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A Hierarchical, Component Based Approach to Screening Properties of Soft Matter

  • Christoph KleinEmail author
  • János Sallai
  • Trevor J. Jones
  • Christopher R. Iacovella
  • Clare McCabe
  • Peter T. Cummings
Chapter
Part of the Molecular Modeling and Simulation book series (MMAS)

Abstract

In prior work, Sallai, et al. introduced the concept and algorithms of building molecular topologies through the use of a hierarchical data structure and the use of an affine coordinate transformation to connect molecular components. In this work, we expand upon the original concept and present a refined version of this software, termed mBuild , which is a general tool for constructing arbitrarily complex input configurations for molecular simulation in a programmatic fashion. Basic molecular components are connected using an equivalence operator which reduces and often removes the need for users to explicitly rotate and translate components as they assemble systems. Additionally, the programmatic nature of this approach and integration with the scientific Python ecosystem seamlessly exposes high-level variables that users can tune to alter the chemical composition of their systems, such as mixtures of polymers of different chain lengths and surface patterning. Leveraging these features, we demonstrate how mBuild serves as a stepping stone towards screening and performing optimizations in chemical parameter space of complex materials by performing automated screening studies of monolayer systems as a function of graft type, degree of polymerization, and surface density.

Keywords

Molecular dynamics Software System construction 

Notes

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grants No. NSF CBET-1028374 and OCI-1047828.

References

  1. 1.
    Bernstein, F.C., Koetzle, T.F., Williams, G.J., Meyer, E.F., Brice, M.D., Rodgers, J.R., Kennard, O., Shimanouchi, T., Tasumi, M.: The protein data bank: a computer-based archival file for macromolecular structures. Arch. Biochem. Biophys. 185, 584–591 (1978)CrossRefGoogle Scholar
  2. 2.
    Humphrey, W., Dalke, A., Schulten, K.: VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996)CrossRefGoogle Scholar
  3. 3.
    Salomon-Ferrer, R., Case, D.A., Walker, R.C.: An overview of the Amber biomolecular simulation package. Wiley Interd. Rev.: Comput. Mol. Sci. 3, 198–210 (2013)Google Scholar
  4. 4.
    Omnia: High performance, high usability toolkits for predictive biomolecular simulation. http://www.omnia.md
  5. 5.
    Sallai, J., Varga, G., Toth, S., Iacovella, C.T., Klein, C., McCabe, C., Ledeczi, A., Cummings, P.T.: Web- and cloud-based software infrastructure for materials design. Proc. Comput. Sci. 29, 2034–2044 (2014)Google Scholar
  6. 6.
    Abbott, L.J., Hart, K.E., Colina, C.M.: Polymatic: a generalized simulated polymerization algorithm for amorphous polymers. Theoret. Chem. Acc. 132, 1–19 (2013)CrossRefGoogle Scholar
  7. 7.
    McGibbon, R.T., Beauchamp, K.A., Schwantes, C.R., Wang, L.-P., Hernández, C.X., Harrigan, M.P., Lane, T.J., Swails, J.M., Pande, V.S.: MDTraj: a modern, open library for the analysis of molecular dynamics trajectories. bioRxiv (2014)Google Scholar
  8. 8.
    Fuller, P.: Imolecule: an embeddable webGL molecule viewer. https://github.com/patrickfuller/imolecule
  9. 9.
    Eastman, P., et al.: OpenMM 4: a reusable, extensible, hardware independent library for high performance molecular simulation. J. Chem. Theory Comput. 9, 461–469 (2013)CrossRefGoogle Scholar
  10. 10.
    Anderson, J.A., Lorenz, C.D., Travesset, A.: General purpose molecular dynamics simulations fully implemented on graphics processing units. J. Comput. Phys. 227, 5342–5359 (2008)CrossRefGoogle Scholar
  11. 11.
    Hanwell, M.D., Curtis, D.E., Lonie, D.C., Vandermeersch, T., Zurek, E., Hutchison, G.R.: Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J. Cheminform. 4, 17 (2012)CrossRefGoogle Scholar
  12. 12.
    http://www.whitehouse.gov/mgi. Materials genome initiative for global competitiveness
  13. 13.
    Aric Hagberg, P.S., Dan Schult NetworkX: High-productivity software for complex networks. https://networkx.github.io/
  14. 14.
    Pérez, F., Granger, B.E.: IPython: a system for interactive scientific computing. Comput. Sci. Eng. 9, 21–29 (2007)CrossRefGoogle Scholar
  15. 15.
    Bhushan, B., Israelachvili, J.N., Landman, U.: Nanotribology: friction, wear and lubrication at the atomic scale. Nature 374, 607–616 (1995)CrossRefGoogle Scholar
  16. 16.
    Brzoska, J.B., Shahidzadeh, N., Rondelez, F.: Evidence of a transition temperature for the optimum deposition of grafted monolayer coatings. Nature 360, 719–721 (1992)CrossRefGoogle Scholar
  17. 17.
    Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M.R., Smith, J.C., Kasson, P.M., Van Der Spoel, D., Hess, B., Lindahl, E.: GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29, 845–854 (2013)CrossRefGoogle Scholar
  18. 18.
    Jorgensen, W.L., Maxwell, D.S., Tirado-Rives, J.: Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 118, 11225–11236 (1996)CrossRefGoogle Scholar
  19. 19.
    Lorenz, C., Webb, E., Stevens, M., Chandross, M., Grest, G.: Frictional dynamics of perfluorinated self-assembled monolayers on amorphous SiO2. Tribol. Lett. 19, 93–98 (2005)CrossRefGoogle Scholar
  20. 20.
    Lagomarsino, M.C., Dogterom, M., Dijkstra, M.: Isotropic nematic transition of long, thin, hard spherocylinders confined in a quasi-two-dimensional planar geometry. J. Phys. Chem. 119, 719–721 (2003)Google Scholar
  21. 21.
    Wilson, M.R.: Determination of order parameters in realistic atom-based models of liquid crystal systems. J. Mol. Liq. 68, 23–31 (1996)CrossRefGoogle Scholar
  22. 22.
    Black, J.E., Iacovella, C.R., Cummings, P.T., McCabe, C.: Molecular dynamics study of alkylsilane monolayers on realistic amorphous silica surfaces. Langmuir 31, 3086–3093 (2015)CrossRefGoogle Scholar
  23. 23.
    Martnez, L., Andrade, R., Birgin, E.G., Martnez, J.M.: PACKMOL: a package for building initial configurations for molecular dynamics simulations. J. Comput. Chem. 30, 2157–2164 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Christoph Klein
    • 1
    • 3
    Email author
  • János Sallai
    • 2
  • Trevor J. Jones
    • 1
  • Christopher R. Iacovella
    • 1
    • 3
  • Clare McCabe
    • 1
    • 3
    • 4
  • Peter T. Cummings
    • 1
    • 3
  1. 1.Department of Chemical and Biomolecular EngineeringVanderbilt UniversityNashvilleUSA
  2. 2.Institute for Software Integrated SystemsVanderbilt UniversityNashvilleUSA
  3. 3.Vanderbilt Multiscale Modeling and Simulation (MuMS) FacilityVanderbilt UniversityNashvilleUSA
  4. 4.Department of ChemistryVanderbilt UniversityNashvilleUSA

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