ˆt) = o(ˆty−2/z) in the localized regime.
±(ˆt) = CDfor all rescaled times, i.e., ignore the corrections on the
. This prediction is indicated in
10 Will be inserted by the editor
Fig. 7. Scaling behavior of the time-
dependent diffusion coefficient for the
densities above n∗
= t0|ǫ|β−2ν−µ. The thick
line indicates the power law t−2/z−1
which is expected to hold in the en-
tire localized phase. The inset includes
the leading correction to scaling with
the same correction amplitude CD =
cwith the crossover
0as in the diffusive regime.
The dynamics of a tracer particle in a densely packed planar course of obstacles has been
investigated by Brownian dynamics simulation. The slowing down of the dynamics close to the
percolation threshold is accompanied by critical behavior observed over more that 6 decades in
time. We corroborate that the planar Lorentz model shares a universality class with the random
resistor network where the critical exponents are known from earlier simulations. We have
shown that the time-dependent diffusion coefficient constitutes a suitable quantity to analyze
the scaling behavior close to the transition. The corresponding scaling relations have been
derived and extended by the leading correction. We find that scaling behavior is in general well
obeyed and the corrections to scaling appear much less important than for the three-dimensional
The Lorentz model exhibits power-law long-time anomalies away from the critical density
due to repeated encounters with the same scatterer. These tails have been derived originally for
ballistic particles, yet they turn out to be universal irrespective of the dynamics at microscopic
scales. Then the velocity autocorrelation, defined via a second derivative of the mean-square
displacement exhibits the tails even for Brownian tracers. On the localized side we also find
long-time tails due to the self-similar distribution of exit times of the cul-de-sacs , again
irrespective of the microscopic dynamics. Interestingly, these long-time tails are part of the
scaling function for the time-dependent diffusion coefficient.
The assumption that the obstacles are distributed independently is certainly an oversimpli-
fication in real systems. Then one would like to extend the Lorentz model where the matrix
consists of some frozen-in configuration of a strongly interacting liquid or a snapshot of a slowly
rearranging matrix of obstacles. Second, experiments are usually for a finite concentration of
particles meandering in the array of obstacles and one may ask at what time and length scales
these interaction of the tracers modifies the dynamics in the labyrinth. In three dimensions an
intriguing interplay of the physics of the glassy dynamics and the localization transition has
been discovered recently [3–8], and since the glass transition in two-dimensions is qualitatively
similar [45, 46] one may hope that the physics of the planar Lorentz model is applicable in
size-disparate two-dimensional mixtures.
Financial support from the Deutsche Forschungsgemeinschaft via contract No. FR 850/6-1 and from
the Konrad-Adenauer-Stiftung (T.B.) is gratefully acknowledged. This project is supported by the
German Excellence Initiative via the program “Nanosystems Initiative Munich (NIM).”
Will be inserted by the editor11
A Appendix: Completely monotone functions
In this Appendix we develop a spectral representation for the mean-square displacement and
the velocity-autocorrelation function for arbitrary dimension d.
For a Brownian particle in an external potential U(r) the time-evolution of the conditional
probability distribution Ψ(r,t) to find the particle at r at time t provided it has been at r′and
some earlier time t′is governed by the Smoluchowski equation
whereˆΩ(r) denotes the Smoluchowski operator acting on the position r. At the very end, we
are interested in hard potentials with infinite barriers, however we anticipate that this case is
assumed as limit of smooth potentials becoming increasingly steep.
One can also consider the evolution of Ψ with respect to the conditional time and one can
− ∂t′Ψ(r,t|r′t′) = −D0
where the adjointˆΩ+(r′) is with respect to the standard scalar product. Furthermore,ˆΩ+(r′)
is identified with the backward Smoluchowski operator and now acts on r′.
The mean-square displacement is obtained as an average
δr2(t − t′) ≡
drdr′(r − r′)2Ψ(rt|r′t′)Ψeq(r′),(15)
where Ψeq(r) = Z−1exp(−U(r)/kBT) denotes the equilibrium distribution. In principle one
may also introduce a disorder average for different realizations of the potential U(r), yet we
anticipate that for large enough systems the quantities of interest are self-averaging. Since in
equilibrium the MSD is stationary, it depends only on the time difference and one derives
dt2δr2(t − t′) = −d
dt′δr2(t − t′)
=drdr′(r − r′)2?ˆΩ(r)ˆΩ+(r′)Ψ(rt|r′t′)
?ˆΩ(r′)ˆΩ+(r)(r − r′)2Ψeq(r′)
ˆΩ+(r′)ˆΩ+(r)(r − r′)2?
where in the second to last line the property of the Smoluchowski operatorˆΩ(r)[A(r)Ψeq(r)] =
result shows that the second derivative of the MSD can be interpreted essentially as the negative
of the autocorrelation function ofˆΩ+(r)r. To make connection with the ballistic case it is helpful
to introduce v = iˆΩ+(r)r as a formal velocity, and one recovers the usual relation to the velocity
?v(t) · v(t′)? =1
Next we recall that autocorrelation functions C(t) = ?A(t)∗A(0)? for overdamped dynamics
are completely monotone, i.e., their derivatives exhibit fixed sign
Ψeq(r) valid for any well-behaved function A(r) has been employed. The preceding
dt2δr2(t − t′).(17)
dtnC(t) ≥ 0for all n ∈ N0,t ≥ 0.(18)
12Will be inserted by the editor
A sketch of a non-rigorous proof is as follows. First, consider the complex scalar product
Then one easily verifies thatˆΩ+is hermitian with respect to this scalar product ?A|Ω+B? =
?Ω+A|B?. Then with the formal solution of Eq. (13), Ψ(rt|r′t′) = exp[(t−t′)ˆΩ(r)]δ(r−r′), one
finds a representation of the autocorrelation function as
C(t) = ?eΩ+tA|A?.(20)
Yet sinceˆΩ+is hermitian all eigenvalues are real, and by a ‘variational principle’,
?ˆΩ+A|A? = −D0
Ψeq(r) ≤ 0,(21)
also negative semi-definite. Zero constitutes the non-degenerate eigenvalue with constant eigen-
function |0?. A spectral decomposition of the backwardsSmoluchowski operatorˆΩ+= −?
in terms of eigenfunctionsˆΩ+|λ? = λ|λ? shows that an autocorrelation function can be repre-
|?A|λ?|2exp(−λt), for t > 0.(22)
From this representation one immediately infers that C(t) is completely monotone. By the
famous Bernstein theorem  the converse is also true, i.e., any completely monotone function
allows for a representation as a superposition of relaxing exponentials with positive weights.
For the VACF one concludes
?v(t) · v(t′)? = −
Since the equilibrium state |0? is annihilated by the backward Smoluchowski operatorˆΩ+|0? =
0, the sum extends in fact only over positive eigenvalues λ > 0.
Integration yields the time dependent diffusion coefficient
D(t) = D0−
21 − exp(−λt)
and one immediately infers that D(t) is monotonically decreasing to the long-time diffusion
D = D0−
For the mean-square displacement one obtains the representation
δr2(t) = 2dD0t − 2
2λt − 1 + exp(−λt)
valid for t ≥ 0.
1. M. Sahimi, Heterogeneous Materials, vol. 22 of Interdisciplinary Applied Mathematics
(Springer, New York, 2003).
2. G. Dagan, J. Fluid Mech. 145, 151 (1984).
Will be inserted by the editor13
3. A. Meyer, J. Horbach, W. Kob, F. Kargl, and H. Schober, Phys. Rev. Lett. 93, 027801
4. F. Kargl, A. Meyer, M. M. Koza, and H. Schober, Phys. Rev. B 74, 014304 (2006).
5. T. Voigtmann and J. Horbach, Europhys. Lett. 74, 459 (2006).
6. A. J. Moreno and J. Colmenero, J. Chem. Phys. 125, 164507 (2006).
7. A. J. Moreno and J. Colmenero, Phys. Rev. E 74, 021409 (2006).
8. N. Kikuchi and J. Horbach, EPL 77, 2600 (2007).
9. R. J. Ellis, Trends in Biochemical Sciences 26, 597 (2001), ISSN 0968-0004.
10. R. J. Ellis and A. P. Minton, Nature 425, 27 (2003), ISSN 0028-0836.
11. I. M. Toli´ c-Nørrelykke, E.-L. Munteanu, G. Thon, L. Oddershede, and K. Berg-Sørensen,
Phys. Rev. Lett. 93, 078102 (2004).
12. I. Golding and E. C. Cox, Phys. Rev. Lett. 96, 098102 (2006).
13. M. Deverall, E. Gindl, E.-K. Sinner, H. Besir, J. Ruehe, M. Saxton, and C. Naumann,
Biohpys. J. 88, 1875 (2005).
14. A. Kusumi, C. Nakada, K. Ritchie, K. Murase, K. Suzuki, H. Murakoshi, R. S. Kasai,
J. Kondo, and T. Fujiwara, Annu. Rev. Biophys. Biomol. Struct. 34, 351 (2005).
15. M. Weiss, H. Hashimoto, and T. Nilsson, Biophys. J. 84, 4043 (2003), ISSN 0006-3495.
16. M. R. Horton, F. H¨ ofling, J. O. R¨ adler, and T. Franosch, submitted to Soft Matter (2009).
17. H. van Beijeren, Rev. Mod. Phys. 54, 195 (1982).
18. D. Stauffer and A. Aharony, Introduction to Percolation Theory (Taylor & Francis, London,
1994), 2nd ed.
19. F. H¨ ofling, T. Franosch, and E. Frey, Phys. Rev. Lett. 96, 165901 (2006).
20. F. H¨ ofling, T. Munk, E. Frey, and T. Franosch, J. Chem. Phys. 128, 164517 (2008).
21. F. H¨ ofling, Ph.D. thesis, Ludwig-Maximilians-Universit¨ at M¨ unchen (2006), ISBN 978-3-
22. B. J. Sung and A. Yethiraj, Phys. Rev. Lett. 96, 228103 (2006).
23. B. J. Sung and A. Yethiraj, J. Phys. Chem. B 112, 143 (2008), ISSN 1520-6106.
24. B. J. Sung and A. Yethiraj, J. Chem. Phys. 128, 054702 (2008).
25. C. Bruin, Phys. Rev. Lett. 29, 1670 (1972).
26. C. Bruin, Physica 72, 261 (1974).
27. B. J. Alder and W. E. Alley, J. Stat. Phys. 19, 341 (1978).
28. B. J. Alder and W. E. Alley, Physica A 121, 523 (1983).
29. W. E. Alley, Ph.D. thesis, California Univ., Davis (1979).
30. C. P. Lowe and A. J. Masters, Physica A 195, 149 (1993).
31. A. Weijland and J. M. J. van Leeuwen, Physica (Amsterdam) 38, 35 (1968).
32. M. H. Ernst and A. Weijland, Phys. Lett. A 34, 39 (1971).
33. F. H¨ ofling and T. Franosch, Phys. Rev. Lett. 98, 140601 (2007).
34. J. Machta and S. M. Moore, Phys. Rev. A 32, 3164 (1985).
35. D. ben Avraham and S. Havlin, Diffusion and Reactions in Fractals and Disordered Systems
(Cambridge University Press, Cambridge, 2000).
36. M. P. M. den Nijs, J. Phys. A 12, 1857 (1979).
37. B. Nienhuis, Phys. Rev. Lett. 49, 1062 (1982).
38. P. Grassberger, Physica A 262, 251 (1999).
39. A. Scala, T. Voigtmann, and C. De Michele, J. Chem. Phys. 126, 134109 (2007).
40. J. Quintanilla, S. Torquato, and R. M. Ziff, J. Phys. A 33, L399 (2000).
41. J. A. Quintanilla and R. M. Ziff, Phys. Rev. E 76, 051115 (2007).
42. P. H. Colberg and F. H¨ ofling, Accelerating glassy dynamics on graphics processing units
(2009), arXiv:0912.3824 [cond-mat.soft].
43. T. Franosch, F. H¨ ofling, T. Bauer, and E. Frey, submitted to Chem. Phys. (2010).
44. A. Kammerer, F. H¨ ofling, and T. Franosch, EPL 84, 66002 (2008).
45. L. Santen and W. Krauth, Nature (2000).
46. M. Bayer, J. M. Brader, F. Ebert, M. Fuchs, E. Lange, G. Maret, R. Schilling, M. Sperl,
and J. P. Wittmer, Phys. Rev. E 76, 011508 (2007).
47. W. Feller, An Introduction to Probability Theory and Its Applications (Wiley, 1968), ISBN