Phase diagram of Gaussian-core nematics
Phase diagram of Gaussian-core nematics
Santi Prestipino1, ∗ and Franz Saija2, †
Università degli Studi di Messina, Dipartimento di Fisica, Contrada Papardo, 98166 Messina, Italy
Istituto per i Processi Chimico-Fisici del CNR, Sezione di Messina, Via La Farina 237, 98123 Messina, Italy
(Dated: November 4, 2018)
We study a simple model of a nematic liquid crystal made of parallel ellipsoidal particles interact-
ing via a repulsive Gaussian law. After identifying the relevant solid phases of the system through
a careful zero-temperature scrutiny of as many as eleven candidate crystal structures, we determine
the melting temperature for various pressure values, also with the help of exact free energy calcu-
lations. Among the prominent features of this model are pressure-driven reentrant melting and the
stabilization of a columnar phase for intermediate temperatures.
PACS numbers: 05.20.Jj, 61.20.Ja, 61.30.-v, 64.70.Md
Keywords: Liquid crystals; columnar phase; solid-liquid and solid-solid transitions; isothermal-isobaric Monte
Carlo; exact free-energy calculations
INTRODUCTION
Since five decades now, numerical simulation has im-
posed as an invaluable tool for the determination of equi-
librium statistical properties of many-particle systems.
Despite a long history, however, a precise numerical eval-
uation of the Helmholtz free energy of a simple model
fluid in its solid phase has resisted all attacks for many
years until, in a remarkable 1984 paper [1], Frenkel and
Ladd showed how a reference Einstein solid can be used
right to this purpose. Since then, it has become possi-
ble to trace a numerically accurate and complete equi-
librium phase diagram for simple-fluid systems by Monte
Carlo simulation methods. The only real limitation of
the Frenkel-Ladd method is the necessity of a preliminary
identification of all relevant solid structures. Depending
on the complexity of the model potential, some struc-
ture could be skipped, neither it does necessarily show
up spontaneously in the simulation due to the effective
fragmentation of the system phase space into inescapable
ergodic basins.
In a series of papers [2, 3, 4], we have employed
the Frenkel-Ladd technique in combination with the
standard thermodynamic-integration method in order to
trace the phase diagram of some reference simple-fluid
models. In particular, we have provided the first accu-
rate determination of the phase diagram for the so-called
Gaussian-core model, which is meant to describe dilute
solutions of polymer coils [5, 6]. The thermodynamics
of this model is ruled by the competition between the
fluid and two different, body-centered-cubic (BCC) and
face-centered-cubic (FCC), crystal structures; its pecu-
liar features are reentrant melting by isothermal com-
pression and, in a narrow range of temperatures, BCC
reentrance in the solid sector.
Following earlier simulational work by Frenkel and col-
laborators on hard ellipsoids and spherocylinders [7, 8, 9,
10], as well as by other authors on hard dumbbells [11],
we aim here to provide another demonstration of the use
of simulation for the description of thermodynamic prop-
erties of elongated particles. Such molecules can exist in a
number of partially-ordered mesophases with long-range
orientational order, possibly in combination with one- or
two-dimensional translational order (as in smectic and
in columnar liquid crystals, respectively) [12, 13]. Liquid
crystals do also usually give rise to numerous solid phases
which, as a rule, can hardly be anticipated from just a
glance at the interaction potential between the molecules.
Very recently, an interesting liquid-crystal model was
introduced by de Miguel and Martin del Rio [14] whose
phase diagram shows a stable smectic phase as well as
pressure-driven reentrance of the nematic phase. The
model consists of equally-oriented hard ellipsoids that are
further equipped with an attractive spherical well (there
is no isotropic phase in this model since the particles
are artificially constrained to stay parallel to each other,
hence the fluid phase is a nematic liquid crystal). Ini-
tially, we thought of this model as an ideal candidate for
a complete reconstruction of the phase diagram. Unfor-
tunately, the model potential turns out to be not simple
enough to allow for a straightforward identification of
the structure of its solid phase(s) and, in this respect,
the original paper is in fact reticent. We have made
an attempt to resolve the solid structure in terms of
stretched cubic lattices but a direct inspection of many
equilibrated solid configurations reveals more complicate,
yet periodically repeated patterns. Probably, this results
from a difficult matching between the optimization re-
quirements of the different pair-potential components,
i.e., a cylindrically-symmetric hard-core repulsion and a
spherically-symmetric step-like attraction.
To retain nematic reentrance and, possibly, also the
smectic phase, we have considered a more tractable test
case, that is a uniaxial deformation of the repulsive Gaus-
sian potential, which we expect to provide a model ne-
matic fluid whose phase diagram can fully be worked out,
also in its solid region. It can plausibly be argued on sym-
metry grounds and also expected from the smoothness of
http://arxiv.org/abs/0704.0056v1
the potential that all solid phases of the model will now
be found within the class of uniaxially-stretched cubic
crystals.
The rest of the paper is organized as follows: In Sec-
tion II, we present our liquid-crystal model together with
a catalogue of crystal structures that are possibly rele-
vant to it. Next, in Section III, we outline the numer-
ical methods by which the phase diagram of the model
is being drawn. Results are exposed in Section IV while
further comments and conclusions are deferred to Section
MODEL
We consider a nematic fluid of N parallel ellipsoids
of revolution whose geometric boundaries are smeared
out by a pair interaction u that smoothly depends on
the ratio between the center-to-center distance r and the
“contact distance” σ, which is the distance of closest ap-
proach in case of sharp boundaries. σ is a function of the
angle θ that the ray r joining the two molecular centers
forms with the direction ẑ of the axis of revolution. Its
closed-form expression is easily found to be:
σ(θ) =
L2 sin2 θ +D2 cos2 θ
, (2.1)
D and L being the transversal (with respect to ẑ) and the
longitudinal diameter respectively (we hereafter consider
only the prolate case L > D). For uniaxial particles, the
functional dependence of σ is actually on cos θ, as ex-
emplified by Eq. (2.1). We also note that hard ellipsoids
do correspond to an interaction strenght being +∞ for
r < σ(θ) and zero otherwise.
For the efficiency of numerical calculation, sufficiently
short-range interaction in all directions is highly desir-
able and, among smooth interactions, a good choice is a
Gaussian-decaying two-body repulsion,
u(r, θ) = ǫ exp
σ(θ)2
, (2.2)
ǫ > 0 being an arbitrary energy scale. Eq. (2.2) defines
the Gaussian-core nematic (GCN) fluid. It is evident
that, upon increasing the aspect ratio L/D, larger and
larger system sizes are needed in order to pull down any
rounding-off error that is implicit e.g. in the numerical
calculation of the total energy.
Another crucial quantity to determine in a simulation
is the pressure. For a V -volume system of N parallel
ellipsoids in contact with a heat bath at temperature
T , the equilibrium pressure P can be calculated from
a virial theorem that generalizes the one valid for a sim-
ple fluid. Let the total potential energy of the system
be of the general form U =
i<j u(|Ri − Rj |, cos θij),
where Ri is the center-of-mass position of particle i and
cos θij = (Ri−Rj)·ẑ. Upon switching to scaled V −1/3Ri
coordinates, one readily gets:
P = kBTρ−
1(Rij , cos θij)
, (2.3)
where u′1 is the u derivative with respect to its first
argument, ρ = N/V is the (number) density, and kB
is Boltzmann’s constant. Clearly, 〈. . .〉 is a canonical-
ensemble average. Upon introducing the T - and ρ-
dependent, two-body distribution function g2(R1,R2) =
g(|R1−R2|, cos θ12), the system pressure can also be ex-
pressed as
P = kBTρ−
dr r3g(r, τ)u′1(r, τ) . (2.4)
In particular, for a system of hard ellipsoids the pressure
reads:
P = kBTρ+
dτ σ(τ)3g(σ(τ)+, τ) . (2.5)
Anyway, a practical implementation of Eq. (2.4) or (2.5)
in a simulation requires a precise evaluation of the two-
argument function g which, ordinarily, is a difficult task
to accomplish with negligible statistical errors. A much
better solution is to switch to the isothermal-isobaric en-
semble, by simulating the system under constant-T and
constant-P conditions, see Section III.
As was mentioned in the Introduction, one main in-
convenience of liquid-crystal simulations is the correct
identification of the solid phase(s) of the system, since
a plethora of such phases are conceivable and there is
no unfailing criterion for choosing those that are really
relevant to the specific model under investigation. The
actual importance of a given crystal phase can only be
judged a posteriori, after proving its mechanical stability
in a long simulation run and, ultimately, on the basis of
the calculation of its Gibbs free energy, but nothing can
nevertheless ensure that no important phase was skipped.
Besides these vague indications, we adopted a more strin-
gent test in order to select the phases for which it is worth
performing the numerically-expensive calculation of the
free energy. With specific reference to the model (2.2),
we did a comprehensive T = 0 study of the chemical po-
tential µ as a function of the pressure for many stretched
cubic and hexagonal phases, in such a way as to iden-
tify the stable ground states and leave out from further
consideration all solids with a very large µ at zero tem-
perature. In fact, it is unlikely that such phases can ever
play a role for the thermodynamics at non-zero temper-
atures.
For the interaction potential describing the GCN
model, we surmise that all of its stable crystal phases
are to be sought among the structures obtained from
the common cubic and hexagonal lattices by a suit-
able stretching along a high-symmetry crystal axis, with
optimal stretching ratios α that are probably close to
L/D. Take e.g. the case of BCC. We can stretch
it along [001], [110], or [111], this way defining new
BCC001(α), BCC110(α), and BCC111(α) lattices (the
number within parentheses is the stretching ratio; for in-
stance, BCC001(2) is a BCC crystal whose unit cell has
been expanded by a factor of 2 along ẑ). The same can
be done with the simple-cubic (SC) and FCC structures.
We further consider hexagonal-close-packed (HCP) and
simple-hexagonal (SH) lattices that are stretched along
[111], this way arriving at a total of eleven potentially
relevant crystal phases.
METHOD
For fixed T and P values, the most stable of several
thermodynamic phases is the one with lowest chemical
potential µ (Gibbs free energy per particle). At T =
0, only crystal phases are involved in this competition
and, once a list of relevant phases has been compiled,
searching for the optimal one at a given P becomes a
simple computational exercise. An exact property of the
Gaussian-core model (which is the L/D = 1 limit of the
GCN model) is that, on increasing pressure, the BCC
crystal takes over the FCC crystal at P ∗ ≡ PD3/ǫ ≃
0.055 [3]. Hence, in the GCN model with L/D > 1 a
leading role is naturally expected for the stretched FCC
and BCC crystals.
For an assigned crystal structure, we calculate the T =
0 chemical potential µ(P ) of the GCN model for a given
pressure P by adjusting the stretching ratio α(P ) and the
density ρ(P ) until the minimum of (U+PV )/N is found.
Once the profile of µ as a function of P is known for each
structure, it is straightforward to draw the T = 0 phase
diagram for the given L/D.
The known thermodynamic behavior at zero tempera-
ture provides the general framework for the further simu-
lational study at non-zero temperatures. In fact, it is safe
to say that the same crystals that are stable at T = 0
also give the underlying lattice structure for the stable
solid phases at T > 0. As we shall see in more detail in
the next Section, the only complication is the existence
of three degenerate T = 0 structures for not too small
pressures, which obliged us to consider each of them as a
potentially relevant low-temperature GCN phase.
We perform a Monte Carlo (MC) simulation of the
GCN model with L/D = 3 in the isothermal-isobaric
ensemble, using the standard Metropolis algorithm with
periodic boundary conditions and the nearest-image con-
vention. For the solid phase, four different types of
lattices are considered, namely FCC001(3), BCC110(3),
BCC111(3), and BCC001(3) (see Section IV). The num-
ber of particles in a given direction is chosen so as to guar-
antee a negligible contribution to the interaction energy
from pairs of particles separated by half a simulation-
box length in that direction. More precisely, our samples
consist of 10× 20× 8 = 1600 particles in the FCC001(3)
phase, of 8 × 24 × 6 = 1152 particles in the fluid and in
the solid BCC110(3) phase, of 10× 12× 18 = 2160 parti-
cles in the BCC111(3) phase, and of 12× 12× 10 = 1440
particles in the BCC001(3) phase. Considering the large
system sizes employed, we made no attempt to extrapo-
late our finite-size results to infinity.
At given T and P , equilibration of the sample typically
took a few thousand MC sweeps, a sweep consisting of
one average attempt per particle to change its center-of-
mass position plus one average attempt to change the
volume by a isotropic rescaling of particle coordinates.
The maximum random displacement of a particle and
the maximum volume change in a trial MC move are ad-
justed once a sweep during the run so as to keep the
acceptance ratio of moves close to 50% and 40%, respec-
tively. While the above setup is sufficient when simu-
lating a (nematic) fluid system, it could have harmful
consequences on the sampling of a solid state to operate
with a fixed box shape since this would not allow the
system to release the residual stress. That is why, after a
first rough optimization with a fixed box shape, the equi-
librium MC trajectory of a solid state is generated with a
modified (so called constant-stress) Metropolis algorithm
which makes it possible to adjust the length of the vari-
ous sides of the box independently from each other (see
e.g. [8]). Ordinarily, however, the simulation box will de-
viate only very little from its original shape. When the
opposite occurs, this indicates a mechanic instability of
the solid in favor of the fluid, hence it gives a clue as to
where melting is located. We note that MC simulations
with a varying box shape are not well suited for the fluid
phase since in this case one side of the box usually be-
comes much larger or smaller than the other two, a fact
that seriously prejudicates the reliability of the simula-
tion results.
In order to locate the melting point for a given pres-
sure, we generate separate sequences of simulation runs,
starting from the cold solid on one side and from the hot
fluid on the other side. The last configuration produced
in a given run is taken to be the first of the next run at a
slightly different temperature. The starting configuration
of a “solid” chain of runs was always a perfect crystal with
α = 3 and a density equal to its T = 0 value. Usually,
this series of runs is carried on until a sudden change is
observed in the difference between the energies/volumes
of solid and fluid, so as to prevent us from averaging over
heterogeneous thermodynamic states. Thermodynamic
averages are computed over trajectories 104 sweeps long.
Much longer trajectories are constructed for estimating
the chemical potential of the fluid (see below).
Estimating statistical errors is a critical issue whenever
different candidate solid structures so closely compete for
thermodynamic stability. To this aim, we divide the MC
trajectory into ten blocks and estimate the length of the
error bars to be twice as large as the standard deviation of
the block averages. Typically, the relative errors affecting
the energy and the volume of the fluid are found to be
very small, a few hundredths percent at the most (for a
solid, they are even smaller).
A more direct clue about the nature of the phase(s)
expressed by the system for intermediate temperatures
can be got from a careful monitoring across the state
space of a “smectic” order parameter (OP) and of two
different, transversal and longitudinal (with respect to ẑ)
distribution functions (DFs). The smectic OP is defined
τ(λ) =
. (3.1)
This quantity is able to notice the existence of a layered
structure along ẑ in the system, be it solid-like or smectic-
like. In particular, the λ at which τ takes its largest value
gives the nominal distance λmax between the layers. A
large value of τ at λmax signals a strong layering along z
with period λmax. In order to discriminate between solid
and smectic (fluid) layers, we can rely on the in-plane
DF g⊥(r⊥), with r⊥ = r − (r · ẑ)ẑ, which informs on
how much rapid is the decay of crystal-like spatial corre-
lations in directions perpendicular to ẑ. The persistence
of crystal order along ẑ is measured through another DF,
g‖(z), which gives similar indications as τ(λ). A liquid-
like profile of g⊥ along with a sharply peaked τ or g‖ will
be faithful indication of a smectic phase. Conversely,
a sharply peaked g⊥ along with a structureless g‖ will
be the imprints of a columnar phase. Both g⊥(r⊥) and
g‖(z) are normalized in such a way as to approach 1 at
large distances in case of fully disordered center-of-mass
distributions in the respective directions. Slight devia-
tions from this asymptotic value may occur as a result
of the variation of box sidelengths during a simulation
run. The two DFs were constructed with a spatial reso-
lution of ∆r⊥ = D/20 and ∆z = L/20 respectively, and
updated every 10 MC sweeps.
We compute the difference in chemical potential be-
tween any two equilibrium states of the system – say,
1 and 2 – within the same phase (or even in different
phases, provided they are separated by a second-order
boundary) by the standard thermodynamic-integration
method as adapted to the isothermal-isobaric ensemble,
i.e., via the combined use of the formulas:
µ(T, P2)− µ(T, P1) =
dP v(T, P ) (3.2)
µ(T2, P )
µ(T1, P )
u(T, P ) + Pv(T, P )
(3.3)
To prove really useful, however, the above equations re-
quire an independent estimate of µ for at least one ref-
erence state in each phase. For the fluid, a reference
state can be any state characterized by a very small den-
sity (a nearly ideal gas), since then the excess chemical
potential can be estimated accurately through Widom’s
particle-insertion method [15]. The use of this technique
for small but finite densities avoids the otherwise neces-
sary extrapolation to the ideal gas limit as a reference
state for thermodynamic integration.
In order to calculate the excess Helmholtz free energy
of a solid, we resort to the method proposed by Frenkel
and Ladd [1], based on a different kind of thermody-
namic integration (see Ref. [4] for a full description of this
method and of its implementation on a computer). We
note that the ellipsoidal symmetry of the GCN particles
is not a complication at all, since the particle axes are
frozen and the only degrees of freedom been left are the
centers of mass. The solid excess Helmholtz free energy is
calculated through a series of NV T simulation runs, i.e.,
for fixed density and temperature. As far as the density
is concerned, its value is chosen in a way such that com-
plies with the pressure of the low-temperature reference
state, that is the one from which the NPT sequence of
runs is started. We wish to emphasize that, thanks to the
large sample sizes employed, the density histogram in a
NPT run always turned out to be sharply peaked, indi-
cating very limited density fluctuations (hence, negligible
ensemble dependence of statistical averages).
RESULTS
Zero-temperature calculations
For various L/D values in the interval between 1.1
and 3, we have calculated the T = 0 chemical poten-
tial µ(P ) for our eleven candidate ground states, with
P ranging from 0 to 0.20. We report in Table 1 the
results relative to L/D = 3 for two values of P , 0.05
and 0.20. An emergent aspect of this Table is the exis-
tence of a rich degeneracy that is only partly a result of
the effective identity of crystal structures up to a dila-
tion. Take e.g. the five structures with the minimum µ
(and with the same density). While the BCC001 lattice
with α = 3 is obtained from the FCC001 lattice with
α = 3/
2 = 2.12 . . . by a simple
2 dilation, there is
no homothety transforming BCC001(3) into BCC110(3)
or into BCC111(3) (in turn equivalent to SC111(1.5)):
Points in these three lattices have different local envi-
ronments, as can be checked by counting the nth-order
neighbors for n up to 4, yet the three stretched BCC crys-
tals of minimum µ share the same U/N . Also the pairs
FCC110(3), FCC111(3) and SC001(3), SC110(3) consist
of topologically-different degenerate structures. This fact
is an emergent phenomenon whose deep reason remains
unclear to us; it should deal with the dependence of u on
the ratio r/σ(θ), since the same symmetry holds with a
polynomial, rather than Gaussian, dependence.
For the case of L/D = 3, we show in Fig. 1 the over-
all P dependence at T = 0 of the chemical potential µ
for the various solids. The solid with the minimum µ is
either of the type FCC001 (with α = 3) or, say, of the
type BCC001 (with α = 3), a fact that holds true, but
with α = L/D, for all 1 < L/D < 3. Other solids are
definitely ruled out, and the same will probably hold for
T > 0. On increasing L/D, the transition from a FCC-
type to a BCC-type phase occurs at a lower and lower
pressure, whose reduced value is slightly less than 0.02
for L/D = 3.
Monte Carlo simulation
In order to investigate the thermodynamic behavior of
the GCN model at non-zero temperatures, we have car-
ried out a number of MC simulation runs for a GCN
system with L/D = 3, which is the system with the
strongest liquid-crystalline features that we can still man-
age numerically.
We have effected scans of the phase diagram for six
different pressure values, P ∗ = 0.01, 0.02, 0.03, 0.05, 0.12,
and 0.20. With all probability, FCC001(3) is the sta-
ble system phase only in a very small pocket of T -P
plane nearby the origin. However, we decided not to
embark on a free-energy study of the relative stability of
fluid, FCC001(3), and BCC-type phases at such low pres-
sures since this would require a numerical accuracy that
is beyond our capabilities. To a first approximation, the
boundary line between FCC001(3) and, say, BCC111(3)
can be assumed to run at constant pressure. For relating
data obtained at different pressures, we have carried out
two further sequences of MC runs along the isothermal
paths for T ∗ = 0.002 (solids) and T ∗ = 0.015 (fluid).
The Frenkel-Ladd computation of the excess
Helmholtz free energy per particle fex confirms that
the BCC001(3), BCC110(3), and BCC111(3) solids
are nearly degenerate at low temperature. We take
T ∗ = 0.002, P ∗ = 0.05 as a reference state for the
calculation of solid free energies. With the density
fixed at ρ = 0.08562D−3, in every case corresponding
to P ∗ = 0.05, we find βfex = 144.461(2), 144.470(2),
and 144.453(3), for the three above solids respectively,
implying a weak preference for the BCC111(3) phase.
Then, using thermodynamic integration along the
T ∗ = 0.002 isotherm (see Eq. (3.2)), we have studied
the relative stability of the three solids as a function
of pressure, up to P ∗ = 0.20. The results, depicted
in Fig. 2, suggest that BCC111(3) is the stable phase
throughout the low-temperature region, the other solids
being very good solutions anyway with near-optimal
chemical potentials.
We then follow the thermal disordering of the BCC-
type solids for fixed pressure (with three cases consid-
ered, P ∗ = 0.05, 0.12, and 0.20) through sequences of
isothermal-isobaric runs, all starting from T ∗ = 0.002,
with steps of 0.001. Any such sequence is stopped when
the values of potential energy and specific volume have
collapsed onto those of the fluid, thus informing that the
ultimate bounds of solid stability are reached (usually, a
solid can hardly be overheated). The stability thresholds
detected this way are fairly consistent with the indica-
tion coming from the DF profiles which, upon increas-
ing temperature, will eventually show a fluid-like appear-
ance. Thermodynamic integration (see Eq. (3.3)) is used
to propagate the calculated µ for T ∗ = 0.002 to higher
temperatures.
As far as the (nematic) fluid is concerned, we have
first generated a sequence of NPT simulation runs for
P ∗ = 0.05, starting from T ∗ = 0.015. At this initial
point, the excess chemical potential µex was estimated
by Widom’s insertion method, obtaining µex = 0.986(5).
It is worth noting that, in a long simulation run of as
many as 5×104 MC sweeps at equilibrium, the chemical-
potential value relaxed very soon, with small fluctuations
around the average and no significant drift observed. Our
analysis of the fluid phase is completed by further sim-
ulation runs along the isobaric paths for P ∗ = 0.12 and
0.20, for which we did not have the need to compute the
chemical potential again since this could be deduced from
the volume data along the T ∗ = 0.015 isotherm.
Chemical-potential results along the three isobars on
which we focussed are reported in Figs. 3 to 5. As is clear,
with increasing temperature the fluid eventually takes
over the solids. Among the solids, the BCC111(3) phase
is the preferred one for any temperature and pressure, al-
though the chemical potential of the other solid phases is
only slightly larger. On increasing pressure, the melting
temperature goes down, like in the Gaussian-core model.
The necessity of a matching with the zero-temperature
melting point for P = 0 will then imply reentrant melt-
ing in the GCN model too. The maximum error on the
melting temperature Tm, which we estimate to be about
0.003 (hence not that small), entirely depends on the lim-
ited precision of the fluid µex, which then constitutes a
major source of error on Tm.
The only conclusion we can draw from the above
chemical-potential study is that BCC111(3) is the most
stable solid phase of the system (provided the pressure is
not too low). However, a closer look at the DF profiles
obtained from the simulation of BCC111(3) raises some
doubts about the absolute stability of this phase at in-
termediate temperatures, whatever the pressure, calling
for a different interpretation of the hitherto considered
as BCC111(3) MC data. Take, for instance, the case of
P = 0.05. Upon increasing temperature, while g⊥ keeps
strongly peaked all the way to melting, the solid-like os-
cillations of g‖ undergo progressive damping until they
are washed out completely, suggesting a second-order
(or very weak first-order at the most) transformation of
BCC111(3) into a columnar phase before melting. This
is illustrated in Figs. 6 and 7, where the DFs are plotted
for a number of temperatures. A similar indication is got
from the behavior of the smectic OP, see Fig. 8, whose
highest maximum eventually deflates at practically the
same temperature, T ∗ ≈ 0.005, at which the oscillations
of g‖ disappear. Note that no appearance of a columnar
phase is seen during the simulation of either BCC110(3)
or BCC001(3), nor in the simulation of FCC001(3) for
P ∗ = 0.01. A slice of the columnar phase is depicted in
Fig. 9 (right panels). In this phase, columns of stacked
particles are arranged side by side, tightly packed to-
gether so as to project a triangular solid on the x-y plane.
Neighboring columns are not commensurate with each
other, as implied by a completely featureless g‖.
The probable reason for the instability of the smectic
phase in the GCNmodel is the absence of an ad hocmech-
anism for lateral attraction between the molecules, which
is present instead in the model of Ref. [14]. By the way,
hard ellipsoids do not show a smectic phase either [7], at
variance with (long) hard spherocylinders where particle
geometry alone proves sufficient to stabilize a periodic
modulation of the number density along ẑ [10].
Given the compelling evidence of a columnar phase
in the GCN model, one may now ask whether the con-
clusions drawn from the chemical-potential data are all
flawed. In particular, the µ curves that are tagged as
BCC111(3) in Figs. 3 to 5 would be meaningless beyond
a certain temperature Tc < Tm. In fact they are not, i.e.,
they retain full validity up to melting since the (nearly)
continuous character of the transition from BCC111(3)
to columnar allows one to safely continuate thermody-
namic integration across the boundary, with the proviso
that what previously treated as the BCC111(3) chemi-
cal potential beyond Tc is to be assigned instead to the
columnar phase.
As pressure goes up, the transition from BCC111(3) to
columnar takes place at lower and lower temperatures.
In order to exclude that the columnar phase too, like-
wise the fluid, will show reentrant behavior at low pres-
sure, we have simulated the disordering of a BCC111(3)
solid also for P ∗ = 0.02 and 0.03 (in fact, no reentrance
of the columnar phase is observed). Further points on
the melting line for P = 0.01, 0.02, and 0.03 are fixed
through the behavior of g⊥ as a function of temperature.
All in all, the overall GCN phase diagram appears as
sketched in Fig. 10. This is similar to the phase portrait
of the Gaussian-core model, see Fig. 1 of Ref. [4], with
the obvious exception of the columnar phase. There is a
small discrepancy between the melting points as located
through free-energy calculations (full dots in Fig. 10) and
those assessed from the evolution of g⊥ (open dots). In
our opinion, this would mostly be attributed to the sta-
tistical error associated with the µex of the fluid in its
reference state. Notwithstanding their limited precision,
however, free-energy calculations are all but useless in
identifying the structure of the solid phase. In conclu-
sion, although some aspects of the equilibrium behavior
of the GCN model remain still uncertain, especially with
regard to the exact location of the solid-solid transition
at low pressure, we are confident that the main features
of the GCN phase diagram are correctly accounted for
by Fig. 10.
Summing up, there are at least two conceivable and
mutually exclusive paths for the thermal disordering of
a liquid-crystal solid (aside from a direct transformation
of it into a nematic phase). One is through the forma-
tion of a smectic phase, which eventually transforms into
a nematic fluid. A second possibility is a more gradual
release of crystalline order by the appearance of a colum-
nar phase as intermediate stage between the solid and the
nematic phase. Our study showed that it is this second
scenario that occurs in the GCN model, with no evidence
whatsoever of a smectic phase.
CONCLUSIONS
We have introduced a liquid-crystal model of softly-
repulsive parallel ellipsoids, named the Gaussian-core ne-
matic (GCN) model, aiming at a complete characteriza-
tion of its phase behavior, including the solid sector. This
requires a preliminary identification of all relevant solid
structures, which is generally a far-from-trivial task to be
accomplished for model liquid crystals [16]. Through a
careful scrutiny of as many as eleven uniaxially-deformed
cubic and hexagonal phases, we obtained a thorough de-
scription of the T = 0 equilibrium phase portrait of the
GCN model, identifying its ground state at any given
pressure. In doing so, we discovered a rich and absolutely
unexpected structural degeneracy, which is only lifted by
going to T > 0. At low temperature, and for not too
low pressures, our free-energy calculations indicate that a
GCN system with an aspect ratio of 3 is found in just one
solid phase, i.e., a stretched BCC solid with the molecules
oriented along [111]. Only near zero pressure, the stable
phase becomes a stretched FCC solid. With increasing
temperature, the BCC-type solid first undergoes a weak
transition into a columnar phase, which still retains par-
tial crystalline order, before melting completely into the
nematic fluid.
It is worth emphasizing that our interest in the GCN
model is purely theoretical, hard-core ellipsoids provid-
ing a more physically realistic model liquid crystal. One
could even argue that a Gaussian repulsion is highly irre-
alistic for a liquid crystal. In real atomic systems, super-
position of particle cores is strongly obstructed, whence
the consideration of hard-core or steep inverse-power re-
pulsion in the more popular models. However, unless the
system density is very high, higher than considered in
our study, repulsive Gaussian particles would effectively
be blind to an inner hard core, which thus may or may
not exist, as evidenced e.g. in the snapshots of Fig. 9
where particles appear well spaced out.
The GCN model is a “deformation” of Stillinger’s
Gaussian-core model, well known for exhibiting a
reentrant-melting transition. Various instances of reen-
trant behavior are also known for nematics [17] and in-
deed one of the original motivations for the present work
was searching for a new kind of reentrance, i.e., re-
appearance of a more disordered phase with increasing
pressure. With this study, we provide yet another exam-
ple of reentrant behavior in a model nematic: While this
is nothing but the analog of fluid-phase reentrance in the
Gaussian-core model, the absolute novelty of our findings
is in the nature of the intermediate phase, this being sur-
prisingly columnar in a range of pressures rather than
genuinely solid.
∗ Electronic address: Santi.Prestipino@unime.it
† Electronic address: saija@me.cnr.it
[1] D. Frenkel and A. J. C. Ladd, J. Chem. Phys. 81, 3188
(1984); see also J. M. Polson, E. Trizac, S. Pronk, and
D. Frenkel, J. Chem. Phys. 112, 5339 (2000).
[2] F. Saija and S. Prestipino, Phys. Rev. B 72, 024113
(2005).
[3] S. Prestipino, F. Saija, and P. V. Giaquinta, Phys. Rev.
E 71, 050102(R) (2005).
[4] S. Prestipino, F. Saija, and P. V. Giaquinta, J. Chem.
Phys. 123, 144110 (2005).
[5] F. H. Stillinger, J. Chem. Phys., 65, 3968 (1976).
[6] A. Lang, C. N. Likos, M. Watzlawek, and H. Löwen, J.
Phys.: Condens. Matter, 12, 5087 (2000).
[7] D. Frenkel, B. M. Mulder, and J. P. McTague, Phys. Rev.
Lett. 52, 287 (1984).
[8] A. Stroobants, H. N. W. Lekkerkerker, and D. Frenkel,
Phys. Rev. A 36, 2929 (1987).
[9] J. A. C. Veerman and D. Frenkel, Phys. Rev. A 41, 3237
(1990); ibidem, 43, 4334 (1991).
[10] P. Bolhuis and D. Frenkel, J. Chem. Phys. 106, 666
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[11] C. Vega, E. P. A. Paras, and P. A. Monson, J. Chem.
Phys. 96, 9060 (1992); ibidem, 97, 8543 (1992).
[12] P. Pasini and C. Zannoni eds., Advances in the Computer
Simulations of Liquid Crystals (NATO-ASI Series, 1998).
[13] S. Singh, Phys. Rep. 324, 107 (2000).
[14] E. de Miguel and E. Martin del Rio, Phys. Rev. Lett. 95,
217802 (2005).
[15] B. Widom, J. Chem. Phys. 39, 2808 (1963).
[16] After completion of this paper, we became aware of
the discovery, reported in P. Pfleiderer and T. Schilling,
cond-mat/0612151, of a new stable crystal phase in
freely-standing hard ellipsoids. This further demonstrates
that the solid structure of liquid crystals is generally dif-
ficult to anticipate, even when the model system is the
simplest as possible.
[17] The first example of such behavior was discovered by
P. E. Cladis, Phys. Rev. Lett. 35, 48 (1975); see also
Ref. [14] and references therein.
mailto:Santi.Prestipino@unime.it
mailto:saija@me.cnr.it
TABLE I: GCN model for L/D = 3: T = 0 chemical poten-
tial µ(P ) for eleven different solids and two values of P ∗, 0.05
and 0.20. Nx, Ny , Nz are the number of lattice points along
the three spatial directions, ρ = NxNyNz/V is the density,
and α is the stretching ratio (for the SH111 lattice, α is the
so-called c/a ratio). Nx, Ny , Nz have been chosen so large
that the rounding-off error on the total potential energy per
particle, U/N , due to the finite lattice size is negligible. The
numerical precision on ρ and α is of one unit on the last deci-
mal digit. Looking at the Table, the most stable structures at
both pressures are five degenerate crystals, actually belonging
to three distinct types which are exemplified by BCC001(3)
(equivalent to FCC001(2.12) up to a dilation), BCC110(3),
and BCC111(3) (equivalent to SC111(1.5)) – within brackets
is the value of α.
crystal Nx, Ny , Nz ρ(0.05) α(0.05) µ(0.05) ρ(0.20) α(0.20) µ(0.20)
FCC001 10,20,10 0.086 2.12 0.855724 0.157 2.12 2.093695
BCC001 14,14,10 0.086 3.00 0.855724 0.157 3.00 2.093695
SC001 20,20,8 0.086 3.00 0.881586 0.158 3.00 2.105241
FCC110 16,12,12 0.086 3.00 0.856391 0.157 3.00 2.094368
BCC110 10,28,8 0.086 3.00 0.855724 0.157 3.00 2.093695
SC110 14,18,10 0.086 3.00 0.881586 0.158 3.00 2.105241
FCC111 16,18,9 0.086 3.00 0.856391 0.157 3.00 2.094368
BCC111 12,12,18 0.086 3.00 0.855724 0.157 3.00 2.093695
SC111 12,12,18 0.086 1.50 0.855724 0.157 1.50 2.093695
HCP111 18,20,10 0.086 3.00 0.856429 0.157 3.02 2.094474
SH111 18,20,9 0.086 2.75 0.870014 0.158 2.69 2.099565
FIG. 1: T = 0 equilibrium behavior of the GCN model with
L/D = 3. Left: T = 0 chemical potential µ(P ∗) of var-
ious crystals relative to BCC110(3), which thus serves as
the zero or reference level. The reduced pressure P ∗ is in-
cremented by steps of 0.01. Note that, for all P , the five
crystals FCC001(2.12), BCC001(3), BCC110(3), BCC111(3),
and SC111(1.5) are degenerate (∆µ = 0). Other data points
are for FCC001 (continuous line; α = 3 for P ∗ = 0.01, be-
ing α = 2.12 otherwise), FCC110(3) and FCC111(3) (dotted
line), HCP111 (open dots), SH111 (open squares), SC001(3)
and SC110(3) (dashed line). Right: Resulting equation of
state in the pressure range from 0 to 0.30. FCC001(3) (open
triangle) is stable at very low pressure, up to slightly less
than 0.02, while FCC001(2.12), BCC001(3), etc. (open dots)
prevail for higher pressures.
FIG. 2: GCN model with L/D = 3, chemical-potential results
for T ∗ = 0.002. In the picture, we plot the reduced chemical
potential of the three T = 0 degenerate structures that exist
for not too low pressure, taking BCC111(3) for reference. The
latter phase gives the most stable solid for any P in the range
from 0.05 to 0.20 (and, most likely, even further). The µ
curves are obtained by thermodynamic integration of volume
MC data, using as initial conditions those specified by the
Frenkel-Ladd calculations that were carried out at P ∗ = 0.05.
Though the reported µ values for the BCC-type solids are very
close to each other and also affected by some numerical noise,
the higher stability of BCC111(3) cannot be truly called into
question – a regular pattern is clearly seen behind each curve.
FIG. 3: GCN model with L/D = 3, chemical-potential
results for P ∗ = 0.05: Chemical potential of the fluid
phase (dotted line) as compared with those of the competing
solid phases for that pressure (BCC001(3), long-dashed line;
BCC110(3), dashed line; BCC111(3), continuous line). While
the BCC111(3) solid is stable at low temperature, the fluid
phase overcomes it in stability for higher temperatures. This
is more clearly seen in the inset, where chemical-potential
differences are reported, taking the fluid µ for reference. The
melting temperature for P ∗ = 0.05, which is where the con-
tinuous line crosses zero, is estimated to be T ∗ ≃ 0.0073.
FIG. 4: GCN model with L/D = 3, chemical-potential results
for P ∗ = 0.12. Same notation as in Fig. 3, except for the
absence of data for BCC001(3), which were not computed.
Despite this, a look at the results in Figs. 2 and 3 give us
confidence that the chemical potential of BCC001(3) will be
closer to that of BCC110(3) than is for P ∗ = 0.05.
FIG. 5: GCN model with L/D = 3, chemical-potential results
for P ∗ = 0.20. Same notation as in Figs. 3 and 4.
FIG. 6: GCN model with L/D = 3, distribution functions of
BCC111(3) for P ∗ = 0.05. Left: T ∗ = 0.002. Right: T ∗ =
0.003. The strenght of crystalline order along ẑ, as measured
by the amplitude of g‖ oscillations, reduces with increasing
temperature, until complete disorder is left above T ∗ ≃ 0.005
(see next Fig. 7). Considering that the crystallinity within
the x-y plane persists well beyond T ∗ = 0.005 (the spatial
modulation of g⊥ remains solid-like beyond this temperature
and up to melting), we conclude that the GCN system is found
in a columnar phase for 0.005 < T < Tm.
FIG. 7: GCN model with L/D = 3, distribution functions
of BCC111(3) for P ∗ = 0.05. Left: T ∗ = 0.004. Right:
T ∗ = 0.005.
FIG. 8: GCN model with L/D = 3, smectic order parame-
ter τ (λ) of BCC111(3) for P ∗ = 0.05. The behavior of τ (λ)
faithfully reproduces that seen for g‖(z) (cf. Figs. 6 and 7):
The deflating of the highest τ (λ) maximum with increasing
temperature closely follows the thermal damping of g‖(z) os-
cillations.
FIG. 9: GCN model with L/D = 3, some snapshots of the
particle configuration taken at low temperature (T ∗ = 0.002,
BCC111(3) solid phase) and at intermediate temperature
(T ∗ = 0.006, columnar phase). The reduced pressure is
P ∗ = 0.05 in both cases. Above: side view, i.e., projection
of particle coordinates onto the x-z plane. Below: top view,
i.e., projection of particle coordinates onto the x-y plane. For
clarity, in spite of their mutual interaction being soft, the par-
ticles are given sharp ellipsoidal boundaries, corresponding to
a unitary short axis (D) and a long axis of L = 3D. While the
crystalline order along z is lost already at T ∗ = 0.005 (hence,
it is there in the top-left panel while it is absent in the top-
right panel), the triangular order within the x-y plane is main-
tained up to the melting temperature (here, Tm ≃ 0.0073).
FIG. 10: GCN model with L/D = 3, sketch of the phase
diagram on the T -P plane. The full dots mark the location
of the melting transition as extracted from our free-energy
calculations. Open symbols refer instead to the transition
thresholds as given by a visual inspection of the DF profiles.
Though the latter melting-point estimates are more easily ob-
tained than the former, the free-energy study was essential to
identify the correct solid structure of the GCN model at not
too low pressure. To help the eye, tentative phase bound-
aries are drawn as continuous (i.e., first-order) and dashed
(nearly second-order) lines through the transition points. In
the low-pressure region, the solid-solid boundary is highly hy-
pothetical since we have no data there.
