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Comparing the Efficiencies of Stochastic Isothermal Molecular Dynamics Methods

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Abstract

Molecular dynamics typically incorporates a stochastic-dynamical device, a “thermostat,” in order to drive the system to the Gibbs (canonical) distribution at a prescribed temperature. When molecular dynamics is used to compute time-dependent properties, such as autocorrelation functions or diffusion constants, at a given temperature, there is a conflict between the need for the thermostat to perturb the time evolution of the system as little as possible and the need to establish equilibrium rapidly. In this article we define a quantity called the “efficiency” of a thermostat which relates the perturbation introduced by the thermostat to the rate of convergence of average kinetic energy to its equilibrium value. We show how to estimate this quantity analytically, carrying out the analysis for several thermostats, including the Nosé-Hoover-Langevin thermostat due to Samoletov et al. (J. Stat. Phys. 128:1321–1336, 2007) and a generalization of the “stochastic velocity rescaling” method suggested by Bussi et al. (J. Chem. Phys. 126:014101, 2007). We find efficiency improvements (proportional to the number of degrees of freedom) for the new schemes compared to Langevin Dynamics. Numerical experiments are presented which precisely confirm our theoretical estimates.

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References

  1. Bussi, G., Parrinello, M.: Stochastic thermostats: comparison of local and global schemes. Comput. Phys. Commun. 179, 26–29 (2008)

    Article  ADS  Google Scholar 

  2. Bussi, G., Donadio, D., Parrinello, M.: Canonical sampling through velocity rescaling. J. Chem. Phys. 126(1), 014101 (2007)

    Article  ADS  Google Scholar 

  3. Cancés, E., Legoll, F., Stoltz, G.: Theoretical and numerical comparison of some sampling methods for molecular dynamics. Modél. Math. Anal. Numér. 41(2), 351–389 (2007)

    Article  MATH  Google Scholar 

  4. Chae, K., Elvati, P., Violi, A.: Effect of molecular configuration on binary diffusion coefficients of linear alkanes. J. Phys. Chem. B 115(3), 500–506 (2011)

    Article  Google Scholar 

  5. Cottrell, D., Tupper, P.: Energy drift in molecular dynamics simulations. BIT Numer. Math. 47, 507–523 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Davidchack, R.L.: Discretization errors in molecular dynamics simulations with deterministic and stochastic thermostats. J. Comput. Phys. 229(24), 9323–9346 (2010)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  7. Ford, G.W., Kac, M.: On the quantum Langevin equation. J. Stat. Phys. 46(5–6), 803–810 (1987)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  8. Garrido, P., Gallavotti, G.: Boundary dissipation in a driven hard disk system. J. Stat. Phys. 126, 1201–1207 (2007)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  9. Hoover, W.: Canonical dynamics: equilibrium phase space distributions. Phys. Rev. A 31, 1695–1697 (1985)

    Article  ADS  Google Scholar 

  10. Hörmander, L.: Hypoelliptic second order differential equations. Acta Math. 119(1), 147–171 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  11. Hörmander, L.: The Analysis of Linear Partial Differential Operators. Springer, Berlin (1985)

    Google Scholar 

  12. Hünenberger, P.: Thermostat algorithms for molecular dynamics simulations. Adv. Polym. Sci. 173, 105–149. (2005)

    Google Scholar 

  13. Langevin, P.: On the theory of Brownian motion (Sur la thórie du mouvement Brownien). C. R. Acad. Sci. (Paris) 146, 530–533 (1908)

    MATH  Google Scholar 

  14. Legoll, F., Luskin, M., Moeckel, R.: Non-ergodicity of the Nose-Hoover thermostatted harmonic oscillator. Arch. Ration. Mech. Anal. 184, 449–463 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  15. Legoll, F., Luskin, M., Moeckel, R.: Non-ergodicity of Nosé-Hoover dynamics. Nonlinearity 22, 1673–1694 (2009)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  16. Leimkuhler, B., Noorizadeh, N., Theil, F.: A gentle stochastic thermostat for molecular dynamics. J. Stat. Phys. 135(2), 261–277 (2009)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  17. Mattingly, J.C., Stuart, A.M., Higham, D.J.: Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise. Stoch. Process. Appl. 101(2), 185–232 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  18. Nosé, S.: A unified formulation of the constant temperature molecular dynamics method. J. Chem. Phys. 81, 511–519 (1984)

    Article  ADS  Google Scholar 

  19. Oksendal, B.K.: Stochastic Differential Equations: An Introduction with Applications, 4th edn. Springer, Berlin (1995)

    Google Scholar 

  20. Samoletov, A., Chaplain, M.A.J., Dettmann, C.P.: Thermostats for “slow” configurational modes. J. Stat. Phys. 128, 1321–1336 (2007)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  21. Sun, J., Zhanga, L.: Temperature control algorithms in dual control volume grand canonical molecular dynamics simulations of hydrogen diffusion in palladium. J. Chem. Phys. 127, 164721 (2007)

    Article  ADS  Google Scholar 

  22. Williams, G.: Time-correlation functions and molecular motion. Chem. Soc. Rev. 7(89), 89–131 (1978)

    Article  Google Scholar 

  23. Yamashita, H., Endo, S., Wako, H., Kidera, A.: Sampling efficiency of molecular dynamics and Monte Carlo method in protein simulation. Chem. Phys. Lett. 342(3–4), 382–386 (2001)

    Article  ADS  Google Scholar 

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Correspondence to Ben Leimkuhler.

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Leimkuhler, B., Noorizadeh, E. & Penrose, O. Comparing the Efficiencies of Stochastic Isothermal Molecular Dynamics Methods. J Stat Phys 143, 921–942 (2011). https://doi.org/10.1007/s10955-011-0210-2

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  • DOI: https://doi.org/10.1007/s10955-011-0210-2

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