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Thermal Resistance from Irradiation Defects in Graphite
Laura de Sousa Oliveira and P. Alex Greaney∗
School of Mechanical, Industrial & Manufacturing Engineering
Oregon State University
Corvallis, OR, 97331
Published in June 2015
Abstract
An atomistic level understanding of how varying
types and numbers of irradiation induced defects af-
fect thermal resistance in graphite is vital in designing
accident tolerant fuels for next-generation nuclear re-
actors. To this end we performed equilibrium molecu-
lar dynamics simulations and computed the change to
thermal conductivity due to a series of clustering and
non-clustering point defects using the Green–Kubo
method. In addition, we present a comprehensive
discussion of several approaches to converge the inte-
gral of the heat current autocorrelation function. Our
calculations show that more energetically favorable
clustering defects exhibit fewer low frequency modes
and increase the anisotropic nature of graphite selec-
tively exerting a significant effect on thermal resis-
tance along the c-axis.
1 Introduction
In the early 1940s, polycrystalline graphite was the
only abundantly produced material with the required
purity to be used as a moderator in nuclear reactors
[1]. While other reactor materials have since been
adopted, at the present time, graphite is still in high
demand for the development of high-tech fuel ele-
ments for next-generation nuclear reactors. Graphite
or pyrolytic carbon is included in many nuclear fuel
assemblies to encapsulate the fissile material. In these
applications, in addition to utilizing its high temper-
ature strength the graphite acts as a neutron mod-
erator and reflector. In some fuels graphite encapsu-
lates the fissile materials in which case all the heat
produced by fission in a fuel pin must be conducted
out through the graphite. As the moderating prop-
erties of graphite are temperature dependent, accu-
rately predicting the thermal conductivity of graphite
and other fuel assembly materials — including how
their thermal conductivity evolves under irradiation
— is vitally important for the design of accident tol-
erant fuels.
The thermal conductivity (κ) of graphite is exper-
imentally found to change with synthesis conditions
and while in service as a direct result of radiation [2].
This indicates that κis not an intrinsic property
and is instead governed by the defect morphology of
the graphite. Simulations typically measure intrin-
sic properties, but we aim to determine an atomistic
level understanding of scattering processes from col-
lections of irradiation induced point defects and to
establish a systematic understanding of how defect
type, number and different defect-type ensembles af-
fect thermal resistance and phonon mean free path
in graphite. We do so with the goal that the insight
that we gain can be incorporated into approaches for
quantitatively predicting the lattice thermal conduc-
tivity that are based on solving the Boltzmann trans-
port equation. Such a tool would be useful to nuclear
engineers and materials scientists in the process of
designing new reactors and fuel systems that are ac-
cident tolerant. As the first step along this path, we
have computed the energy and structure of a zoo of
point defects and determined their separate effects
on thermal conductivity along and across the basal
plane.
In section 2 we establish and validate our method
for computing thermal conductivity of defect-free
graphite. More specifically, we discuss advantages
and challenges associated with the Green–Kubo for-
malism: in section 2.1 we discuss different approaches
to converge the heat current autocorrelation function
(HCACF) and propose a solution based on our find-
ings; the issue of size convergence is explained and
addressed in section 2.2. After establishing an ade-
quate system size, we introduce defects and compute
their formation energies in Section 3. Values are ob-
tained using classical molecular dynamics and com-
1
pared with density functional theory (DFT) calcula-
tions. Interstitial defects are also annealed to find the
most energetically favorable configuration. In section
4 we compare the perfect crystalline system, where
transport is limited by crystal lattice anharmonic-
ity and the acoustic phonons carrying the bulk of
heat are only scattered by other phonons, with sys-
tems with point defects, where defect scattering is
expected to play a crucial role in thermal transport.
Concluding remarks are presented in section 5.
2 Computational method and
validation
Molecular dynamics modeling captures the anhar-
monic interactions of atomic vibrations that carry
heat and both equilibrium and non-equilibrium sim-
ulations can be used to predict thermal conductiv-
ity [3]. The Green–Kubo formalism [4, 5] is a well es-
tablished equilibrium molecular dynamics approach
that has been used successfully to compute thermal
conductivity in a wide range of materials from sili-
con [6] to metal-organic–frameworks [7]. This method
is derived from the fluctuation-dissipation theorem
and computes the thermal conductivity, κ, from the
equilibrium fluctuations in the heat current vector, J,
by:
κxx =V
kBT2Z∞
0
CJxx(τ)dτ, (1)
where kB,Tand Vare the Boltzmann’s constant,
temperature and volume of the simulated region re-
spectively. The term CJ(τ) = hJ(t)J(t+τ)i, and
is the non-normalized heat current autocorrelation
function (HCACF). The net flow of heat fluctuates
about zero at equilibrium and the thermal conductiv-
ity is related to how long it takes for the fluctuations
to dissipate. Both equilibrium and non-equilibrium
molecular dynamics (NEMD) simulations suffer from
size artifacts that must be mitigated. In NEMD, the
simulated system size must be larger than the intrin-
sic mean-free path of the phonons in order to elimi-
nate ballistic transport between the heat source and
sink [3]. Equilibrium MD affords one a smaller system
size as phonons may move through periodic bound-
aries unhindered.
Simulations were performed with the large-scale
equilibrium classical molecular dynamics software
LAMMPS [8] in the microcanonical ensemble (NVE)
at 300 K for 0.6 ns with a 0.2 fs time step and periodic
boundary conditions. Note that this is well below the
Debye temperature for graphite (approximately 2500
K in the basal plane and 950 K along the c-axis [2]).
However, our goal is a comparative analysis of phonon
scattering from and around the defect. As scatter-
ing from classically occupied high frequency modes
is present with and without the defect this has little
contribution to the change in κ. The adaptive inter-
molecular reactive empirical bond-order (AIREBO)
potential function formulated by Stuart et al. [9] was
used for all simulations. The AIREBO potential in-
cludes anharmonic terms in the carbon bonds, an
adaptive treatment of the non-bonded and dihedral
angle interactions and has the capability to model
the interaction between layers in graphite [9]. Two
main challenges result from using the Green–Kubo:
(1) determining an appropriate system size and (2)
converging the HCACF. We shall first address the
later challenge and propose a solution based on the
work of Chen et al. [10].
2.1 HCACF convergence
There is no average net heat flux, hJi, for a sys-
tem in equilibrium, and the HCACF, i.e. the term
inside the integral in Eqn. (1), is therefore expected
to decay to zero given sufficient time. Instead, long
lived oscillations with a significant contribution to
the computed thermal conductivity have been ob-
served [11–14]; this behavior is illustrated in Fig.
1. The HCACF is crucial in computing κusing the
Green–Kubo method and yet there is little consensus
among researchers on whether these oscillations are
significant to thermal transport or a result of noise,
and on what approach to take. A discussion of this
behavior and of possible approaches is essential in un-
derstanding the limitations of the Green–Kubo and
validating thermal transport calculations.
Figure 1(a) shows the accumulation of the averaged
HCACF along a basal direction over a typical sim-
ulation. It can be seen that the tail of the HCACF
contains many fluctuations, but rather than these de-
caying smoothly as more data is averaged there oc-
cur sporadic events that can overwhelm the average
to add new fluctuations to CJ(τ) and significantly
change the initial value CJ(0). These large events
show up in the majority of simulations and for all
simulated system sizes. Long lasting oscillations are
prevalent along the basal plane and different in char-
acter to oscillations along the c-axis (see Fig. 1).
Fluctuations along the c-axis exhibit a much higher
frequency and oscillate around zero with the HCACF
converging to zero with only minor instabilities affect-
ing its integral. Fluctuations along the basal plane,
on the other hand, do not fade away during compu-
tation time and significantly affect κ. In graphite,
2
κcalculations in the c-direction are not affected by
HCACF fluctuations as much as basal plane calcu-
lations are. This makes results perpendicular to the
basal plane easier to compute and more reliable.
Along the basal plane the HCACF exhibits a two-
stage decay: a rapid decay associated with high fre-
quency phonons and a slower decay associated with
lower frequency phonons. Similar two-stage decay
(or three-stage decay) is observed in many single el-
ement materials and different authors have modeled
κby fitting the HCACF to the sum of two or more
exponentials [13–15]. This is a more physically mean-
ingful approach than a single exponential fit in that
it captures multiple relaxation processes, but it ap-
pears to neglect the contribution of the HCACF tail
and to play a part in the systematic underestimate
of κ[3, 14]. When addressing the issue of conver-
gence in the HCACF we have examined a wide vari-
ety of strategies. These strategies included direct in-
tegration of the HCACF truncated to various cutoffs,
fits of varying sums of exponentials to the truncated
HCACF, and fits in the frequency domain. Here we
present only a few of the best or otherwise insightful
findings and a brief discussion of our approach.
(i–iv) Direct numerical integration of the truncated
HCACF up to (i) 50 ps, (ii) 5 ps, (iii-iv) and
a noise dependent cut off time, tc, proposed
by Chen et al. and described below [10]. For
(iv) individual cut-offs were computed for each
HCACF as shown in Figs. 1(b)–1(d) , and for
(iii) an average tcwas used for each simulation
set.
(v) Single exponential fits to the first 5ps of the
HCACF.
(vi) The fitting procedure proposed by Chen et al.,
which includes a fixed offset term in the fitting
function:
CJ(τ)
CJ(0) =A1e−τ/t1+A2e−τ/t2+Y0,(2)
such that κis computed as
κxx =V CJxx(0)
kBT2(A1t1+A2t2+Y0tc),(3)
where A1,A2,Y0,t1and t2are fitting parame-
ters. Chen et al. argue that including the offset
Y0reduces the computational error. In our im-
plementation of this we used the simplex method
to optimize the fit variables. It is physically
meaningless to have negative Y0and this term
was weighed with a Heaviside function to pro-
hibit negative Y0terms. We also imposed the
condition that A1+A2+Y0= 1.
(vii) Double exponential of the form in (vi) with Yo
set to zero.
(viii) Triple exponential of the form:
CJ(τ)
CJ(0) =A1e−τ/t1+A2e−τ/t2
+(1 −A1−A2)e−τ/t3,(4)
fit to each HCACF.
The issue of the cut-off time should now be discussed,
before analyzing the results in Fig. 2. The necessity
to truncate the HCACF is illustrated in Fig. 1 in
which it can be seen that after roughly 2–5 ps the
integrals of the autocorrelations diverge even though
the HCACF is almost zero. This divergence arises
from the integration of random fluctuations in CJ(τ)
effectively adding a random walk to the integral of
CJ(τ). The error from this random walk grows over
time, while the systematic error from omitting the
long tail of slow decay processes in the HCACF di-
minishes over time. There exists an optimal trun-
cation point that minimizes the error in the integral
of CJ(τ), but there is little consensus in the litera-
ture on how to determine it [3,12]. While selecting a
consistent cut-off may often suffice to obtain a com-
parative analysis, it introduces a systematic error in
the estimation of the HCACF, potentially neglecting
the contribution to kof lower phonon modes. Chen
et al. [10] propose obtaining a quantitative descrip-
tion of the numerical noise in the relative fluctuation
of the HCACF, F(t), defined as
F(t) =
σ(CJ)
E(CJ)
,(5)
where σis the standard deviation and Ethe expected
value of the HACF in an interval (t,t+δt). The cut-
off point is determined to be above an F(t) of 1 (see
Figs. 1(b)–1(d)), i.e. when the fluctuations become
the same scale as the mean. Chen suggests that F(t)
is insensitive to the choice of δ. We find this is the
case for only small variations and between a δof 1,
3, and 5 ps the best results correspond to the 1 ps in-
terval. Both 3 and 5 ps intervals resulted in outliers
with a significant effect on κ. The variability we ob-
served with the choice of δsuggests that to obtain a
good fit using this method requires a balance between
having sufficient data points to compute the local av-
erages while maintaining enough temporal resolution
to reasonably determine when in time the noise ex-
ceeds F(t) = 1. A cut-off point was computed for
each run and the average cut-off point for a given
system was then obtained. Each system was simu-
lated 10 times. We compared κfor the cases when δ
3
was 1, 3 and 5 ps with κbeing computed using both
each independent simulation’s cut-off (as in Chen et
al.) and using the average cut-off for all simulations.
We found that using the average cut-off yielded sim-
ilar results with error bars significantly smaller than
using the corresponding systems’ individual run cut-
offs to compute κfor each simulation within a cell
size. In theory, if we could consider the average local
fluctuations in the heat flux over an infinite amount
of time, we should be able to find a “true” thermal
conductivity of a given system. It is then reasonable
to assume that each HCACF is an approximation to
an HCACF obtained over infinite time and that there
is a “true” cut-off point, thus providing an argument
for using the average cut-off on each individual run to
compute κ. When only the first two terms of the
HCACF were computed, as in (vii), Y0contributed
up to nearly 100 W/(mK) in the most extreme case.
This illustrates the insufficiency of the two exponen-
tial fits to estimate κ. The sum of three exponential
fits yields results very similar to the strategy adopted
by Chen et al. with the added modification of using
the average cut-off instead of each individual simula-
tion’s cut-off. However, as the number of fitting vari-
ables increases, results are expected to mimic those
of a full integration and the fit loses its physical sig-
nificance. This correspondence nevertheless suggests
Eqn. (4) to be an adequate fit and substantiates the
cut-off method. More strikingly, simply using the av-
erage cut-off as the HCACF integration limit yields
similar results with error bars comparable to the fit.
The correct behavior of the HCACF along the basal
plane is thus more accurately explained by the fit type
suggested by Chen at al. than merely the sum of ex-
ponentials, but in order to compute actual κvalues,
the fit introduces an unnecessary hassle to no gain.
Furthermore, the nature of the HCACF along the c-
axis is very different than that of the basal plane, as
can be seen by looking at Figs. 1 and 2 and this fit
type is not adequate to explain the HCACF perpen-
dicular to the basal plane. That said, the error bars
are noticeably smaller when the HCACF is integrated
only up to tcthan when they are integrated over the
total HCACF time. The simplest, most effective ap-
proach is to select the cut-off for each simulation by
setting F(t) = 1, but to use the average cut-off of
all simulations when computing each simulation’s in-
dividual κ. This method is adequate to compute κ
along any direction for highly oriented graphite. In
light of this analysis, a similar simulation time with
a lower HCACF is likely to yield more accurate κ
results, as it would allow more time for convergence
and not necessarily lower the cut-off.
While there is no consensus on the best method to
reduce noise and capture the nature of the HCACF of
graphite and other materials, the approach selected
in this paper yields κestimates higher than a sum of
exponentials, with moderately small error bars and
without the need of a complicated fit. This method
was used for all defect calculations along the basal
plane and along c, taking into account that the cut-
off along c must neglect the first values of F(t) = 1
which take place in the initial decay stages (see Fig.
1). Being consistent with the choice of method is
often sufficient for a significant comparative analysis
and this method allows us to do that.
2.2 Size convergence
Periodic boundary conditions allow simulations of
a small number of particles to mimic the behavior
of an infinite solid; however, they limit the number
and wavelength of the vibrational modes available
to carry heat. Thus, when using the Green–Kubo
method it is first necessary to establish size conver-
gence. Thermal conductivity values were computed
for perfectly crystalline systems of varying size, as
can be observed in Fig. 2. An 8 atom unit cell was
defined and 7 systems ranging between 3x3x3 and
15x15x15 super cells were simulated (again 10 times
each). Along the basal plane the systems’ size was
asymmetric in the x and y dimensions with x smaller
than y — this was done to better gauge potential
size artifacts. While there was a large variability in
the thermal conductivity, the values are scattered be-
tween 300 and 400 W/(mK) along the x direction and
350 and 450 W/(mK) along the y. This suggests a size
artifact not evident just looking at the system size in-
crease within each direction and that only comes into
play within each system. For this reason different x
and y values were maintained when computing ther-
mal conductivity in defective systems as well. For
computations performed with defects, the 11x11x11
super cell was selected to allow for a big enough com-
pute cell with a feasible computational expense as-
sociated. The 11x11x11 super cell corresponds to a
10648 atom system in the perfect graphite, with a
270.5×468.6×737.9nm3volume in the x,y, and
zdirections, respectively.
3 Identifying defect structures
In irradiated graphite carbon atoms are displaced
due to cascade reactions giving rise to many point
defects. We categorize these into defects that have
a strong driving force for clustering, such as vacan-
cies and interstitials, and defects that are less driven
to cluster such as bond rotation defects, and isotopic
4
defects. The following clustering defects were con-
sidered: a single interstitial (Fig. 3(b)), a single va-
cancy (Fig. 3(j)), clusters of 2–8 interstitials (Figs.
3(c)–3(i)) and clusters of 2–3 vacancies (Figs. 3(k)
and 3(l)). For a single interstitial, three interstitial
locations were considered, as depicted in Fig. 4(a).
Similarly, four configurations were simulated for 2-
interstitial clusters, as shown in Fig. 4(b). The single
vacancy site is between the centers of hexagonal voids
on the planes adjacent to the plane of the vacancy,
i.e. where the type A single interstitial is positioned
in Fig. 4(a), but in the lower, less visible layer. The
added vacancies lie directly between atom sites on
the adjacent layers. The non-clustering defects con-
sidered were a Stone-Wales defect (Fig. 3(a)) and
an isotope. The C14 isotope was selected for hav-
ing a higher mass than C13, another common carbon
isotope, and thus being expected to have a higher
contribution to changes in κ. The defects were intro-
duced to the center of the selected 11x11x11 perfect
system; the interstitial defects were placed between
the 11th and 12th layer of the 22 layer cell, and the
remaining defects within the 11th layer, as shown in
Fig. 5.
Formation energies were computed using classical
MD for all defects. These calculations were used to
estimate the likelihood of formation of each defect,
where the energy per defect is given by
Ed=ED−EO
NO
∗ND.(6)
NDand NOare the number of atoms in the defective
system and the corresponding non-defective system,
in that order. EDcorresponds to the total energy of
the system and EOto the total energy of the perfect
system of the same size.
The optimization process for the classical calcula-
tions is described in the flowchart in Fig. 6 as was per-
formed using the FIRE scheme [16] as implemented
in LAMMPS. As part of the process to optimize the
geometry of the interstitial defects, low energy inter-
stitials (type Ain Fig. 4(a) and type Cin Fig. 4(b)
for one and two-interstitial defects respectively) were
also annealed and subsequently cooled. The defects
were annealed to 1500 K for 500 ps and cooled to 300
K for 1 ns, in the canonical (NVT) ensemble. By do-
ing this we allowed the already low energy interstitial
defects to migrate and rearrange themselves into po-
tentially lower energy configurations.
Energy values for the different defect types and num-
bers are depicted in Fig. 7 and in Table 1. Classical
interstitial defect energies were computed for the op-
timized structures before and after annealing. It is
notable in Fig. 7, that the annealing process often
yielded defect structures with considerably lower en-
ergy than those reached by direct relaxation using
the FIRE algorithm — even for very simple defects
such as a lone interstitial. There is, however, good
agreement in the overall trend of defect energies as
modeled with the AIREBO empirical potential and
those from Li et al., computed using density func-
tional theory (DFT) with the local density approxi-
mation (LDA) [17]. Furthermore, the type Asingle
interstitial when annealed becomes structurally sim-
ilar to Li et al.’s 5.5 eV formation energy “free” in-
terstitial, computed with DFT. Stone-Wales defects
have the lowest formation energy of all intrinsic de-
fects in graphenic systems [18], calculated with DFT
at 5.2 eV [17].
4 Thermal resistance from de-
fects
While the thermal conductivity of near-perfect
graphite has been reported to be as high as 4180
W/(mK) along the basal plane [19], the experimen-
tally measured anisotropy ratio (κa/κc) of near-ideal
graphite has been found to be just below 210 at 300
K [20]. This suggests the Green–Kubo calculations
to be an order of magnitude below experimental val-
ues in the basal plane, but within the expected or-
der of magnitude for κalong the c-axis. Finally, κ
for nine defects including the more energetically fa-
vorable ones was computed using the Green–Kubo
method as with the perfect crystal. Thermal conduc-
tivity for the hexagonal platelet was also computed
using different super cell sizes (Fig. 10). While κis
within the error bars along the basal plane for all de-
fects, the overall trend suggests a decrease in thermal
conductivity with the presence of defects, as would
be expected. More distinctly, the systems with in-
terstitial clusters exhibit a clear decrease in the ther-
mal conductivity along c (see Fig. 8(c)). Note that
these defects correspond to low configuration ener-
gies as well and are therefore more likely to occur.
Frenkel pairs is one type of defect that is expected to
emerge from exposure to radiation due to knock-out
reactions; the added effect of vacancy and interstitial
clusters would significantly reduce κperpendicular to
the basal plane.
Performing a discrete cosine transform (DCT) of
the HCACF reveals the presence of localized modes
exclusively associated with the lower thermal conduc-
tivity defect types (see Fig. 9). We performed DCTs
for the defect systems both along x,yand z, and
found two notable differences between systems in the
5
Defect Type Single: A Single: B Single: C Two: A Two: B Two: C Two: D
LAMMPS 3.57 eV 4.73 eV 4.46 eV 4.99 eV 3.27 eV 2.95 eV 2.98 eV
Literature (DFT, LDA) [17] 6.7 eV 7.7 eV 7.4 eV - - - -
Table 1: Classical MD energy calculations for single and double interstitial defect types based on location.
The values obtained for a single interstitial are compared with available density functional theory (DFT)
calculations using the local density approximation (LDA) from Ref. [17].
DCT of the c-axis HCACF. Systems containing in-
terstitial platelets develop a series of peaks at ∼1.3,
2.5 and 3 THz. We attribute these to rattling of
the platelets in the c-direction and the defects being
large enough to have relatively low frequency vibra-
tional modes. More interestingly, there is a dramatic
reduction in the intensity of low frequency modes in
the HCACF of the systems with diminished thermal
conductivity.
There is little difference in terms of how the number
of interstitials (between 5 and 8) in a cluster affect the
overall thermal conductivity in the system, but there
is a noticeable change as the system size increases —
the systems containing an hexagonal platelet increase
in κwith system size (see Fig. 10). This seems to
suggest that the defect concentration has an effect on
the total thermal conductivity as well. The last sys-
tem corresponds to an 11x11x11 super cell with two
hexagonal defects equally spaced and, as expected,
it shows a lower thermal conductivity than the same
system size with a single defect.
If instead of considering κwe assume that defects
make an additive contribution to the systems’ ther-
mal resistance, r, then we might expect rdefect =
rdefective −rperfect, and that thus the thermal re-
sistance for a system containing two defects would
be r2defects =rperfect + 2 ·rdefect , or r2def ects =
2.32 ±0.32W/(mK) for the hexagonal platelet. It
appears from Fig. 10(b) that adding a defect does
not double its thermal resistance, but reducing the
size to half does; a fit through a system with varying
defect numbers may shed light into how κscales with
defect concentration for each defect type.
5 Conclusions
In this work we have reported calculations of the
reduction in thermal conductivity of graphite due to a
series of point defects typical under irradiation. The
calculations reveal three important conclusions:
•Clustered interstitial defects are stable (with re-
spect to lone interstitials) and strongly detrimen-
tal to the thermal transport in both the in-plane
and c-axis directions.
•In addition to lowering the thermal conductiv-
ity they also increase the thermal conductivity
anisotropy.
•Although the noise in the calculations of κis
large, it is clear that the platelets create larger
thermal resistance than the constituent number
of lone interstitials.
In pebble bed reactors graphite is used to encapsulate
the fissile materials and thus the graphite experiences
an extremely large neutron dose. The average fuel
temperatures in such a reactor is 1200 K (with peak
temperatures expected to stay below 1500 K) [21].
At these temperatures interstitials are highly mobile
and readily condensing to interstitial platelets. These
platelets are responsible for c-axis swelling under ir-
radiation [2]. Our work indicates that this has a dou-
bly negative effect on thermal conductivity; elongat-
ing grains along their thermally resistive directions
while also increasing the thermal resistance in these
directions.
In addition to computing the reduction in ther-
mal conductivity due to defects we have performed
a systematic comparison of various numerical strate-
gies for reducing uncertainty in the integration of the
HCACF. Our simulations reveal infrequent large heat
current fluctuations that are large enough to over-
whelm the averaged HCACF. The origin of these fluc-
tuations is unclear to us at this stage and we specu-
late two possible causes. It is possible that the fluc-
tuations are a manifestation of Fermi–Pasta–Ulam–
Tsingou recurrence [22] or some related breakdown of
ergodicity over the time period accessible to simula-
tion. An alternative explanation is that the fluctua-
tions are physically realistic processes similar to rogue
ocean waves and caused by amplitude dependence of
the phonon dispersion in graphite. It has been pro-
posed that carbon nanotubes possess soliton-like heat
carriers [23] and it is possible that similar conditions
may arise in graphite. These two potential explana-
tions are incompatible and would require one to treat
the fluctuations differently: in the first case remov-
ing their effect from computed thermal conductivity,
and in the latter case performing enough simulations
to obtain a statistically significant sampling of these
infrequent fluctuations.
6
6 Acknowledgments
This work used the Extreme Science and Engineer-
ing Discovery Environment (XSEDE), which is sup-
ported by National Science Foundation grant number
OCI-1053575.
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Figure 1: In Fig. 1(a), the HCACF is computed as
the simulation progresses along x for the perfectly
crystalline 11x11x11 super cell. At first only a few
values contribute to the ensemble average and the
initial HCACFs are noisy. As the averaging time
progresses the HCACF becomes smoother with the
exception of well defined crests and troughs, most of
which do not fade away during the total simulation
time. Figures 1(b), 1(c) and 1(d) correspond to the
HCACF noise (computed as F(t)), the final HCACF,
and the integral of the HCACF, respectively, for all
simulations of the perfect 11x11x11 super cell system
along y and z (or c).
8
Figure 2: These figures correspond to κmeasured for
different super cells along x (2(a)) and y (2(b)) in the
basal plane and along the c-axis (2(c)). In addition
to establishing size convergence, the figures illustrate
a set of different approaches (labeled in the legend)
considered to converge the HCACF and the corre-
sponding standard error. Method (iii) was selected.
Figure 3: Illustration of the defects examined in this
study: Stone-Wales defect (3(a)); single interstitial
(3(b)); 2-8 interstitials (3(c) -3(i)); single vacancy
(3(j)), di-vacancy (3(k)), and 3 vacancies (3(l)). The
interstitial defects are shown in their annealed con-
figurations.
Figure 4: Possible defect types for single (Fig. 4(a))
and two-interstitial defects (Fig. 4(b)).
Figure 5: Slice of a graphite system with an hexag-
onal platelet, indicating the location of the defect.
There are 22 total layers in the system.
9
Figure 6: Schematic of the optimization procedure
applied to classically simulated defects before com-
puting formation energies.
Figure 7: These energies correspond to the defects
depicted in Fig. 3. In the case of the interstitial
defect-types, values were computed both for annealed
and non-annealed systems.
10
Figure 8: Anisotropy ratio (κa/κc) computed for both x and y for different defect types, including defects
found to be most energetically favorable (Fig. 8(a)); κobtained for different defect types along x and y (Fig.
8(b)) and in the basal plane (Fig. 8(c))
Figure 9: Discrete cosine transform applied to the
c-axis HCACF for different defect types.
Figure 10: Hexagonal platelet κand corresponding
standard error computed along x and y in the basal
plane (Fig. 10(a)) and along the c-axis (Fig. 10(b))
for 4 different super cell sizes including the 10648 base
atom system, and for two hexagonal platelets in the
same base system.
11
