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: ballen34@du.edu

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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762 Journal of Computational Chemistry 2014, 35, 756–764 WWW.CHEMISTRYVIEWS.COM

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.

SOFTWARE NEWS AND UPDATESWWW.C-CHEM.ORG

Journal of Computational Chemistry 2014, 35, 756–764 763

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Received: 1 November 2013Revised: 27 December 2013Accepted: 1 January 2014Published online on 4 February 2014

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