integrating open-source software applications to build molecular dynamics systems
TRANSCRIPT
Integrating Open-Source Software Applications to BuildMolecular Dynamics Systems
Bruce M. Allen,* Paul K. Predecki, and Maciej Kumosa
Three open-source applications, NanoEngineer-1, packmol, and
mis2lmp are integrated using an open-source file format to
quickly create molecular dynamics (MD) cells for simulation.
The three software applications collectively make up the
open-source software (OSS) suite known as MD Studio (MDS).
The software is validated through software engineering prac-
tices and is verified through simulation of the diglycidyl ether
of bisphenol-a and isophorone diamine (DGEBA/IPD) system.
Multiple simulations are run using the MDS software to create
MD cells, and the data generated are used to calculate density,
bulk modulus, and glass transition temperature of the DGEBA/
IPD system. Simulation results compare well with published
experimental and numerical results. The MDS software proto-
type confirms that OSS applications can be analyzed against
real-world research requirements and integrated to create a
new capability. VC 2014 Wiley Periodicals, Inc.
DOI: 10.1002/jcc.23537
Introduction
The goal of this work was to integrate existing open-source
software (OSS) applications for the purpose of quickly creating
an initial molecular dynamics (MD) cell, also known as a sys-
tem of molecules or a system for simulation using the large-
scale atomic/molecular massively parallel simulator
(LAMMPS).[1] LAMMPS and the visual MD (VMD) software are
used to run MD simulations and to view atom trajectories.[2,3]
LAMMPS itself is an object toolkit and can be built as a library
and called as part of a larger software application.
Colleagues reported initial epoxy system creation times of
up to 6 months due to the complexity of specifying single sys-
tem geometry and associated force field data for LAMMPS.
Also, ring spearing and manually fixing systems generated
with commercial software made finding alternative system crea-
tion approaches attractive.[4] A search for chemistry or nanotech-
nology computer-aided design (CAD) software yielded
NanoEngineer-1 (NE-1), while a review of tools bundled with
LAMMPS provided msi2lmp.[5] The initial project direction was to
integrate the two software applications, but NE-1 lacked a MD
cell or cell concept and drawing medium to large geometries (at
least several thousand atoms) would take significant time. Later,
while attending the LAMMPS Workshop in August 2011, packmol
was brought to our attention, which had the necessary cell con-
cept explicitly designed into it.[6] Packmol was determined to be
the missing software application needed to complete the OSS
development. The three integrated applications, NE-1, packmol,
and msi2lmp are now known collectively as MD Studio (MDS).
Initial development requirements were based on the need for
application source code and a development environment to sup-
port software modifications and test. A lengthy comparison of
many OSS applications was not attempted.
Simulated Epoxy System
The MDS software integration was tested in this research by
following standard software engineering practices and by
calculating physical properties of an epoxy material. This was
achieved by calculating the density of the diglycidyl ether of
bisphenol-a and isophorone diamine (DGEBA/IPD) epoxy sys-
tem over a large temperature range allowing the simulated
epoxy to transition from the glassy to viscous state. The glass
transition temperature and the bulk modulus of the simulated
epoxy were also calculated. The DGEBA/IPD system was
selected for simulation due to its complexity and the fact that
this system had been previously investigated providing experi-
mental and numerical data for comparison.[7] The DGEBA and
IPD structures are shown in Figures 1 and 2, respectively.
The actual epoxy material consists of a three-dimensional
network of polymer and cure molecules intertwined with other
networks of itself creating a crosslinked and intertwined amor-
phous system. For simplicity, oligomers composed of nine
DGEBA and four IPD molecules were used in the simulation in
an attempt to model the epoxy material. Each oligomer was
75% intracrosslinked within itself as shown in Figure 3.
A total of five oligomers were used to create a cell, which
provided intertwining to the cell. Tack and Ford modeled and
simulated a similar system consisting of DGEBF and DETDA
using the Amorphous Cell in Materials Studio by Accelrys.[8,9]
Modeling a similar system using MDS was important due to
the intertwining of oligomers provided by packmol.[6] The
Consistent Family of Force Fields (CFF91) was used for all simu-
lations.[10] Figure 4 illustrates through a flowchart the three
components of the MDS software applications, shown in yel-
low, and the LAMMPS application shown in green. The integra-
tion between the three applications was accomplished with
B. M. Allen, P. K. Predecki, M. Kumosa
University of Denver, Daniel Felix Richie School of Engineering & Computer
Science, Mechanical & Materials Engineering, 2390 South York Street, Denver,
Colorado 80210
E-mail: [email protected]
Contract grant sponsor: National Science Foundation [(NSF) Grant
Opportunities for Academic Liaison with Industry (GOALI)]; contract grant
number: #CMMI-1232520
VC 2014 Wiley Periodicals, Inc.
756 Journal of Computational Chemistry 2014, 35, 756–764 WWW.CHEMISTRYVIEWS.COM
SOFTWARE NEWS AND UPDATES WWW.C-CHEM.ORG
the development of the enhanced molecular machine part
(EMMP) file.
The EMMP file was developed for sharing data among the
three applications, and is based on the existing molecular
machine part (MMP) file. The LAMMPS geometry input file
(LGIF) is the final output from the MDS software. The LGIF and
the LAMMPS Command Input File were provided to the
LAMMPS software, and the LAMMPS output trajectory file was
generated and used to calculate the final simulation results
(e.g., Tg, density, volume, temperature) for the epoxy system of
interest. The trajectory files generated by LAMMPS were
viewed using the VMD application.
Software Use
Following the flowchart in Figure 4, it took approximately 2 h
to draw a single oligomer. Single DGEBA and IPD molecules
were drawn and then copied several times. Subsequently, the
molecules were attached to one another to form the oligomer.
Care was taken to not introduce high energies into the start-
ing molecules or when attaching them to form the oligomer.
The NE-1 molecular mechanics energy minimzer (MMEM) was
executed periodically to lower the molecules’ and oligomers’
potential energies. The existing NE-1 “combine” feature was
used to group the molecules in the oligomer into a single
molecule. Grouping the attached molecules into a single mole-
cule (oligomer) was necessary because the EMMP file parser
added to packmol, expected a single molecule per file. The
final EMMP file created was a template for packmol containing
the oligomer. The initial cell of five oligomers is shown in
Figure 5.
Running packmol is an iterative process; therefore, an initial
guess of the initial cell size and shape was made based on the
size of the oligomer and the number of oligomers. Packmol
was run several times, and the volume of the cell was reduced
until no solution was obtained. This previous successful execu-
tion provided the minimum volume for the initial cell. Packmol
took no more than 1 h to generate the initial cell. Finally, the
LGIF was created by msi2lmp in a few seconds. The DGEBA/
IPD system required no more than 3 or 4 h to generate the
initial cell. The CFF91 force field file required maintenance to
add generic values for missing parameters reported by
msi2lmp. Maintenance times are outside the execution of the
MDS software, but do have an obvious affect on the simula-
tion results.
Software Features and Modifications
NE-1 is CAD software created by Nanorex for molecular-level
CAD.[5] The source code consists of Python and C. The Wing-
IDE integrated development environment and debugger was
used to test Python software changes. NE-1 is capable of mod-
eling and simulating nanomachines, DNA, carbon nanotubes,
and many other systems both organic and inorganic. NE-1
stores complex geometry in its MMP file format.
The MDS software uses the MMP file as a molecule tem-
plate. It provides the positions of atoms and bonding topol-
ogy from which bond lengths,
angles, and dihedral angles
can be calculated. The MMP
file format was expanded in
this research to include atom
types, thus creating the EMMP
file, and was used for building
the DGEBA/IPD initial cell. The
original NE-1 uses a force field,
NanoDynamics-1 (ND-1), which
is element based and cannot
be used for MD simulations in
LAMMPS. ND-1 provides a sin-
gle atom type for all chemical
environments, whereas most
force fields used with LAMMPS
provide several per atom.
Therefore, the modifications to
the MMP file were critical.
Figure 1. Diglycidyl ether of bisphenol a (DGEBA) epoxy monomer resin.
Figure 2. Isophorone diamine (IPD) crosslinker (curing agent).
Figure 3. Oligomer structure.
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Modifications to the NE-1 graphical user interface (GUI) were
made to support user access and selection of atom types
while drawing molecules. Figure 6 shows the force field
chooser on the left hand side of the NE-1 GUI. The atom
chooser shows oxygen highlighted in blue, and the generic
oxygen atom type is highlighted in the force field chooser.
Notice the CFF91 force field is selected in Figure 6. On the
drawing canvas, an oxygen atom is highlighted in yellow, to
allow the text and numeric atom types to be displayed. In the
interest of completing the prototype quickly, atom types are
given numerical representations to preclude token parsing
issues and allow every element to have a maximum of 100
atom types. The ToolTips dialog is displayed in Figure 7. The
dialog allows the user to select whether numerical or character
atom types are displayed when atoms are highlighted on the
canvas as presented in Figure 6. The user can choose another
force field from the drop down box in the force field chooser.
Clicking on the CFF91 title button displays another force field
to be selected. The COMPASS atom types, not the associated
proprietary force field parameters, were integrated into the NE-
1 force field chooser as a proof of concept, demonstrating how
multiple force fields can be integrated into the application.[11]
Packmol was developed by Dr. Leandro Martinez at IMECC-
UNICAMP.[6] Packmol was written in FORTRAN and the GNU
FORTRAN compiler and the dynamic data display debugger
were used in the CYGWIN environment under Windows 7 to
make and test source code changes. The packmol application
reads one or more molecule templates and fills a user defined
volume with a user specified number of each molecule. Pack-
mol allows the user to generate many unique cells by specify-
ing a unique pseudorandom number seed. This allows the
user to create and later recreate a cell based on initial system
configuration information.[6]
The original packmol software could not read or write MMP
files, and was, therefore, modified to read and write the EMMP
file. An example of how packmol functions is as follows: a
cube containing 1,000 DGEBA molecules and 500 IPD mole-
cules is created using a single-molecule template for DGEBA
and single-molecule template for IPD. Given the size of the
DGEBA and IPD molecules and the packing constraints, inter-
twining of molecules is not expected. This is not the case for
the oligomers used in this research.
Figure 5. Initial cell: Five oligomers. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
Figure 4. Flowchart for MDS software.
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758 Journal of Computational Chemistry 2014, 35, 756–764 WWW.CHEMISTRYVIEWS.COM
The oligomers used
in this work were unex-
pectedly intertwined,
which was an added
benefit to cell creation.
For the cell shown in
Figure 5, packmol cal-
culated a cubed-
shaped initial cell with
a side length of 55 A.
The initial cell was then
reduced to the equili-
brated final cubed-
shaped geometry, 30.5
A per side at 1 atm and
298 K using LAMMPS.
The final cell in Figure
8 is rendered in
unwrapped atom
coordinates.[2]
The simulation was
run using periodic
boundary conditions
(PBC) which wrap
atoms that leave the
boundaries of the cell
into positions directly
on the opposite side
of the cell. VMD was
used to display the
final cell. VMD
attempts to display
elements using
unique colors. Figure
8 illustrates the point
that the final cell is
considerably denser
than the initial cell
seen in Figure 5.
Packmol optimized
the packing of the
molecules by calculat-
ing a minimum cost
function value. The
minimum cost is asso-
ciated with molecules
being very close to
one another and pro-
viding a minimum
system volume, while
minimizing the repul-
sion forces between
atoms of different
molecules. The cost
function contained a
user defined con-
straint for maintaining
Figure 6. New software feature allows choosing the force field. [Color figure can be viewed in the online issue, which is avail-
able at wileyonlinelibrary.com.]
Figure 7. New software feature allows display of numeric atom types. [Color figure can be viewed in the online issue, which is avail-
able at wileyonlinelibrary.com.]
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Journal of Computational Chemistry 2014, 35, 756–764 759
a minimum distance between atoms of different molecules
with an initial value of 2 A. When atoms from different mole-
cules were too close to one another, high repulsive forces
occurred, resulting in atoms moving a large distance in a sin-
gle simulation time step. LAMMPS terminates under such con-
ditions due to bad dynamics.
LAMMPS supports identification of individual molecules to
calculate mean-squared displacement (MSD) during simulation.
Packmol was modified again to uniquely identify molecules in
the EMMP file allowing MSD calculations for small molecules
in the cell. The molecule ID number generated in packmol was
passed to msi2lmp for inclusion in the LGIF.
The msi2lmp software was created by Dr. John Carpenter at
Cray Research.[2] Msi2lmp generated a LGIF from the Cartesian
coordinate file/molecular data file (CAR/MDF) file pair, which is
one file format supported by Materials Studio, a commercial soft-
ware application.[9] Msi2lmp was written in the C language, and
the Microsoft Visual Studio tool was used to modify and test its
source code modifications. The msi2lmp software enumerates all
unique bond and nonbonded force relationships among atoms
of a molecule and between many molecules composing a system.
Msi2lmp also populates the force field parameters from a user
supplied force field file into the LGIF. The epoxy material simula-
tion required atom partial charges for Coulombic force calcula-
tions in LAMMPS. The original msi2lmp transferred the atom
partial charges from the (CAR/MDF) file pair directly to the LGIF
when processing files created by Materials Studio. The modified
msi2lmp software, in this research, calculates the atom partial
charges using the method defined by Sun after reading atom
data from the EMMP file.[11] The LAMMPS “full atom” model is
written to the LGIF, which is required to support the atom partial
charges and the molecule ID created by packmol.[2]
The above software modifications reflect the new require-
ments needed to integrate the three applications. Table 1
summarizes the MDS requirements.
The density requirement determines the free volume in the
epoxy cell. Crosslinking and intertwining of polymer chains
determine moduli of the material and density. The remaining
requirements were cited above as software modifications to
the three software applications. Table 2 lists the specific source
code changes by application and programming language.
Software Verification
The MDS software developed in this study was subsequently
verified by using standard software test methods of white and
black box testing, requiring the use of debuggers to examine
data values and pathways taken in the modified software. The
MDS software was parallel tested using a single DGEBA/IPD
oligomer to generate two LGIF files using two separate paths
through the modified msi2lmp software.
Parallel software testing was performed by converting the
EMMP file into a protein data bank (PDB) file, which was then
transformed into a CAR/MDF file pair. The CAR/MDF file part
was subsequently converted to a LGIF using the existing path-
way in the msi2lmp application for string-based atom types.
The EMMP file was rewritten into a LGIF using a different path-
way in the msi2lmp application that understood numeric atom
types. The Beyond Compare software tool was used to com-
pare the two LGIF files for differences.[12] Figure 9 shows the
process for the above parallel testing.
The two LGIF files were nearly identical, thus showing that
the new MDS software created the correct file for simulation.
Subtle differences in the two LGIF files were traced back to
how NE-1 created errors in the PDB file. The MD verification
simulations were executed using the major simulation parame-
ters listed in Table 3.
Table 1. MDS requirements map.
Requirement NE-1 packmol Msi2lmp LAMMPS
Experimental density X X X X
Crosslinked and intertwined
cell geometry/moduli
X X
Minimize repulsive forces X
Force field support X X X X
Calculate atom partial charges X X
Draw molecules X
Manually type atoms X
Modify and add atom types X
Generate LGIF X
Uniquely identify molecules X
Figure 8. Final cell: five oligomers after simulated annealing. [Color figure can
be viewed in the online issue, which is available at wileyonlinelibrary.com.]
Table 2. Software modifications.
Software modifications NE-1 packmol Msi2lmp
NE-1 EMMP file parser (Python) X
Packmol EMMP file parser (FORTRAN) X
Msi2lmp EMMP file parser(C) X
GUI added force field chooser (Python) X
GUI changes to tooltips dialog (Python) X
Passes atom types X X
Calculate atom partial charges (C) X
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760 Journal of Computational Chemistry 2014, 35, 756–764 WWW.CHEMISTRYVIEWS.COM
PBC were used to maintain a constant number of atoms
over the simulation duration and to isolate the computations
from surface effects. The Van der Waals and Coulombic cutoffs
were set to 9.5 and 10.0 A to support the LAMMPS full atom
model. The commonly used in organic system simulations, 1 fs
time-step, was assumed.[13]
Simulation Methods and Results
The following simulation steps were performed to generate
the final cell: MMEM, quench (velocity rescaling), and simu-
lated annealing (SA). Table 4 presents the SA details for each
of the five rounds. System equilibrium, after SA, was accom-
plished by running a series of NPT ensembles, where NPT rep-
resents holding the number of moles (N), pressure (P) and
temperature for the system constant while allowing the sys-
tem volume to change over time. The ensembles are listed in
Table 5. The bulk modulus was calculated by linearly increas-
ing the system pressure from 1 to 5001 atm at 298 K using
the NPT ensemble in Table 6.
Table 7 lists the calculated density values based on cell
dimensions and temperature. The cell dimensions were
collected every 0.1 ps, and the calculated density at that tem-
perature was the average of 1,000 data points. The simulated
density value was verified by comparing it to the experimen-
tally determined value from the literature for the same resin
system tested at ambient conditions (i.e., 298 K and 1 atm).[10]
The agreement was within 7% as shown in Table 8. This
clearly indicates that MDS provided a high-quality estimate of
the resin’s density. The cell dimensions and the bulk modulus
of the resin at ambient temperature are presented in Tables 9
and 10, respectively.
In addition, a comparison is made in Table 10 between the
experimental and simulated values of the modulus. Similar to
the density predictions, the error in the modulus prediction is
small and was found to be less than 3%.
MDS was further verified by determining the glass transition
temperature of the resin of interest. Figure 10 presents a graph
of the average density data plotted, in black diamonds, against
the average temperatures from Table 7. The line in black is the
least squares fit of the first five temperatures vs. density values.
Figure 9. Parallel testing msi2lmp. [Color figure can be viewed in the
online issue, which is available at wileyonlinelibrary.com.]
Table 3. LAMMPS simulation parameters.
Parameter Value
PBC Default
K-space Solver PPPM
Van der Waals cuffoff 9.5 A
Coulombic force cutoff 10.0 A
Time step 1 fs
Table 4. Single round of simulated annealing at 1 atm.
Step Start temperature (K) End temperature(K) Duration(ps)
1 298 298 50
2 298 600 50
3 600 600 200
4 600 298 50
Table 5. LAMMPS NPT ensemble.
Step Begin temperature (K) End temperature(K) Duration (ps)
1 298.0 298.0 10
2 298.0 328.2 100
3 328.2 328.2 10
4 328.2 358.4 100
5 358.4 358.4 10
6 358.4 388.8 100
7 388.8 388.8 10
8 388.8 419.0 100
9 419.0 419.0 10
10 419.0 449.2 100
11 449.2 449.2 10
12 449.2 479.4 100
13 479.4 479.4 10
14 479.4 509.6 100
15 509.6 509.6 10
16 509.6 539.6 100
17 539.6 539.6 10
18 539.6 570.0 100
19 570.0 570.0 10
20 570.0 600.2 100
21 600.2 600.2 10
Table 6. Bulk modulus data collection at 298 K NPT ensemble.
Step Begin pressure (atm) End pressure (atm) Duration (ps)
1 1.0 1.0 100
2 1.0 5001.0 100
3 5001.0 5001.0 100
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The remaining points were used to generate the least squares
fit of the remaining six temperatures vs. density values as indi-
cated by the gray dashed line. The first five points were
selected to represent the glassy material prior to Tg, whereas
the remaining points represent the viscous material. Tg was also
estimated by selecting the first six and four glassy points as
shown in Figures 11 and 12. Taking the average value of three
determined Tgs using the first four, five, and six glassy data
points, the average Tg was predicted to be 433 K.
Discussion
Particle–particle–particle-mesh (PPPM) performs nonbonded
calculations faster than using the Ewald method in a multicore
environment. It fails sooner in a multicore environment, due
to missing atoms compared to the Ewald method, making
PPPM preferable.[2] The results shown in Table 7 took approxi-
mately 3 days on a 2.93 GHz 4-core i7 Intel processor using
the PPPM option, whereas the same simulation required
approximately 5 days using the Ewald option.
Using a combination of MMEM, quench, and SA allowed cell
conformations to reach a density near the experimental value at
ambient conditions. It took approximately 36 h to achieve the
ambient density shown in Table 7. Simulation of a system of
small molecules would not have required such a long equilibra-
tion time. The oligomer size and perhaps the intertwining are
responsible for the long preparation time. The oligomers were
used to achieve intracrosslinking in the epoxy, due to previous
attempts to manually intercrosslink oligomers led to longer
equilibration times. The longer times were due to the introduc-
tion of nonequilibrium geometry, when constructing the cross-
links using NE-1. The calculated system density undershoots by
7% compared to the experimental value.[10] Polymer density is
also a function of the degree of crosslinking.[14] Shokuhfar and
Arab calculated DGEBA/DETA densities for crosslinking densities
from 5 to 81%. The experimental values for 16, 25, and 37% are
1.16, 1.19, and 1.13 gm/cc, respectively, whereas the simulated
densities are 1.1, 1.11, and 1.12 gm/cc, respectively.[15]
In general, simulations undershoot the experimental values.
Epoxy materials absorb water vapor, increasing density. There
appears to be enough uncertainty in the experimental and sim-
ulation methods making determination of monotonically
increasing densities with increasing degree of crosslinking diffi-
cult. The fifth density in Figure 10 is higher than expected and
was most likely due to the heating rate and equilibration time
used in elevating the system temperature from 388 to 418 K.
The system is transitioning from glassy and viscous, and further
Table 7. Simulated densities by NPT ensemble.
Temperature (K) Lx (A) Ly (A) Lz (A) Volume (cc) 3 1.0e20 Density (gm/cc)
297.55 30.75 30.76 30.77 2.8954 1.056
328.02 30.74 30.76 30.77 2.9106 1.0510
358.12 30.78 30.79 30.80 2.9197 1.0458
388.63 30.83 30.84 30.85 2.9344 1.0427
418.66 30.79 30.80 30.81 2.9222 1.0470
449.02 30.93 30.94 30.95 2.9634 1.0325
479.55 31.07 31.08 31.09 3.0042 1.0185
509.85 31.39 31.14 30.42 3.023 1.0122
539.95 31.32 31.34 31.35 3.0783 0.99404
570.57 31.46 31.47 31.48 3.1164 0.98187
599.86 31.56 31.57 31.58 3.14645 0.9725
Table 8. Simulated vs. experimental density at 1 atm and 298 K.
Simulated density
(gm/cc)
Experimental density
(gm/cc)
Percent
difference
1.056 6 0.02 1.13110 7 6 1
Table 9. Simulated bulk modulus raw data.
Pressure (atm) Lx (A) Ly (A) Lz (A) Volume (cc)
1.0 30.86 30.87 30.88 2.94e-20
5001.0 24.76 29.82 35.92 2.65e-20
Table 10. Simulated bulk modulus.
D (MPa) DV (cc) V0 (cc)
Bulk
modulus (GPa)
Experiential bulk
modulus (GPa)
506.6 2.90e-21 2.94e-20 5.13 5.0110
Figure 10. Five point glassy Tg least squares.
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study could be done to understand the physics in this range
and determine a better heating rate and equilibration time.
The simulated bulk modulus overshoots by less than 3%.
Using the average Tg value calculated (433 K) from the three
least squares Tg values determined from Figures 10, 11, and
12, the average Tg value undershoots the experimental value
(436 K) by less than 1%. The experimental value was deter-
mined by Sindt to be 436 K.[7] The initial density, bulk modu-
lus, and glass transition temperatures are near the
experimental values providing a high-comfort level for the
MDS software in its alpha-level release state.
On Going Software Development
Work on the MDS software is ongoing. The software is under
configuration at source forge, http://sourceforge.net/projects/
moleculardynami/, and is chronicled on our blog at http://
moleculardynamicsstudio.blogspot.com/. Several software
enhancements are in progress including static and dynamic
crosslinking to build better cells, automatic atom typing, sup-
port for all open-source atomistic and united atom force fields,
and integration of packmol and msi2lmp applications into NE-1
for ease of use. Better crosslinking will result in ease of use and
speed up preparing the final cell for simulation. Automatic
atom typing will free the user to manually type exceptional
cases, where the software is unable to determine the correct
atom type. The CFF force fields usually include an “auto equiv-
alence” table to provide missing force field parameters. Msi2lmp
will be modified again to make use of this feature. NE-1 will
read force field files to maintain up-to-date atom types.
Conclusions
OSS can be effectively integrated through software modifica-
tions, thus, providing new capabilities that the separate appli-
cations did not have in their original versions. The MDS
software developed in this research, in conjunction with
LAMMPS, successfully provided the necessary base geometry
for calculating the DGEBA/IPD system densities as a function
of temperature, the bulk modulus, and the glass transition
temperature to within a few percent of experimentally deter-
mined values. MDS, thanks to the robustness of packmol, gen-
erated a complex initial cell containing intertwined oligomers.
Finally, MDS allowed the user to generate the LGIF for the ini-
tial cell in hours, instead of months.
Acknowledgments
Any opinions, findings, and conclusions or recommendations
expressed in this paper are those of the authors, and do not neces-
sarily reflect the views of the National Science Foundation. We wish
to thank The University of Denver for providing an excellent
research atmosphere and for additional funds for Mr. Allen’s gradu-
ate teaching assistant appointment. Mr. Allen also wishes to thank
The Boeing Company for its support of his masters’ work in materi-
als science, making this article possible, and Dr. Leora Peltz for her
early guidance.
Keywords: molecular dynamics � large-scale atomic/molecular
massively parallel simulator � software integration � open
source
How to cite this article: B. M. Allen, K. Paul, M. Predecki, M.
Kumosa. J. Comput. Chem. 2014, 35, 756–764. DOI: 10.1002/
jcc.23537
[1] S. Plimpton, J. Comput. Phys. 1995, 117, 1.
[2] S. J. Plimpton, P. Crozier, A. Thompson, LAMMPS Users Manual, 2013,
Available at: http://lammps.sandia.gov/doc/Manual.html, Accessed on
December 19, 2009.
[3] NIH Center for Macromolecular Modeling and Bioinformatics, VMD Vis-
ual Molecular Dynamics, Available at: http://www-s.ks.uiuc.edu/
Research/vmd, Accessed on March 30, 2013.
Figure 11. Six point glassy Tg least squares.
Figure 12. Four point glassy Tg least squares.
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Received: 1 November 2013Revised: 27 December 2013Accepted: 1 January 2014Published online on 4 February 2014
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