Controllable Atomistic Graphene Oxide Model
and its Application in Hydrogen Sulfide
Removal
Liangliang Huang,a Mykola Seredych,b Teresa J. Bandosz,b Adri C. T. van Duin,c Xiaohua
Lud and Keith E. Gubbinsa,*1
a. Department of Chemical and Biomolecular Engineering, North Carolina State
University, Raleigh, NC, 27695, U.S.A.
b. Department of Chemistry, The City College of New York and the Graduate School of
the City University of New York, New York, NY, U.S.A.
c. Department of Mechanical and Nuclear Engineering, Pennsylvania State University,
University Park, PA, 16801, U.S.A.
d. State Key Laboratory of Materials-Oriented Chemical Engineering, Nanjing University
of Technology, Nanjing, 210009, China.
* To whom correspondence should be addressed: keg@ncsu.edu.
SI-1 A brief overview of ReaxFF development and implementation
The first ReaxFF implementation came from the original serial FORTRAN code of van
Duin et al. in 2001, with the release of ReaxFF parameters for hydrocarbons.1 The source
code integrates parameter optimization and the molecular dynamics (MD) calculation
engine. It is also the only code that can develop new ReaxFF parameters from scratch or
re-fit the existing ReaxFF parameters for new applications. In 2003, the San Diego
Supercomputer Center (SDSC), in conjunction with the Computer Science and
Engineering department at the University of California, San Diego (UCSD), released the
Grid Assessment Probes (GRASP) software package, which is designed to give system
architects and applications developers a simple and easy-to-use set of probes for gaining
insight into the performance and reliability of grid computing platforms. 2 Thompson et
al. implemented the first parallel version of ReaxFF in the GRASP package,3 and later in
a more popular parallel package, LAMMPS (Large-scale Atomic/Molecular Massively
Parallel Simulator).4 GRASP is a C++ code, and it calls the optimized version of the
original FORTRAN routines to calculate the bond orders and the corresponding energies,
and thus exactly matches the results from the serial FORTRAN code. There are two
different implementations of ReaxFF in LAMMPS. One is from the original FORTRAN
code, similar to the implementation in GRASP; the FORTRAN routines are included
directly as libraries files into the main C++ calculation engine. The charge equilibration
calculation in LAMMPS uses a standard parallel conjugate gradient algorithm for sparse
linear system, which is different from the original FORTRAN code, where the
electrostatic interactions are modeled as shielded interactions with Taper corrections. The
other ReaxFF implementation in LAMMPS is from the PuReMD (Purdue Reactive
Molecular Dynamics) code, developed by Aktulga and co-workers.5-7 The PuReMD code
is also written in C++. It uses dynamics memory allocation and has incorporated other
optimizations to extend the simulation capability to larger systems (total number of atoms
~ 107).
Recently ReaxFF has also been incorporated into commercial simulation
packages. Scientific Computing & Modeling (SCM) has implemented and parallelized
ReaxFF in the ADF® (Amsterdam Density Functional software).8 The implementation
has significantly optimized the original FORTRAN code, removing the memory
bottlenecks, and integrated a Graphic User Interface (GUI) for the on-the-fly analysis of
calculation results. Since the version 4.0, GULP (General Utility Lattice Program),9 a
program for performing a variety of types of simulation on materials, has added ReaxFF
into its force field libraries. GULP has been incorporated into Materials Studio®
software,10 an integrated multi-scale modeling environment that delivers a complete
range of simulation methods. Since the recent version 6.0, users can perform ReaxFF MD
calculations from the GULP module of the Materials Studio® software.
SI-II A detailed description of the temperature-programmed protocol
Since the functional groups are randomly attached to the basal surface by a simple
geometry criterion, as shown in Figure S-1, we start the simulation by optimizing the
initial GO model (I) at a low temperature, 10 K, to fine tune the conformation of the
functional groups. During the 25 ps RMD simulation at 10 K, we observe the curvature
change of the basal surface, but there is no chemical reaction between the functional
groups or from the functional group/basal surface interactions. The optimized structure
(A) is then heated slowly from 10 K to 2010 K at a speed of 0.005 K/iteration, where the
iteration is the timestep of the simulation, 0.25 fs. The high temperature (2010 K) is
chosen so that we observe the reactions that normally require a large real time scale (on
the order of seconds for some reactions) at room temperature. It is also worth pointing out
that the choice of the high temperature 2010 K is somewhat arbitrary, as long as it
satisfies: (1) the final GO structure at that high temperature is stable. A higher heat-up
temperature, 5000 K, was tried, but was found to break down the GO structure into pieces
along the B - C path. (2) the release of CO, CO2, and H2O molecules is observed along
the A-B path, which mimics the experimental oxidation conditions. As shown in Figure 3
in the main text, the high temperature has to be greater than 1200 K for the 10%(1:1) and
20%(1:1) initial structures. We of course can use other high temperatures such as 2100 K
or 3010 K.
Along the heat-up path, we observe the release of CO and CO2 molecules as
reaction products from the surface, and also the formation of vacancy defects on the basal
surface. A 25 ps RMD run is then performed at 2010 K to further optimize the GO
structure (B). Many more small molecules (CO, CO2, H2O) are released from the surface.
We also identify larger vacancies and other functional groups on the GO structure along
the B - C path. After that, the GO structure (C) is cooled down from 2010 K to 300 K at
the same rate of 0.005 K/iteration. It is interesting to note that along this path, some gasphase molecules (CO, CO2, H2O) can react with the GO structure to be part of the final
structure (D). Finally, a 25 ps RMD run is carried out to further optimize the GO
structure (D). The final structure (F) from the temperature-programmed protocol is
considered to be the realistic atomistic GO model for the subsequent theoretical
calculations. Longer simulations for path B - C (up to 300 ps) have been performed to
compare the structures (C). There is no significant structural difference observed. Two
different rates of 0.05 K/iteration and 0.001 K/iteration have also been applied for the
heat-up (A to B) and cool-down (C to D) paths. They produce similar final GO structures
(F). We thus conclude that the final GO structure (F) mainly depends on the initial
structure (I) and the high temperature used.
Figure S-1. The temperature-programmed protocol of GO structure in RMD simulation:
“I” and “F” are the initial and final GO configurations, respectively. “A”, “B”, “C” and
“D” are the intermediate GO configurations along the paths. A timestep of 0.25 fs is used
throughout the calculations.
SI-III Mechanical equilibrium examination: the local von Mised shear-strain
analysis
To understand the mechanical equilibrium of the system, we analyzed the atomic local
von Mises shear-strain invariant for the initial (I) and final (F) configurations, as
described in Figure S-1. It is worth noting that we did not calculate the least-square
atomic local strain tensor, which is implemented in AtomEye software, an atomistic
configuration viewer.11 This is because the least-square atomic local strain tensor method
requires a defined reference configuration. However, the reactions between the functional
groups and the basal graphene surface changes the total number of atoms of the GO
structure, and thus it is impossible to keep track of the initial configuration along the
temperature-programmed protocol, as shown in Figure S-1.
The atomic local von Mises shear-strain invariant for the 30%-(1:1) and 10%(1:1) GOs is shown in Figure S-2. Comparing (a) and (c), it is clear that the initial
random functionalization of 30% of the basal carbon atoms resulted in a larger local
strain. The comparison between (a)-(b) and (c)-(d) in Figure S-2 confirmed that the
formation of GO curvatures and the reactions between the functional groups (epoxy and
hydroxyl) and the basal plane can reduce the local strain in the NVT ensemble.
0.9109
(a) 30%-(1:1), image I
(b) 30%-(1:1), image F
0.1341
0
(c) 10%-(1:1), image I
(d) 10%-(1:1), image F
Figure S-2. The atomic local von Mises shear-strain invariant: images (a) and (b) are for
the 30%-(1:1) GO, and images (c) and (d) belong to the 10%-(1:1) GO. The temperatureprogrammed protocol is 10K-2010K-300K, as shown in Figure S-1.
(a) Strainxx
(b) Strainyy
(c) Strainzz
Figure S-3. The change of strain versus the simulation time: (a) Strainxx; (b) Strainyy; (c)
Strainzz. The calculation time step is 0.25 fs/iteration.
We also monitored the strain change along the temperature-programmed protocol.
As shown in Figure S-3, the pressure tensor for the 10%-(1:1) GO had a very high value
at the beginning of the simulation, which is due to the random functionalization of the
basal graphene surface. As discussed in the previous paragraph, the initial GO structure
can reduce the strain to zero quickly by forming curvatures or allowing reactions between
functional groups and the basal plane.
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