Skip to main content

Overcoming Instabilities in Verlet-I/r-RESPA with the Mollified Impulse Method

  • Conference paper
Computational Methods for Macromolecules: Challenges and Applications

Abstract

The primary objective of this paper is to explain the derivation of symplectic mollified Verlet-I/r-RESPA (MOLLY) methods that overcome linear and nonlinear instabilities that arise as numerical artifacts in Verlet-I/r-RESPA. These methods allow for lengthening of the longest time step used in molecular dynamics (MD). We provide evidence that MOLLY methods can take a longest time step that is 50% greater than that of Verlet-I/r-RESPA, for a given drift, including no drift. A 350% increase in the timestep is possible using MOLLY with mild Langevin damping while still computing dynamic properties accurately. Furthermore, longer time steps also enhance the scalability of multiple time stepping integrators that use the popular Particle Mesh Ewald method for computing full electrostatics, since the parallel bottleneck of the fast Fourier transform associated with PME is invoked less often. An additional objective of this paper is to give sufficient implementation details for these mollified integrators, so that interested users may implement them into their MD codes, or use the program ProtoMol in which we have implemented these methods.

Using simple analysis of a 1-d model problem we show the linear instability present in Verlet-I/r-RESPA at approximately half the period of the fastest motion, and more interestingly, how the mollified methods can be designed to overcome them. The paper also includes an experimental component that shows how these methods overcome instability barriers in practice.

We also present evidence that more complicated instabilities are present in Verlet-I/r-RESPA than linear analysis reveals. In particular, we postulate nonlinear resonance mechanisms hereto ignored, although these mechanisms are known for leapfrog. This means that Verlet-I/r-RESPA is no better than leapfrog if one wants a simulation with no drift. Currently, we use mild Langevin damping to overcome these nonlinear instabilities, but it is possible to design symplectic MOLLY integrators that are nonlinearly stable as well.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. E. Barth and T. Schlick. Extrapolation versus impulse in multiple-timestepping schemes. II. Linear analysis and applications to Newtonian and Langevin dynamics. J. Chem. Phys., 109(5):1633–1642, Aug 1998.

    Article  CAS  Google Scholar 

  2. E. Barth and T. Schlick. Overcoming stability limitations in biomolecular dynamics. I. Combining force splitting via extrapolation with Langevin dynamics in ln. J. Chem. Phys., 109(5):1617–1632, August 1998.

    Article  CAS  Google Scholar 

  3. J. J. Barton and L. R. Nackman. Scientific and Engineering C++: an introduction with advanced techniques and examples. Addison-Wesley, Reading, Massachusetts, 1994.

    Google Scholar 

  4. P. F. Batcho and T. Schlick. Special stability advantages of position Verlet over velocity Verlet in multiple-timestep integration. J. Chem. Phys., 2001.

    Google Scholar 

  5. D. M. Beazley and P. S. Lomdahl. Message-passing multi-cell molecular dynamics on the connection machine 5. Parallel Computing, 20:173–195, 1994.

    Article  Google Scholar 

  6. D. M. Beazley and P. S. Lomdahl. Lightweight computational steering of very large scale molecular dynamics simulations. In Proceedings of Supercomputing ′96, 1996.

    Google Scholar 

  7. H. J. C. Berendsen. Molecular dynamics simulations: The limits and beyond. In P. Deuflhard, J. Hermans, B. Leimkuhler, A. Mark, S. Reich, and R. D. Skeel, editors, Computational Molecular Dynamics: Challenges, Methods, Ideas, volume 4 of Lecture Notes in Computational Science and Engineering, pages 3–36. Springer-Verlag, Nov. 1998.

    Google Scholar 

  8. J. J. Biesiadecki and R. D. Skeel. Dangers of multiple-time-step methods. J. Comput. Phys., 109(2):318–328, Dec. 1993.

    Article  Google Scholar 

  9. T. Bishop, R. D. Skeel, and K. Schulten. Difficulties with multiple timestepping and the fast multipole algorithm in molecular dynamics. J. Comput. Chem., 18(14):1785–1791, Nov. 15, 1997.

    Article  CAS  Google Scholar 

  10. B. R. Brooks and M. Hodošček. Parallelization of CHARMm for MIMD machines. CDA, 7:16–22, Dec. 1992.

    Google Scholar 

  11. D. Brown, H. Minoux, and B. Maigret. A domain decomposition parallel processing algorithm for molecular dynamics simulations of systems of arbitrary connectivity. Computer Physics Communications, 103:170–186, 1997.

    Article  CAS  Google Scholar 

  12. A. T. Brünger. X-PLOR, Version 3.1: A System for X-ray Crystallography and NMR. Yale University Press, New Haven and London, 1992.

    Google Scholar 

  13. T. W. Clark, R. v. Hanxleden, J. A. McCammon, and L. R. Scott. Parallelizing molecular dynamics using spatial decomposition. In Proceedings of the Scalable High-Performance Computing Conference, pages 95–102, Los Alamitos, Calif., 1994. IEEE Computer Society Press.

    Google Scholar 

  14. T. Darden, D. York, and L. Pedersen. Particle mesh Ewald. An N-log(N) method for Ewald sums in large systems. J. Chem. Phys., 98:10089–10092, 1993.

    Article  CAS  Google Scholar 

  15. D. Frenkel and B. Smit. Understanding Molecular Simulation: From Algorithms to Applications. Academic Press, 1996.

    Google Scholar 

  16. E. Gamma, R. Helm, R. Johnson, and J. Vlissides. Design Patterns. Elements of Reusable Object-Oriented Software. Addison-Wesley, Reading, Massachusetts, 1995.

    Google Scholar 

  17. B. García-Archilla, J. M. Sanz-Serna, and R. D. Skeel. Long-time-step methods for oscillatory differential equations. SIAM J. Sci. Comput., 20(3):930–963, Oct. 20, 1998.

    Article  Google Scholar 

  18. B. García-Archilla, J. M. Sanz-Serna, and R. D. Skeel. The mollified impulse method for oscillatory differential equations. In D. F. Griffiths, G. A. Watson, editors, Numerical Analysis 1997, pages 111–123, London, 1998. Pitman.

    Google Scholar 

  19. A. Griewank, D. Juedes, and J. Utke. ADOL-C, a package for the automatic differentiation of algorithms written in C/C++. ACM Trans. Math. Softw., 22(2):131–167, 1996.

    Article  Google Scholar 

  20. H. Grubmüller. Dynamiksimulation sehr großer Makromoleküle auf einem Parallelrechner. Master’s thesis, Physik-Dept. der Tech. Univ. München, Munich, 1989.

    Google Scholar 

  21. H. Grubmüller, H. Heller, A. Windemuth, and K. Schulten. Generalized Verlet algorithm for efficient molecular dynamics simulations with long-range interactions. Molecular Simulation, 6:121–142, 1991.

    Article  Google Scholar 

  22. J. M. Haile. Molecular Dynamics Simulation. John Wiley and Sons, 1992.

    Google Scholar 

  23. S. Haney and J. Crotinger. How templates enable high-performance scientific computing in C++. Computing In Science & Engineering, l (4):66–72, Jul–Aug 1999. POOMA reference.

    Article  Google Scholar 

  24. G. A. Huber and J. A. McCammon. OOMPAA—Object-oriented model for probing assemblages of atoms. J. Comput. Phys, 151(1):264–282, May 1, 1999.

    Article  CAS  Google Scholar 

  25. D.D. Humphreys, R. A. Friesner, and B. J. Berne. A multiple-time-step molecular dynamics algorithm for macromolecules. J. Phys. Chem., 98(27):6885–6892, July 7, 1994.

    Article  CAS  Google Scholar 

  26. Y.-S. Hwang, R. Das, J. H. Saltz, M. Hodošček, and B. R. Brooks. Parallelizing molecular dynamics programs for distributed-memory machines. IEEE Computational Science & Engineering, 2(2):18–29, Summer 1995.

    Google Scholar 

  27. J. A. Izaguirre. Longer Time Steps for Molecular Dynamics. PhD thesis, University of Illinois at Urbana-Champaign, 1999. Also UIUC Technical Report UIUCDCS-R-99-2107. Available online via http://www.cs.uiuc.edu/research/tech-reports.html.

    Google Scholar 

  28. J. A. Izaguirre. Generalized mollified multiple time stepping methods for molecular dynamics. In A. Brandt, J. Bernholc, and K. Binder, editors, Multiscale Computational Methods in Chemistry and Physics, volume 177 of NATO Science Series: Series III Computer and Systems Sciences, pages 34–47. IOS Press, Amsterdam, Netherlands, Jan 2001.

    Google Scholar 

  29. J. A. Izaguirre, D. P. Catarello, J. M. Wozniak, and R. D. Skeel. Langevin stabilization of molecular dynamics. J. Chem. Phys., 114(5):2090–2098, Feb. 1, 2001.

    Article  CAS  Google Scholar 

  30. J. A. Izaguirre, T. Matthey, J. Willcock, Q. Ma, B. Moore, T. Slabach, and G. Viamontes. A tutorial on the prototyping of multiple time stepping integrators for molecular dynamics. Available from http://www.cse.nd.edu/~lcls/Protomol.html, 2001.

    Google Scholar 

  31. J. A. Izaguirre, S. Reich, and R. D. Skeel. Longer time steps for molecular dynamics. J. Chem. Phys., 110(19):9853–9864, May 15, 1999.

    Article  CAS  Google Scholar 

  32. J. A. Izaguirre, J. Willcock, T. Matthey, T. B. Slabach, T. Stein-bach, S. Stender, G. F. Viamontes, and J. Mohnke. ProtoMol: An object oriented framework for molecular dynamics. Available online via http://www.cse.nd.edu/~lcls/Protomol.html, 2000.

    Google Scholar 

  33. W. L. Jorgensen, J. Chandrasekhar, J. D. Madura, R. W. Impey, and M. L. Klein. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys., 79:926–935, 1983.

    Article  CAS  Google Scholar 

  34. L. Kalé, R. Skeel, M. Bhandarkar, R. Brunner, A. Gursoy, N. Krawetz, J. Phillips, A. Shinozaki, K. Varadarajan, and K. Schulten. NAMD2: Greater scalability for parallel molecular dynamics. J. Comp. Phys., 151:283–312, 1999.

    Article  Google Scholar 

  35. M. Karplus. Molecular dynamics: Applications to proteins. In J.-L. Rivail,, editor, Modelling of Molecular Structures and Properties, volume 71 of Studies in Physical and Theoretical Chemistry, pages 427–461, Amsterdam, 1990. Elsevier Science Publishers. Proceedings of an International Meeting.

    Google Scholar 

  36. A. R. Leach. Molecular Modelling, Principles and Applications. Addison Wesley Longman Limited, Essex, 1996.

    Google Scholar 

  37. M. López-Marcos, J. M. Sanz-Serna, and R. D. Skeel. Explicit symplectic integrators using Hessian-vector products. SIAM J. Sci. Comput., 18:223–238, Jan. 1997.

    Article  Google Scholar 

  38. Q. Ma, R. D. Skeel, and J. A. Izaguirre. Verlet-I/r-RESPA is nonlinearly unstable! In preparation, 2001.

    Google Scholar 

  39. A. D. MacKerell Jr., D. Bashford, M. Bellott, R. L. Dunbrack Jr., J. Evanseck, M. J. Field, S. Fischer, J. Gao, H. Guo, S. Ha, D. Joseph, L. Kuchnir, K. Kuczera, F. T. K. Lau, C. Mattos, S. Michnick, T. Ngo, D. T. Nguyen, B. Prod-horn, I. W. E. Reiher, B. Roux, M. Schlenkrich, J. Smith, R. Stote, J. Straub, M. Watanabe, J. Wiorkiewicz-Kuczera, D. Yin, and M. Karplus. All-hydrogen empirical potential for molecular modeling and dynamics studies of proteins using the CHARMM22 force field. J. Phys. Chem. B, 102:3586–3616, 1998.

    Article  CAS  Google Scholar 

  40. T. Matthey and J. P. Hansen. Evaluation of MPI’s one-sided communication mechanism for short-range molecular dynamics on the Origin2000. In PARA 2000 and Workshop on Applied Parallel Computing, 2000.

    Google Scholar 

  41. T. Matthey and J. A. Izaguirre. ProtoMol: A molecular dynamics framework with incremental parallelization. In Proc. of the Tenth SIAM Conf. on Parallel Processing for Scientific Computing (PP01), Proceedings in Applied Mathematics, Philadelphia, March 2001. Society for Industrial and Applied Mathematics.

    Google Scholar 

  42. M. Nelson, W. Humphrey, A. Gursoy, A. Dalke, L. Kalé, R. D. Skeel, and K. Schulten. NAMD — A parallel, object-oriented molecular dynamics program. Int. J. Supercomput. Appl. High Perform. Comput., 10:251–268, 1996.

    Article  Google Scholar 

  43. S. Reich. Dynamical systems, numerical integration, and exponentially small estimates, 1998. Habilitation Thesis.

    Google Scholar 

  44. A. Sandu and T. Schlick. Masking resonance artifacts in force-splitting methods for biomolecular simulations by extrapolative Langevin dynamics. J. Comput. Phys, 151(1):74–113, May 1, 1999.

    Article  CAS  Google Scholar 

  45. J. Sanz-Serna and M. Calvo. Numerical Hamiltonian Problems. Chapman and Hall, London, 1994.

    Google Scholar 

  46. T. Schlick. Some failures and successes of long-timestep approaches to biomolecular simulations. In P. Deuflhard, J. Hermans, B. Leimkuhler, A. Mark, S. Reich, and R. D. Skeel, editors, Algorithms for Macromolecular Modelling, volume 4 of Lecture Notes in Computational Science and Engineering, pages 221–250. Springer-Verlag, 1998.

    Google Scholar 

  47. T. Schlick, M. Mandziuk, R. D. Skeel, and K. Srinivas. Nonlinear resonance artifacts in molecular dynamics simulations. J. Comput. Phys., 139:1–29, 1998.

    Article  Google Scholar 

  48. SGI. The Standard Template Library: Introduction, http://www.sgi.com/Technology/STL/stl_introduction.html.

    Google Scholar 

  49. J. G. Siek and A. Lumsdaine. The Matrix Template Library: A unifying framework for numerical linear algebra. In International Symposium on Computing in Object-Oriented Parallel, 1998. Also available from http://www. lsc.nd.edu/downloads/research/mtl/papers/mtl_poosc. pdf.

    Google Scholar 

  50. R. D. Skeel. Macromolecular dynamics on a shared-memory multiprocessor. J. Comp. Chem., 12(2):175–179, January 1991.

    Article  CAS  Google Scholar 

  51. R. D. Skeel. Integration schemes for molecular dynamics and related applications. In M. Ainsworth, J. Levesley, and M. Marietta, editors, The Graduate Student’s Guide to Numerical Analysis, SSCM, pages 119–176. Springer-Ver lag, Berlin, 1999.

    Chapter  Google Scholar 

  52. R. D. Skeel and J. J. Biesiadecki. Symplectic integration with variable stepsize. Annals of Numer. Math., 1:191–198, 1994.

    Google Scholar 

  53. R. D. Skeel and J. Izaguirre. The five femtosecond time step barrier. In P. Deuflhard, J. Hermans, B. Leimkuhler, A. Mark, S. Reich, and R. D. Skeel, editors, Computational Molecular Dynamics: Challenges, Methods, Ideas, volume 4 of Lecture Notes in Computational Science and Engineering, pages 303–318. Springer-Ver lag, Berlin Heidelberg New York, Nov. 1998.

    Google Scholar 

  54. R. D. Skeel, G. Zhang, and T. Schlick. A family of symplectic integrators: stability, accuracy, and molecular dynamics applications. SIAM J. Sci. Comput., 18(l):203–222, Jan. 1997.

    Article  Google Scholar 

  55. B. Stroustrup. The C++ Programming Language. Addison-Wesley, third edition, 1997.

    Google Scholar 

  56. M. Tuckerman, B. J. Berne, and G. J. Martyna. Reversible multiple time scale molecular dynamics. J. Chem. Phys, 97(3):1990–2001, 1992.

    Article  CAS  Google Scholar 

  57. M. E. Tuckerman, D. Yarne, S.O. Samuelson, A. L. Hughes, and G. J. Martyna. Exploiting multiple levels of parallelism in Molecular Dynamics based calculations via modern techniques and software paradigms on distributed memory computers. CPC, 128:333-376, 2000.

    CAS  Google Scholar 

  58. T. Veldhuizen. Blitz++: The library that thinks it is a compiler. Conference presentation, Extreme! Computing Laboratory, Indiana University Computer Science Department, Sep. 1998. http://oonumerics.org/blitz/blitztalk.ps.gz.

    Google Scholar 

  59. T. Veldhuizen. Techniques for scientific c++. Technical Report 542, Indiana University Computer Science Department, 2000. http://extreme.indiana.edu/~tveldhui/papers/techniques/.

    Google Scholar 

  60. J. Vincent and K. M. Merz. A highly portable parallel implementation of Amber using the message passing interface standard. J. Comp. Chem., 11:1420–1427, 1995.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Izaguirre, J.A. et al. (2002). Overcoming Instabilities in Verlet-I/r-RESPA with the Mollified Impulse Method. In: Schlick, T., Gan, H.H. (eds) Computational Methods for Macromolecules: Challenges and Applications. Lecture Notes in Computational Science and Engineering, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56080-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-56080-4_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43756-7

  • Online ISBN: 978-3-642-56080-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics