Skip to main content

Molecular Dynamics Simulation

  • Chapter
  • First Online:
Supercomputing for Molecular Dynamics Simulations

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

This section provides a compact description of the basics of MD simulation. It only covers topics that are required to understand MD simulation in process engineering, i.e. in particular molecular modeling, the computation of potentials and forces, as well as the efficient identification of neighboring molecules. Here focus is put on single- and multi-center interactions based on the Lennard-Jones potential for short-range interactions. These detailed descriptions help to elaborate the differences between MD in process engineering and other fields and motivate the development of a specialized code. Such a code is ls1 mardyn, whose optimizations are discussed in the up-coming chapters. At the end of the section we provide the general layout of the software.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://towhee.sourceforge.net/.

  2. 2.

    http://www.materialsdesign.com/medea/medea-gibbs.

References

  1. J.-P. Ryckaert, G. Ciccotti, H.J. Berendsen, Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23(3), 327–341 (1977)

    Article  Google Scholar 

  2. T.R. Forester, W. Smith, SHAKE, rattle, and roll: efficient constraint algorithms for linked rigid bodies. J. Comput. Chem. 19(1), 102–111 (1998)

    Article  Google Scholar 

  3. B. Hess, P-LINCS: a parallel linear constraint solver for molecular simulation. J. Chem. Theory Comput. 4(1), 116–122 (2008)

    Article  Google Scholar 

  4. D. Fincham, Leap frog rotational algorithms. Mol. Simul. 8, 165–178 (1992)

    Article  Google Scholar 

  5. J.B. Kuipers, Quaternions and rotation sequences (Princeton University Press, Princeton, 1999)

    Google Scholar 

  6. C. Niethammer, S. Becker, M. Bernreuther, M. Buchholz, W. Eckhardt, A. Heinecke, S. Werth, H.-J. Bungartz, C.W. Glass, H. Hasse, J. Vrabec, M. Horsch, ls1 mardyn: the massively parallel molecular dynamics code for large systems. J. Chem. Theory Comput. (2014)

    Google Scholar 

  7. H.A. Lorentz, Über die Anwendung des Satzes vom Virial in der kinetischen Theorie der Gase. Ann. Phys., 12(1):127–136, (1881). Addendum 12(4):660–661

    Google Scholar 

  8. D. Berthelot, Sur le mélange des gaz. Comptes rendus hebdomadaires des séances de l’Académie des Sciences, 126:1703–1706, (1898). Addendum: vol. 126, no. 4, pp. 1857–1858

    Google Scholar 

  9. T. Schnabel, J. Vrabec, H. Hasse, Unlike Lennard-Jones parameters for vapor-liquid equilibria. J. Mol. Liq. 135, 170–178 (2007)

    Article  Google Scholar 

  10. C.G. Gray, K.E. Gubbins, Theory of molecular fluids, Volume 1: Fundamentals (Clarendon Press, Oxford, 1984)

    Google Scholar 

  11. M.P. Allen, D.J. Tildesley, Computer Simulation of Liquids (Oxford University Press, Oxford, 1989)

    Book  Google Scholar 

  12. J. Barker, R. Watts, Monte Carlo studies of the dielectric properties of water-like models. Mol. Phys. 26(3), 789–792 (1973)

    Article  Google Scholar 

  13. R. Clausius, XVI on a mechanical theorem applicable to heat. Philos. Mag. Ser. 4, 40(265):122–127 (1870)

    Google Scholar 

  14. P.H. Hünenberger, Thermostat algorithms for molecular dynamics simulations, Advanced Computer Simulation (Springer, Berlin, 2005), pp. 105–149

    Chapter  Google Scholar 

  15. L. Woodcock, Isothermal molecular dynamics calculations for liquid salts. Chem. Phys. Lett. 10(3), 257–261 (1971)

    Article  Google Scholar 

  16. L. Verlet, Computer experiments on classical fluids. I. Thermodynamical properties of Lennard-Jones molecules. Phys. Rev. Online Arch. (Prola) 159(1), 98–103 (1967)

    Google Scholar 

  17. S. Pll, B. Hess, A flexible algorithm for calculating pair interactions on SIMD architectures. Comput. Phys. Commun., (2013). Accepted for publication

    Google Scholar 

  18. R. Hockney, S. Goel, J. Eastwood, Quiet high-resolution computer models of a plasma. J. Comput. Phys. 14(2), 148–158 (1974)

    Article  Google Scholar 

  19. P. Schofield, Computer simulation studies of the liquid state. Comput. Phys. Commun. 5(1), 17–23 (1973)

    Article  Google Scholar 

  20. M. Bernreuther, H.-J. Bungartz, Molecular simulation of fluid flow on a cluster of workstations, in Proceedings of the 18th Symposium Simulationstechnique (ASIM 2005), Volume 15 of Fortschritte in der Simulationstechnik—Frontiers in Simulation, ed. by F. Hülsemann, M. Kowarschik, U. Rüde (SCS European Publishing House, Erlangen, 2005), pp. 117–123

    Google Scholar 

  21. G. Sutmann, V. Stegailov, Optimization of neighbor list techniques in liquid matter simulations. J. Mol. Liq. 125, 197–203 (2006)

    Article  Google Scholar 

  22. M. Buchholz, Framework zur Parallelisierung von Molekulardynamiksimulationen in verfahrenstechnischen Anwendungen. Dissertation, Institut für Informatik, Technische Universität München (2010)

    Google Scholar 

  23. P. Gonnet, A simple algorithm to accelerate the computation of non-bonded interactions in cell-based molecular dynamics simulations. J. Comput. Chem. 28(2), 570–573 (2007)

    Article  Google Scholar 

  24. U. Welling, G. Germano, Efficiency of linked cell algorithms. Comput. Phys. Commun. 182(3), 611–615 (2011)

    Article  MATH  Google Scholar 

  25. J.C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R.D. Skeel, L. Kale, K. Schulten, Scalable molecular dynamics with NAMD. J. Comput. Chem. 26(16), 1781–1802 (2005)

    Article  Google Scholar 

  26. B. Hess, C. Kutzner, D. van der Spoel, E. Lindahl, Gromacs 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J. Chem. Theory Comput. 4(3), 435–447 (2008)

    Article  Google Scholar 

  27. K.J. Bowers, E. Chow, H. Xu, R.O. Dror, M.P. Eastwood, B.A. Gregersen, J.L. Klepeis, I. Kolossvary, M.A. Moraes, F.D. Sacerdoti, J.K. Salmon, Y. Shan, D.E. Shaw, Scalable algorithms for molecular dynamics simulations on commodity clusters. In Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, SC ’06, ACM, New York, USA (2006)

    Google Scholar 

  28. B.R. Brooks, C.L. Brooks, A.D. Mackerell, L. Nilsson, R.J. Petrella, B. Roux, Y. Won, G. Archontis, C. Bartels, S. Boresch et al., CHARMM: the biomolecular simulation program. J Comput. Chem. 30(10), 1545–1614 (2009)

    Article  Google Scholar 

  29. R. Salomon-Ferrer, D.A. Case, R.C. Walker, An overview of the Amber biomolecular simulation package (Computational Molecular Science, Wiley Interdisciplinary Reviews, 2012)

    Google Scholar 

  30. A. Arnold, O. Lenz, S. Kesselheim, R. Weeber, F. Fahrenberger, D. Roehm, P. Košovan, C. Holm, Espresso 3.1: molecular dynamics software for coarse-grained models, in meshfree methods for partial differential equations VI, p. 1–23. (Springer, 2013)

    Google Scholar 

  31. S. Plimpton, Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117(1), 1–19 (1995)

    Article  MATH  Google Scholar 

  32. D.A. Case, T.E. Cheatham, T. Darden, H. Gohlke, R. Luo, K.M. Merz, A. Onufriev, C. Simmerling, B. Wang, R.J. Woods, The Amber biomolecular simulation programs. J. Comput. Chem. 26(16), 1668–1688 (2005)

    Article  Google Scholar 

  33. M. Griebel, S. Knapek, G.W. Zumbusch, Numerical simulation in molecular dynamics: numerics, algorithms, parallelization, applications, vol 5. (Springer, 2007)

    Google Scholar 

  34. B. Eckl, J. Vrabec, H. Hasse, On the application of force fields for predicting a wide variety of properties: ethylene oxide as an example. Fluid Phase Equilib. 274(1–2), 16–26 (2008)

    Article  Google Scholar 

  35. T. Merker, C. Engin, J. Vrabec, H. Hasse, Molecular model for carbon dioxide optimized to vapor-liquid equilibria. J. Chem. Phys., 132(23), (2010)

    Google Scholar 

  36. The ls1 mardyn website (2014), http://www.ls1-mardyn.de/

  37. S. Deublein, B. Eckl, J. Stoll, S.V. Lishchuk, G. Guevara-Carrion, C.W. Glass, T. Merker, M. Bernreuther, H. Hasse, J. Vrabec, ms2: a molecular simulation tool for thermodynamic properties. Comput. Phys. Commun. 182(11), 2350–2367 (2011)

    Article  Google Scholar 

  38. K.E. Gubbins, J.D. Moore, Molecular modeling of matter: impact and prospects in engineering. Ind. Eng. Chem. Res. 49(7), 3026–3046 (2010)

    Article  Google Scholar 

  39. E. Gamma, R. Helm, R. Johnson, J. Vlissides, Design patterns: elements of reusable object-oriented software. (Addison-Wesley, 1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexander Heinecke .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 The Author(s)

About this chapter

Cite this chapter

Heinecke, A., Eckhardt, W., Horsch, M., Bungartz, HJ. (2015). Molecular Dynamics Simulation. In: Supercomputing for Molecular Dynamics Simulations. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17148-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-17148-7_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17147-0

  • Online ISBN: 978-3-319-17148-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics