Approach to the glass transition studied by higher order correlation ...
Approach to the glass transition studied by higher order correlation functions N. Laˇ cevi´ c and S.C. Glotzer Departments of Chemical Engineering and Materials Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA Abstract. We present a theoretical framework based on a higher order density correlation function, analogous to that used to investigate spin glasses, to describe dynamical heterogeneities in simulated glass-forming liquids. These higher order correlation functions are a four-point, time-dependent density correlation function g4 (r, t) and corresponding “structure factor” S4 (q, t) which measure the spatial correlations between the local liquid density at two points in space, each at two different times. g4 (r, t) and S4 (q, t) were extensively studied via MD simulations of a binary LennardJones mixture approaching the mode coupling temperature from above in Refs. [1–5]. Here, we examine the contribution to g4 (r, t), S4 (q, t), and corresponding dynamical correlation length, as well as the corresponding order parameter Q(t) and generalized susceptibility χ4 (t) from localized particles. We show that the dynamical correlation length ξ4SS (t) of localized particles has a maximum as a function of time t, and the value of the maximum of ξ4SS (t) increases steadily in the temperature range approaching the mode coupling temperature from above.
2 1. Introduction One of the most challenging problems in condensed matter physics is understanding the dynamics and thermodynamics of glass formation [6]. On cooling from high temperature, liquids may crystallize at Tm , or if a liquid is cooled so that crystallization is avoided, it may become supercooled. As a supercooled liquid is further cooled, the particles move more and more slowly, and their motion is slowed down so drastically that, at some low T , particles will not be able to rearrange. This rearrangement is necessary for a liquid to find its equilibrium, and at this temperature particles in a liquid appear to be “frozen” or “jammed”, at least for the time scale of an experiment. At Tg the supercooled liquid is no longer in equilibrium. This is called a glass transition, and the temperature at which this occurs is called the glass transition temperature Tg . An extensive treatment of the dynamics and thermodynamics of supercooled liquids can be found in the textbooks by Debenedetti [7], Harrison [8] and Knight [9]. There are many conference proceedings [10–15] on experimental developments, theoretical advances and the role of computer simulation in the search for a universal theory of supercooled liquids and the glass transition. We are still left with unanswered questions about the dramatic slowing down in the molecular motion as temperature is lowered to Tg , which would not be surprising alone, but this process is accompanied with no significant change in the long-range structure. How is it possible that dynamics changes so dramatically while the structure, as measured by traditional static two-point correlation functions, remains almost unchanged, and what type of transition is the glass transition are some of the crucial questions in the field of glass transition research. To be able to answer those questions, we must fully understand the dynamics of particles on the microscopic level, i.e. search for the patterns in particle motion and relate them to a mechanism for slowing down. The prominent features of supercooled liquids are the non-Arrhenius temperature dependence of the viscosity and structural relaxation time [16], non-exponential character of the structural relaxation time [17], absence of changes in static structural quantities [18–21] and decoupling of the transport coefficients [22–25]. In particular, the non-exponential character of the relaxation of density correlation functions and decoupling of the transport coefficients can be rationalized with the existence of spatially heterogeneous dynamics (SHD) or “dynamical heterogeneity” now well established in experiment and computer simulation [26–30]. Therefore, understanding of the origin of dynamical heterogeneity is directly related to understanding the origin of the glass transition. We refer to a system as dynamically heterogeneous if it is possible to select a dynamically distinguishable subset of particles by experiment or computer simulation [31]. From the theoretical point of view, the question “what is dynamical heterogeneity” still has different answers depending on the theoretical framework used to describe it. Recently, a new theoretical approach to the problem of cooperativity demonstrated how spatially heterogeneous dynamics (SHD) can arise in simple systems with cooperative dynamics. This theory predict a growing correlation length on decreasing T [32]. Here
3 we use a four-point, time-dependent density correlation function formalism to select a dynamically distinguishable subset of particles. In particular, we investigate properties of those particles deemed to be localized. 2. Method and model The simulation method we use to generate data for our analyses is molecular dynamics (MD). This is a widely used method in the investigation of supercooled liquids and glasses that provides static and dynamic properties for a collection of particles. The code we use in our simulations is LAMMPS [33], a parallel MD code based on spatial decomposition parallel technique. We study a 50/50 binary mixture of particle types “A” and “B” that interact via the Lennard-Jones potential "µ
Vαβ (r) = 4²αβ
σαβ r
¶12
µ
σαβ − r
¶6 #
.
(1)
This system has been previously studied by Wahnstrom [34] and Schrøder [35]. Following these authors, we use length parameters σAA = 1, σBB = 5/6, and σAB = (σAA +σBB )/2, and energy parameters ²AA = ²BB = ²AB = 1. The masses of the particles are chosen to be mA = 2 and mB = 1. We shift the potential and truncate it so it vanishes at r = 2.5σAB . We simulate a system of N = 8000 particles using periodic boundary conditions in a cubic box of length L = 18.334 in units of σAA , which yields a density of 1 2 ρ = N/L3 = 1.296 for all state points. We report time in units of τ = (mB σAA /48²AA ) 2 , length in units of σAA , and temperature, T , in units of ²AA /kB , where kB is Boltzmann’s constant. We simulate eight state points at temperatures ranging from T = 2.0 to T = 0.59, following a path similar to that followed in Refs. [2, 35–37]. The simulations are performed in the N V E ensemble. We estimate the mode coupling temperature TMCT = 0.57 ± 0.01, (the glass transition temperature Tg is typically in the range 0.6TM CT < Tg < 0.9TM CT [38]) and the Kauzmann temperature T0 , which can be considered a lower bound for the glass transition temperature Tg , T0 = 0.48 ± 0.02. How we estimate these temperatures and other simulation details can be found in Refs. [4,5]. 3. Background In this section, we briefly review the theoretical framework of the four-point, spatiotemporal density correlation function and corresponding structure factor. Detailed derivation of these quantities can be found in Refs. [1–5]. We consider a liquid of N particles occupying a volume V with density ρ(r, t) = P δ(r − ri (t)), and investigate a quantity Z
Q(t) =
dr1 r2 ρ(r1 , 0)ρ(r2 , t)w(|r1 − r2 |) =
N X N X i=1 j=1
w(|ri (0) − rj (t)|),
(2)
4 which measures the number of particles that, in time t, either remain within a distance a of their original position, or are replaced by another particle (“overlapping particles”). The reason for introducing an “overlap” function w is to eliminate weakly correlated vibrational motion of the particles (for more details see e.g. Ref. [4]). The fluctuation in Q(t) may be defined as βV [hQ(t)2 i − hQ(t)i2 ]. (3) N2 Expressing χ4 (t) in terms of the four-point correlation function G4 (r1 , r2 , r3 , r4 , t), we obtain βV Z χ4 (t) = 2 dr1 dr2 dr3 dr4 G4 (r1 , r2 , r3 , r4 , t), (4) N where χ4 (t) =
G4 (r1 , r2 , r3 , r4 , t) = hρ(r1 , 0)ρ(r2 , t)w(|r1 − r2 |)ρ(r3 , 0)ρ(r4 , t)w(|r3 − r4 |)i − hρ(r1 , 0)ρ(r2 , t)w(|r1 − r2 |)i × hρ(r3 , 0)ρ(r4 , t)w(|r3 − r4 |)i.
(5)
Note that in the case of both the mean-field, p-spin model and a liquid in the hypernetted chain approximation [39–42], the time dependence of χ4 (t) was calculated numerically from an analytic expression in Ref. [1]. Those calculations provide the first analytical prediction of the growth of a generalized dynamical susceptibility and, by inference, a corresponding dynamical correlation length ξ4 (t) in a model glass-forming system. We wish to radially average the four-point correlation function in Eq. (5) to obtain a function g4 (r, t) that depends only on the magnitude r of the distance between two particles at time t = 0. We start from the requirement that Z
χ4 (t) = β and obtain g4 (r, t) = −
drg4 (r, t),
(6)
E 1 DX δ(r − rk (0) + ri (0))w(|ri (0) − rj (t)|)w(|rk (0) − rl (t)|) N ρ ijkl D Q(t) E2
. (7) N Assuming an isotropic, homogeneous system, g4 (r, t) is a function of r = |r| (i.e. g4 (r, t)). Details of above derivation can be found in Refs. [4, 5]. g4 (r, t) describes spatial correlations between overlapping particles separated by a distance r at the initial time (using information at time t to label the overlapping particles). The first term in g4 (r, t) is a pair correlation function restricted to the subset of overlapping particles. The second term represents the probability of any two randomly chosen particles overlapping at times 0 and t. The structure factor S4 (q, t) that corresponds to g4 (r, t) is its Fourier transform Z
S4 (q, t) =
g4 (r, t)exp[−iq · r]dr.
(8)
5 Eq. (8) implies that χ4 (t) lim S (q, t) = . (9) q→0 4 β Eq. (8) is analogous to the static structure factor S(q), but “scatters” off of overlapping particles using information on overlapping particles at time t to label particles at time 0. 3.1. Self and distinct contributions to Q(t), χ4 (t), g4 (r, t), and S4 (q, t) The contribution of a given particle i to Q(t) is a result of three possible events: (i) particle i remains within a distance a of its original position; (ii) particle i moves and is replaced (within a distance a) by another particle; or (iii) particle i moves a distance greater than a and is not replaced by another particle. Case (iii) does not count as an overlap, and thus does not contribute to Q(t). Cases (i) and (ii) count as overlaps and contribute to the value of Q(t). Case (ii) and (iii) belong to the set of delocalized particles. However, the case (i) and case (ii) clearly represent two very different physical situations. To elucidate the various contributions to the four-point correlation function, we separate Q into self and distinct components, Q(t) = QS (t) + QD (t) =
N X
w(|ri (t) − ri (0)|)
i=1
+
N X N X
w(|ri (0) − rj (t)|).
(10)
i=1 i6=j
The self part, QS (t), measures the number of particles that move less than a distance a in a time interval t; we call these “localized” particles. The distinct part, QD (t) measures the number of particles replaced within a radius a by another particle in time t; we call these “replaced” particles. Following the scheme of decomposing Q(t), χ4 (t) can be decomposed into self χSS (t), distinct χDD (t), and cross χSD (t) terms: χ4 (t) = χSS (t)+χDD (t)+χSD (t), where χSS (t) ∝ hQ2S (t)i−hQS (t)i2 , χDD (t) ∝ hQ2D (t)i−hQD (t)i2 , and χSD (t) ∝ hQS (t)QD (t)i− hQS (t)ihQD (t)i. Thus χSS (t) is the susceptibility arising from fluctuations in the number of localized particles, χDD (t) is the susceptibility arising from fluctuations in the number of particles that are replaced by a neighboring particle, and χSD (t) represents cross fluctuations between the number of localized and replaced particles. We also consider “delocalized” particles, that is, particles that in a time t are more than a distance a from their original location. As was pointed out in Ref. [2], substituting 1 − w for w in Eq. (2) gives the delocalized order parameter QDL (t) = N − QS (t), and as a result, χDL (t) ≡ χSS (t). Likewise, we can find terms in g4 (r, t) and S4 (q, t) that correspond to localized, replaced and delocalized particles, e.g. g4 (r, t) and S4 (q, t) of localized particles correspond to g4SS (r, t) and S4SS (q, t) and for i = j and l = k in Eq. (7) and Eq. (8), respectively. In the next section we present numerical results for S4SS (q, t) and corresponding correlation length ξ4SS (t).
6 4. Results Figure 1 shows the time and temperature dependence of Q(t) and χ4 (t) and their terms described in Subsection 3.1. Figure 1 shows that for all sufficiently low T , hQ(t)/N i and hQS (t)/N i, are characterized by a two-step relaxation, commonly observed in the intermediate scattering function [26], as a result of the transient caging of particles. hQDL (t)/N i has the opposite time dependence from hQS (t)/N i due to the fact that it measures the number of particles that moved a distance greater than a. The same applies to hQD (t)/N i since those particles constitute the subset of delocalized particles. At short times, particles oscillate in a region smaller than the overlap radius a, and so hQ(t)/N i = 1 and hQS (t)/N i = 1. We observe a short, initial relaxation of Q(t) and QS (t), and a longer, secondary relaxation. χ4 (t), χSS (t), and χDL (t) are zero at short time, attain a maximum at some intermediate time tmax , and decay at long time to zero 4 in the thermodynamic limit. χ4 (t) and its terms measure the correlated motion between pairs of particles, calculated equivalently from fluctuations in the number of localized, replaced and delocalized particles or from the corresponding four-point correlation functions. The behavior of χ4 (t) demonstrates that correlations are time dependent, with a maximum at a time tmax . Similar behavior was reported for the same and other model liquids in 4 Refs. [2, 43, 44] for a generalized susceptibility related to a displacement-displacement correlation function χU (t), which measures the correlations between displacements of particles as a function of time. In these works, SHD was observed to be most pronounced in the α-relaxation regime. We find that the correlations measured by χ4 (t) are also most pronounced in the α-relaxation regime (see e.g. Ref. [4]). Figure 2 shows the time dependence of χSS (t) at T = 0.62. There are nine points marked as open circles that correspond to the times at which we show localized and delocalized particles in Figure 3. The four-point structure factor of localized particles S4SS (q, t) calculated from Eq. (8) is plotted vs q in Figure 4, at T = 0.60. We also find that at very early i = 1) S4SS (q, t) = S(q). We find that while S(q) shows no change at times (when h Q(t) N small q (see e.g the static structure factor in Ref. [3]), S4SS (q, t) develops a peak at small q which grows (Figure 4(a)) and decays in time (Figure 4(b)), indicating the presence of long-range correlations in the locations of overlapping particles. Inspired by the Ornstein-Zernike theory, OZT [45], which describes, e.g., density fluctuations near a liquid-gas transition, we use the following function, S4SS (q, t) =
S4SS (0, t) , (1 + (qξ4SS (t))2 )
(11)
where S4SS (0, t) and ξ4SS (t) are fitting parameters. The fitting was performed using an interior-reflective Newton method in Matlab, and setting the termination tolerance of the function value to 0.1. We find a good fit to the data in the q range from q = 0.34 to q = 1.9, for each T and time. The observed narrowing of the peak directly reveals the growing range of g4SS (r, t) with decreasing T .
7 The time and temperature dependence of ξ4SS (t) obtained from this fit is plotted for several state points in Figure 5. We see that the qualitative behavior of ξ4SS (t) is similar SS to that of χSS 4 (t): ξ4 (t) has a maximum in time that coincides with the maximum in χSS 4 (t), and as T decreases, the amplitude and time of this maximum increase. The highest values of ξ4SS (t) for T = 0.60 exceed half the simulation box size. The fit at these points depends strongly on the number of points used, initial parameter guesses, and other details and can yield large values (e.g. > 40) depending on these details. Since these values greatly exceed the range over which we can meaningfully interpret the resulting correlation length, we make no attempt to rigorously define the upper error bounds at these points, but the data is well bounded from below. The fits at all other points and temperatures are well constrained. The length scale ξ4SS (t) characterizes the typical distance over which localized particles are spatially correlated. 5. Discussion In this paper, we have focused on a four-point, time-dependent density correlation function g4SS (r, t) and corresponding time dependent structure factor S4SS (q, t), and demonstrated that those functions are sensitive to dynamical heterogeneity in a model glass-forming liquid. As derived in previous works [1–3,46,47], this correlation function is related to an order parameter Q(t) corresponding to the number of ”overlapping” particles in a time window t, where the term ”overlap” is used to denote a particle that was either localized or replaced in a time t. We calculated the correlation length of localized particles ξ4SS (t), characterizing the range of g4SS (r, t), and showed that it depends on time, and attains its maximum value in the α-relaxation regime. We also showed that this maximum grows to exceed half of the simulation box size, close to TMCT . This length scale characterizes the typical size of dynamically homogeneous domains. The characteristic length scale calculated here is related to length scales calculated from the displacement-displacement correlation function [44], cluster size [48], and other measures of correlated particle motion and dynamical heterogeneity [49–51]. ξ4SS (t) is essentially the same as that obtained by considering the delocalized particles (the set of particles that in any time window t move beyond a distance a) due to the mathematical identity between χ4 for localized and delocalized particles. This suggests a picture of fluctuating domains of temporarily localized and delocalized particles, perhaps similar to that proposed by Stillinger and Hodgedon [24]. Finally, we note that all quantities presented here can be measured in dense colloidal suspensions using confocal microscopy studies [52, 53]. 6. References [1] S. Franz, C. Donati, G. Parisi, and S. C. Glotzer, Philos. Mag. B 79 1827 (1999); C. Donati, S. Franz, S. C. Glotzer, and G. Parisi, Journal of Non-Crystalline Solids 307, 215 (2002). [2] S. C. Glotzer, V. N. Novikov, and T. B. Schrøder, Journal of Chemical Physics 112, 509 (2000).
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10 Figure captions
Figure 1. Temperature and time dependence of hQ(t)/N i, hQS (t)/N i, hQD (t)/N i, hQDL (t)/N i, χ4 (t), χSS (t), χDD (t) and χDL (t) ≡ χSS (t).
Figure 2. Time dependence of χSS 4 (t) at T = 0.62. Times that are encircled and labeled with appropriate fraction of tmax correspond to the snapshots in Figure 3. 4
Figure 3. Localized (white) and delocalized (gray) particles at times 0.007tmax , 4 max max max max max max max 0.04tmax , 0.2t , 0.5t , t , 1.6t , 3.3t , 6.3t , 10.2t . 4 4 4 4 4 4 4 4
Figure 4. Time dependence of S4SS (q, t) at T = 0.62. S4SS (q, t) is shown at times identical to those shown for g4 (r, t) in Figure 2. Note that the height of the first diffraction peak in S4SS (q, t) decreases monotonically as a function of time. This is because it depends on the number of localized particles, which also decreases monotonically in time (see Figure 1).
Figure 5. Time and temperature dependence of ξ4SS (t) obtained from the fits to Eq. (11). The data shown are smoothed over successive groups of five points.
20 χ4(t)
T=0.60
15 10 5
T=0.94 T=0.60
15 SS
T=0.94
χ (t)
T=0.60
10 5
T=0.94
0.1 0.05
DD
0.8 0.6 0.4 0.2
T=0.94
χ (t)
0.15
T=0.60
T=0.60
χ (t)
T=0.94 T=0.60 -2
10
-1
10
0
10
1
10
t
2
10
3
10
T=0.94
0.5 0.4 0.3 0.2 0.1
T=0.60 T=0.94 T=0.60
15 DL
0.8 0.6 0.4 0.2
1 0.8 0.6 0.4 0.2
11
10 5
4
10
T=0.94 -2
10
-1
10
0
10
1
10
t
2
10
3
10
4
10
12
12 t4
10
0.5t4
max
max
1.6t4
max
SS
χ4 (t)
8 6
0.02t4
3.3t4
max
max
4 0.04t4
2 0.007t4
0 -2 10
-1
10
10
0
6.3t4
max
max
10.2t4
max
1
10
2
t
10
3
10
4
10
max
13
SS
S4 (q,t)
SS
S4 (q,t)
14
12 10 8 6 4 2 10 8 6 4 2 0
(a)
max
0.007t4
max
correlations are growing in time
0.04t4
max
0.2t4
max
0.5t4 max
t4
max
t4 correlations are decaying in time
(b) max
1.6t4
max
3.3t4
max
6.3t4
max
10.2t4
2
4
q
6
8
15
10
6
SS
ξ4 (t)
8
T=0.60 T=0.62 T=0.64 T=0.69 T=0.94
4 2 0 0 10
1
10
2
10
t
10
3
4
10
2 1. Introduction One of the most challenging problems in condensed matter physics is understanding the dynamics and thermodynamics of glass formation [6]. On cooling from high temperature, liquids may crystallize at Tm , or if a liquid is cooled so that crystallization is avoided, it may become supercooled. As a supercooled liquid is further cooled, the particles move more and more slowly, and their motion is slowed down so drastically that, at some low T , particles will not be able to rearrange. This rearrangement is necessary for a liquid to find its equilibrium, and at this temperature particles in a liquid appear to be “frozen” or “jammed”, at least for the time scale of an experiment. At Tg the supercooled liquid is no longer in equilibrium. This is called a glass transition, and the temperature at which this occurs is called the glass transition temperature Tg . An extensive treatment of the dynamics and thermodynamics of supercooled liquids can be found in the textbooks by Debenedetti [7], Harrison [8] and Knight [9]. There are many conference proceedings [10–15] on experimental developments, theoretical advances and the role of computer simulation in the search for a universal theory of supercooled liquids and the glass transition. We are still left with unanswered questions about the dramatic slowing down in the molecular motion as temperature is lowered to Tg , which would not be surprising alone, but this process is accompanied with no significant change in the long-range structure. How is it possible that dynamics changes so dramatically while the structure, as measured by traditional static two-point correlation functions, remains almost unchanged, and what type of transition is the glass transition are some of the crucial questions in the field of glass transition research. To be able to answer those questions, we must fully understand the dynamics of particles on the microscopic level, i.e. search for the patterns in particle motion and relate them to a mechanism for slowing down. The prominent features of supercooled liquids are the non-Arrhenius temperature dependence of the viscosity and structural relaxation time [16], non-exponential character of the structural relaxation time [17], absence of changes in static structural quantities [18–21] and decoupling of the transport coefficients [22–25]. In particular, the non-exponential character of the relaxation of density correlation functions and decoupling of the transport coefficients can be rationalized with the existence of spatially heterogeneous dynamics (SHD) or “dynamical heterogeneity” now well established in experiment and computer simulation [26–30]. Therefore, understanding of the origin of dynamical heterogeneity is directly related to understanding the origin of the glass transition. We refer to a system as dynamically heterogeneous if it is possible to select a dynamically distinguishable subset of particles by experiment or computer simulation [31]. From the theoretical point of view, the question “what is dynamical heterogeneity” still has different answers depending on the theoretical framework used to describe it. Recently, a new theoretical approach to the problem of cooperativity demonstrated how spatially heterogeneous dynamics (SHD) can arise in simple systems with cooperative dynamics. This theory predict a growing correlation length on decreasing T [32]. Here
3 we use a four-point, time-dependent density correlation function formalism to select a dynamically distinguishable subset of particles. In particular, we investigate properties of those particles deemed to be localized. 2. Method and model The simulation method we use to generate data for our analyses is molecular dynamics (MD). This is a widely used method in the investigation of supercooled liquids and glasses that provides static and dynamic properties for a collection of particles. The code we use in our simulations is LAMMPS [33], a parallel MD code based on spatial decomposition parallel technique. We study a 50/50 binary mixture of particle types “A” and “B” that interact via the Lennard-Jones potential "µ
Vαβ (r) = 4²αβ
σαβ r
¶12
µ
σαβ − r
¶6 #
.
(1)
This system has been previously studied by Wahnstrom [34] and Schrøder [35]. Following these authors, we use length parameters σAA = 1, σBB = 5/6, and σAB = (σAA +σBB )/2, and energy parameters ²AA = ²BB = ²AB = 1. The masses of the particles are chosen to be mA = 2 and mB = 1. We shift the potential and truncate it so it vanishes at r = 2.5σAB . We simulate a system of N = 8000 particles using periodic boundary conditions in a cubic box of length L = 18.334 in units of σAA , which yields a density of 1 2 ρ = N/L3 = 1.296 for all state points. We report time in units of τ = (mB σAA /48²AA ) 2 , length in units of σAA , and temperature, T , in units of ²AA /kB , where kB is Boltzmann’s constant. We simulate eight state points at temperatures ranging from T = 2.0 to T = 0.59, following a path similar to that followed in Refs. [2, 35–37]. The simulations are performed in the N V E ensemble. We estimate the mode coupling temperature TMCT = 0.57 ± 0.01, (the glass transition temperature Tg is typically in the range 0.6TM CT < Tg < 0.9TM CT [38]) and the Kauzmann temperature T0 , which can be considered a lower bound for the glass transition temperature Tg , T0 = 0.48 ± 0.02. How we estimate these temperatures and other simulation details can be found in Refs. [4,5]. 3. Background In this section, we briefly review the theoretical framework of the four-point, spatiotemporal density correlation function and corresponding structure factor. Detailed derivation of these quantities can be found in Refs. [1–5]. We consider a liquid of N particles occupying a volume V with density ρ(r, t) = P δ(r − ri (t)), and investigate a quantity Z
Q(t) =
dr1 r2 ρ(r1 , 0)ρ(r2 , t)w(|r1 − r2 |) =
N X N X i=1 j=1
w(|ri (0) − rj (t)|),
(2)
4 which measures the number of particles that, in time t, either remain within a distance a of their original position, or are replaced by another particle (“overlapping particles”). The reason for introducing an “overlap” function w is to eliminate weakly correlated vibrational motion of the particles (for more details see e.g. Ref. [4]). The fluctuation in Q(t) may be defined as βV [hQ(t)2 i − hQ(t)i2 ]. (3) N2 Expressing χ4 (t) in terms of the four-point correlation function G4 (r1 , r2 , r3 , r4 , t), we obtain βV Z χ4 (t) = 2 dr1 dr2 dr3 dr4 G4 (r1 , r2 , r3 , r4 , t), (4) N where χ4 (t) =
G4 (r1 , r2 , r3 , r4 , t) = hρ(r1 , 0)ρ(r2 , t)w(|r1 − r2 |)ρ(r3 , 0)ρ(r4 , t)w(|r3 − r4 |)i − hρ(r1 , 0)ρ(r2 , t)w(|r1 − r2 |)i × hρ(r3 , 0)ρ(r4 , t)w(|r3 − r4 |)i.
(5)
Note that in the case of both the mean-field, p-spin model and a liquid in the hypernetted chain approximation [39–42], the time dependence of χ4 (t) was calculated numerically from an analytic expression in Ref. [1]. Those calculations provide the first analytical prediction of the growth of a generalized dynamical susceptibility and, by inference, a corresponding dynamical correlation length ξ4 (t) in a model glass-forming system. We wish to radially average the four-point correlation function in Eq. (5) to obtain a function g4 (r, t) that depends only on the magnitude r of the distance between two particles at time t = 0. We start from the requirement that Z
χ4 (t) = β and obtain g4 (r, t) = −
drg4 (r, t),
(6)
E 1 DX δ(r − rk (0) + ri (0))w(|ri (0) − rj (t)|)w(|rk (0) − rl (t)|) N ρ ijkl D Q(t) E2
. (7) N Assuming an isotropic, homogeneous system, g4 (r, t) is a function of r = |r| (i.e. g4 (r, t)). Details of above derivation can be found in Refs. [4, 5]. g4 (r, t) describes spatial correlations between overlapping particles separated by a distance r at the initial time (using information at time t to label the overlapping particles). The first term in g4 (r, t) is a pair correlation function restricted to the subset of overlapping particles. The second term represents the probability of any two randomly chosen particles overlapping at times 0 and t. The structure factor S4 (q, t) that corresponds to g4 (r, t) is its Fourier transform Z
S4 (q, t) =
g4 (r, t)exp[−iq · r]dr.
(8)
5 Eq. (8) implies that χ4 (t) lim S (q, t) = . (9) q→0 4 β Eq. (8) is analogous to the static structure factor S(q), but “scatters” off of overlapping particles using information on overlapping particles at time t to label particles at time 0. 3.1. Self and distinct contributions to Q(t), χ4 (t), g4 (r, t), and S4 (q, t) The contribution of a given particle i to Q(t) is a result of three possible events: (i) particle i remains within a distance a of its original position; (ii) particle i moves and is replaced (within a distance a) by another particle; or (iii) particle i moves a distance greater than a and is not replaced by another particle. Case (iii) does not count as an overlap, and thus does not contribute to Q(t). Cases (i) and (ii) count as overlaps and contribute to the value of Q(t). Case (ii) and (iii) belong to the set of delocalized particles. However, the case (i) and case (ii) clearly represent two very different physical situations. To elucidate the various contributions to the four-point correlation function, we separate Q into self and distinct components, Q(t) = QS (t) + QD (t) =
N X
w(|ri (t) − ri (0)|)
i=1
+
N X N X
w(|ri (0) − rj (t)|).
(10)
i=1 i6=j
The self part, QS (t), measures the number of particles that move less than a distance a in a time interval t; we call these “localized” particles. The distinct part, QD (t) measures the number of particles replaced within a radius a by another particle in time t; we call these “replaced” particles. Following the scheme of decomposing Q(t), χ4 (t) can be decomposed into self χSS (t), distinct χDD (t), and cross χSD (t) terms: χ4 (t) = χSS (t)+χDD (t)+χSD (t), where χSS (t) ∝ hQ2S (t)i−hQS (t)i2 , χDD (t) ∝ hQ2D (t)i−hQD (t)i2 , and χSD (t) ∝ hQS (t)QD (t)i− hQS (t)ihQD (t)i. Thus χSS (t) is the susceptibility arising from fluctuations in the number of localized particles, χDD (t) is the susceptibility arising from fluctuations in the number of particles that are replaced by a neighboring particle, and χSD (t) represents cross fluctuations between the number of localized and replaced particles. We also consider “delocalized” particles, that is, particles that in a time t are more than a distance a from their original location. As was pointed out in Ref. [2], substituting 1 − w for w in Eq. (2) gives the delocalized order parameter QDL (t) = N − QS (t), and as a result, χDL (t) ≡ χSS (t). Likewise, we can find terms in g4 (r, t) and S4 (q, t) that correspond to localized, replaced and delocalized particles, e.g. g4 (r, t) and S4 (q, t) of localized particles correspond to g4SS (r, t) and S4SS (q, t) and for i = j and l = k in Eq. (7) and Eq. (8), respectively. In the next section we present numerical results for S4SS (q, t) and corresponding correlation length ξ4SS (t).
6 4. Results Figure 1 shows the time and temperature dependence of Q(t) and χ4 (t) and their terms described in Subsection 3.1. Figure 1 shows that for all sufficiently low T , hQ(t)/N i and hQS (t)/N i, are characterized by a two-step relaxation, commonly observed in the intermediate scattering function [26], as a result of the transient caging of particles. hQDL (t)/N i has the opposite time dependence from hQS (t)/N i due to the fact that it measures the number of particles that moved a distance greater than a. The same applies to hQD (t)/N i since those particles constitute the subset of delocalized particles. At short times, particles oscillate in a region smaller than the overlap radius a, and so hQ(t)/N i = 1 and hQS (t)/N i = 1. We observe a short, initial relaxation of Q(t) and QS (t), and a longer, secondary relaxation. χ4 (t), χSS (t), and χDL (t) are zero at short time, attain a maximum at some intermediate time tmax , and decay at long time to zero 4 in the thermodynamic limit. χ4 (t) and its terms measure the correlated motion between pairs of particles, calculated equivalently from fluctuations in the number of localized, replaced and delocalized particles or from the corresponding four-point correlation functions. The behavior of χ4 (t) demonstrates that correlations are time dependent, with a maximum at a time tmax . Similar behavior was reported for the same and other model liquids in 4 Refs. [2, 43, 44] for a generalized susceptibility related to a displacement-displacement correlation function χU (t), which measures the correlations between displacements of particles as a function of time. In these works, SHD was observed to be most pronounced in the α-relaxation regime. We find that the correlations measured by χ4 (t) are also most pronounced in the α-relaxation regime (see e.g. Ref. [4]). Figure 2 shows the time dependence of χSS (t) at T = 0.62. There are nine points marked as open circles that correspond to the times at which we show localized and delocalized particles in Figure 3. The four-point structure factor of localized particles S4SS (q, t) calculated from Eq. (8) is plotted vs q in Figure 4, at T = 0.60. We also find that at very early i = 1) S4SS (q, t) = S(q). We find that while S(q) shows no change at times (when h Q(t) N small q (see e.g the static structure factor in Ref. [3]), S4SS (q, t) develops a peak at small q which grows (Figure 4(a)) and decays in time (Figure 4(b)), indicating the presence of long-range correlations in the locations of overlapping particles. Inspired by the Ornstein-Zernike theory, OZT [45], which describes, e.g., density fluctuations near a liquid-gas transition, we use the following function, S4SS (q, t) =
S4SS (0, t) , (1 + (qξ4SS (t))2 )
(11)
where S4SS (0, t) and ξ4SS (t) are fitting parameters. The fitting was performed using an interior-reflective Newton method in Matlab, and setting the termination tolerance of the function value to 0.1. We find a good fit to the data in the q range from q = 0.34 to q = 1.9, for each T and time. The observed narrowing of the peak directly reveals the growing range of g4SS (r, t) with decreasing T .
7 The time and temperature dependence of ξ4SS (t) obtained from this fit is plotted for several state points in Figure 5. We see that the qualitative behavior of ξ4SS (t) is similar SS to that of χSS 4 (t): ξ4 (t) has a maximum in time that coincides with the maximum in χSS 4 (t), and as T decreases, the amplitude and time of this maximum increase. The highest values of ξ4SS (t) for T = 0.60 exceed half the simulation box size. The fit at these points depends strongly on the number of points used, initial parameter guesses, and other details and can yield large values (e.g. > 40) depending on these details. Since these values greatly exceed the range over which we can meaningfully interpret the resulting correlation length, we make no attempt to rigorously define the upper error bounds at these points, but the data is well bounded from below. The fits at all other points and temperatures are well constrained. The length scale ξ4SS (t) characterizes the typical distance over which localized particles are spatially correlated. 5. Discussion In this paper, we have focused on a four-point, time-dependent density correlation function g4SS (r, t) and corresponding time dependent structure factor S4SS (q, t), and demonstrated that those functions are sensitive to dynamical heterogeneity in a model glass-forming liquid. As derived in previous works [1–3,46,47], this correlation function is related to an order parameter Q(t) corresponding to the number of ”overlapping” particles in a time window t, where the term ”overlap” is used to denote a particle that was either localized or replaced in a time t. We calculated the correlation length of localized particles ξ4SS (t), characterizing the range of g4SS (r, t), and showed that it depends on time, and attains its maximum value in the α-relaxation regime. We also showed that this maximum grows to exceed half of the simulation box size, close to TMCT . This length scale characterizes the typical size of dynamically homogeneous domains. The characteristic length scale calculated here is related to length scales calculated from the displacement-displacement correlation function [44], cluster size [48], and other measures of correlated particle motion and dynamical heterogeneity [49–51]. ξ4SS (t) is essentially the same as that obtained by considering the delocalized particles (the set of particles that in any time window t move beyond a distance a) due to the mathematical identity between χ4 for localized and delocalized particles. This suggests a picture of fluctuating domains of temporarily localized and delocalized particles, perhaps similar to that proposed by Stillinger and Hodgedon [24]. Finally, we note that all quantities presented here can be measured in dense colloidal suspensions using confocal microscopy studies [52, 53]. 6. References [1] S. Franz, C. Donati, G. Parisi, and S. C. Glotzer, Philos. Mag. B 79 1827 (1999); C. Donati, S. Franz, S. C. Glotzer, and G. Parisi, Journal of Non-Crystalline Solids 307, 215 (2002). [2] S. C. Glotzer, V. N. Novikov, and T. B. Schrøder, Journal of Chemical Physics 112, 509 (2000).
8 [3] N. Laˇcevi´c, F. W. Starr, T. B. Schrøder, et al., Physical Review E 66, 030101 (2002). [4] N. Laˇcevi´c, F. W. Starr, T. B. Schrøder and S. C. Glotzer submitted to Journal of Chemical Physics. [5] N. Laˇcevi´c, “Dynamical heterogeneity in simulated glass-forming liquids studied via a four-point spatiotemporal density correlation function”, dissertation, The Johns Hopkins University, (2003). [6] H. Weintraub, M. Ashburner, P. N. Goodfellow, et al., Science 267, 1609 (1995). [7] P. G. Debenenedetti, Metastable Liquids: Concepts and principles, Princeton University Press, Princeton, N. J. (1996). [8] G. Harrison, Dynamics Properties of Supercooled Liquids, Academic Press, London (1976). [9] C. Knight, The freezing of supercooled liquids, Princeton N.J.: Published for the Commission on College Physics by Van Nostrand (1967). [10] J. T. Fourkas, editor, Supercooled liquids: advances and novel applications, ACS symposium series, 0097-6156; 676, American Chemical Society, Washington DC (1997). [11] S. C. Glotzer, editor, Glasses and the glass transition challenges in materials theory and simulation, Computational Materials Science, 4 (4) 1995. [12] S. Franz, S. C. Glotzer, and S. Sastry, editors, Special issue containing articles from the ICTP-NIS Conference on “Unifying concepts in glass physics”, Journal of Physics-Condensed Matter 12, (2000). [13] M. Giordano, D. Leporini and M. P. Tosi, editors, Non equilibrium phenomena in supercooled fluids, glasses and amorphous materials, World Science, Singapore; River Edge, NJ (1996). [14] K. J. Strandburg, editor; foreword by D. R. Nelson, Bond-orientational order in condensed matter systems, Springer-Verlag, New York (1992). [15] A. J. Liu and S. R. Nagel, editors, Jamming and rheology: constrained dynamics on microscopic and macroscopic scales, Taylor and Francis, London; New York (2001). [16] D. J. Ferry, Viscoelastic properties of polymers, John Wiley, New York, (1980). [17] P. K. Dixon, L. Wu, S. R. Nagel, et al., Physical Review Letters 65, 1108 (1990). [18] A. Tolle, H. Schober, J. Wuttke, et al., Physical Review E 56, 809 (1997). [19] B. Frick, D. Richter, and C. Ritter, Europhysics Letters 9, 557 (1989). [20] E. Kartini, M. F. Collins, B. Collier, et al., Physical Review B 54, 6292 (1996). [21] R. L. Leheny, N. Menon, S. R. Nagel, et al., Journal of Chemical Physics 105, 7783 (1996). [22] D. D. Deppe, R. D. Miller, and J. M. Torkelson, Journal of Polymer Science Part B-Polymer Physics 34, 2987 (1996). [23] D. B. Hall, A. Dhinojwala, and J. M. Torkelson, Physical Review Letters 79, 103 (1997). [24] J. A. Hodgdon and F. H. Stillinger, Physical Review E 48, 207 (1993). [25] F. H. Stillinger and J. A. Hodgdon, Physical Review E 50, 2064 (1994). [26] H. Sillescu, Journal of Non-Crystalline Solids 243, 81 (1999). [27] S. C. Glotzer, Journal of Non-Crystalline Solids 274, 342 (2000). [28] M. D. Ediger, Annual Review of Physical Chemistry 51, 99 (2000). [29] R. Bohmer, Current Opinion in Solid State and Materials Science 3, 378 (1998). [30] R. Richert, Journal of Physics-Condensed Matter 14, R703 (2002). [31] R. Bohmer, R. V. Chamberlin, G. Diezemann, et al., Journal of Non-Crystalline Solids 235, 1 (1998). [32] J. P. Garrahan and D. Chandler, Physical Review Letters 89, 03570 (2002). [33] Steve Plimpton, Sandia National Labs, www.cs.sandia.gov/∼sjplimp [34] G. Wahnstrom, Physical Review A 44, 3752 (1991). [35] T. B. Schrøder, Hopping in Disordered Media: A Model Glass Former and A Hopping Model, cond-mat/0005127. [36] T. B. Schrøder, S. Sastry, J. C. Dyre, et al., Journal of Chemical Physics 112, 9834 (2000). [37] T. B. Schrøder and J. C. Dyre, Journal of Non-Crystalline Solids 235, 331 (1998). [38] V. N. Novikov and A. P. Sokolov, Physical Review E 67, 031507 (2003). [39] S. Franz and G. Parisi, J. Phys. Cond. Matt. 12, 6335 (2000). [40] T. R. Kirkpatrick and P. G. Wolynes, Phys. Rev. A 35, 3072 (1987); 36 852 (1987); T. R.
9
[41] [42] [43] [44]
[45] [46] [47] [48] [49] [50] [51] [52] [53]
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10 Figure captions
Figure 1. Temperature and time dependence of hQ(t)/N i, hQS (t)/N i, hQD (t)/N i, hQDL (t)/N i, χ4 (t), χSS (t), χDD (t) and χDL (t) ≡ χSS (t).
Figure 2. Time dependence of χSS 4 (t) at T = 0.62. Times that are encircled and labeled with appropriate fraction of tmax correspond to the snapshots in Figure 3. 4
Figure 3. Localized (white) and delocalized (gray) particles at times 0.007tmax , 4 max max max max max max max 0.04tmax , 0.2t , 0.5t , t , 1.6t , 3.3t , 6.3t , 10.2t . 4 4 4 4 4 4 4 4
Figure 4. Time dependence of S4SS (q, t) at T = 0.62. S4SS (q, t) is shown at times identical to those shown for g4 (r, t) in Figure 2. Note that the height of the first diffraction peak in S4SS (q, t) decreases monotonically as a function of time. This is because it depends on the number of localized particles, which also decreases monotonically in time (see Figure 1).
Figure 5. Time and temperature dependence of ξ4SS (t) obtained from the fits to Eq. (11). The data shown are smoothed over successive groups of five points.
20 χ4(t)
T=0.60
15 10 5
T=0.94 T=0.60
15 SS
T=0.94
χ (t)
T=0.60
10 5
T=0.94
0.1 0.05
DD
0.8 0.6 0.4 0.2
T=0.94
χ (t)
0.15
T=0.60
T=0.60
χ (t)
T=0.94 T=0.60 -2
10
-1
10
0
10
1
10
t
2
10
3
10
T=0.94
0.5 0.4 0.3 0.2 0.1
T=0.60 T=0.94 T=0.60
15 DL
0.8 0.6 0.4 0.2
1 0.8 0.6 0.4 0.2
11
10 5
4
10
T=0.94 -2
10
-1
10
0
10
1
10
t
2
10
3
10
4
10
12
12 t4
10
0.5t4
max
max
1.6t4
max
SS
χ4 (t)
8 6
0.02t4
3.3t4
max
max
4 0.04t4
2 0.007t4
0 -2 10
-1
10
10
0
6.3t4
max
max
10.2t4
max
1
10
2
t
10
3
10
4
10
max
13
SS
S4 (q,t)
SS
S4 (q,t)
14
12 10 8 6 4 2 10 8 6 4 2 0
(a)
max
0.007t4
max
correlations are growing in time
0.04t4
max
0.2t4
max
0.5t4 max
t4
max
t4 correlations are decaying in time
(b) max
1.6t4
max
3.3t4
max
6.3t4
max
10.2t4
2
4
q
6
8
15
10
6
SS
ξ4 (t)
8
T=0.60 T=0.62 T=0.64 T=0.69 T=0.94
4 2 0 0 10
1
10
2
10
t
10
3
4
10
