A generalized class of strongly stable and dimension-free T-RPMD integrators

Recent work shows that strong stability and dimensionality freedom are essential for robust numerical integration of thermostatted ring-polymer molecular dynamics (T-RPMD) and path-integral molecular dynamics, without which standard integrators exhibit non-ergodicity and other pathologies [R. Korol et al., J. Chem. Phys. 151, 124103 (2019) and R. Korol et al., J. Chem. Phys. 152, 104102 (2020)]. In particular, the BCOCB scheme, obtained via Cayley modification of the standard BAOAB scheme, features a simple reparametrization of the free ring-polymer sub-step that confers strong stability and dimensionality freedom and has been shown to yield excellent numerical accuracy in condensed-phase systems with large time steps. Here, we introduce a broader class of T-RPMD numerical integrators that exhibit strong stability and dimensionality freedom, irrespective of the Ornstein-Uhlenbeck friction schedule. In addition to considering equilibrium accuracy and time step stability as in previous work, we evaluate the integrators on the basis of their rates of convergence to equilibrium and their efficiency at evaluating equilibrium expectation values. Within the generalized class, we find BCOCB to be superior with respect to accuracy and efficiency for various configuration-dependent observables, although other integrators within the generalized class perform better for velocity-dependent quantities. Extensive numerical evidence indicates that the stated performance guarantees hold for the strongly anharmonic case of liquid water. Both analytical and numerical results indicate that BCOCB excels over other known integrators in terms of accuracy, efficiency, and stability with respect to time step for practical applications.

[1]  B. Berne,et al.  Efficient molecular dynamics and hybrid Monte Carlo algorithms for path integrals , 1993 .

[2]  Yury V. Suleimanov,et al.  RPMDrate: Bimolecular chemical reaction rates from ring polymer molecular dynamics , 2013, Comput. Phys. Commun..

[3]  Nawaf Bou-Rabee Cayley Splitting for Second-Order Langevin Stochastic Partial Differential Equations , 2017, 1707.05603.

[4]  Essential Linear Algebra with Applications , 2014 .

[5]  C. Givens,et al.  A class of Wasserstein metrics for probability distributions. , 1984 .

[6]  Ian R. Craig,et al.  Chemical reaction rates from ring polymer molecular dynamics. , 2005, The Journal of chemical physics.

[7]  G. Pavliotis Stochastic Processes and Applications: Diffusion Processes, the Fokker-Planck and Langevin Equations , 2014 .

[8]  Mari Paz Calvo,et al.  Instabilities and Inaccuracies in the Integration of Highly Oscillatory Problems , 2009, SIAM J. Sci. Comput..

[9]  G. Strang On the Construction and Comparison of Difference Schemes , 1968 .

[10]  R. Skeel,et al.  Quasi-reliable estimates of effective sample size , 2017, IMA Journal of Numerical Analysis.

[11]  H. Trotter On the product of semi-groups of operators , 1959 .

[12]  Michael J. Willatt,et al.  Boltzmann-conserving classical dynamics in quantum time-correlation functions: "Matsubara dynamics". , 2015, The Journal of chemical physics.

[13]  Peter W. Glynn,et al.  Stochastic Simulation: Algorithms and Analysis , 2007 .

[14]  Nawaf Bou-Rabee,et al.  Time Integrators for Molecular Dynamics , 2013, Entropy.

[15]  Roman Korol,et al.  Dimension-free path-integral molecular dynamics without preconditioning , 2020, The Journal of chemical physics.

[16]  Roberto Car,et al.  Nuclear quantum effects in water. , 2008, Physical review letters.

[17]  Steven Vandenbrande,et al.  i-PI 2.0: A universal force engine for advanced molecular simulations , 2018, Comput. Phys. Commun..

[18]  C. Villani Optimal Transport: Old and New , 2008 .

[19]  M.G.B. Drew,et al.  The art of molecular dynamics simulation , 1996 .

[20]  M. Parrinello,et al.  Canonical sampling through velocity rescaling. , 2007, The Journal of chemical physics.

[21]  Michele Ceriotti,et al.  Nuclear quantum effects enter the mainstream , 2018, 1803.01037.

[22]  David E Manolopoulos,et al.  On the short-time limit of ring polymer molecular dynamics. , 2006, The Journal of chemical physics.

[23]  Thomas F. Miller,et al.  Ring-polymer molecular dynamics: quantum effects in chemical dynamics from classical trajectories in an extended phase space. , 2013, Annual review of physical chemistry.

[24]  Jianshu Cao,et al.  The formulation of quantum statistical mechanics based on the Feynman path centroid density. II. Dynamical properties , 1994 .

[25]  Ian R. Craig,et al.  Quantum statistics and classical mechanics: real time correlation functions from ring polymer molecular dynamics. , 2004, The Journal of chemical physics.

[26]  Thomas F. Miller,et al.  Quantum diffusion in liquid para-hydrogen from ring-polymer molecular dynamics. , 2005, The Journal of chemical physics.

[27]  Ian R. Craig,et al.  A refined ring polymer molecular dynamics theory of chemical reaction rates. , 2005, The Journal of chemical physics.

[28]  Mark E. Tuckerman,et al.  Algorithms and novel applications based on the isokinetic ensemble. I. Biophysical and path integral molecular dynamics , 2003 .

[29]  Berend Smit,et al.  Understanding molecular simulation: from algorithms to applications , 1996 .

[30]  Nawaf Bou-Rabee,et al.  Two-scale coupling for preconditioned Hamiltonian Monte Carlo in infinite dimensions , 2019 .

[31]  Jianfeng Lu,et al.  Continuum limit and preconditioned Langevin sampling of the path integral molecular dynamics , 2018, J. Comput. Phys..

[32]  M. Parrinello,et al.  Study of an F center in molten KCl , 1984 .

[33]  Alison L Gibbs,et al.  On Choosing and Bounding Probability Metrics , 2002, math/0209021.

[34]  Diego Pallara,et al.  Spectrum of Ornstein-Uhlenbeck Operators in Lp Spaces with Respect to Invariant Measures , 2002 .

[35]  Peter G. Wolynes,et al.  Exploiting the isomorphism between quantum theory and classical statistical mechanics of polyatomic fluids , 1981 .

[36]  Michele Parrinello,et al.  Accelerating the convergence of path integral dynamics with a generalized Langevin equation. , 2011, The Journal of chemical physics.

[37]  B. Leimkuhler,et al.  Rational Construction of Stochastic Numerical Methods for Molecular Sampling , 2012, 1203.5428.

[38]  J. M. Sanz-Serna,et al.  Numerical Hamiltonian Problems , 1994 .

[39]  Michael J. Willatt,et al.  Communication: Relation of centroid molecular dynamics and ring-polymer molecular dynamics to exact quantum dynamics. , 2015, The Journal of chemical physics.

[40]  David E Manolopoulos,et al.  Comparison of path integral molecular dynamics methods for the infrared absorption spectrum of liquid water. , 2008, The Journal of chemical physics.

[41]  Charles J. Geyer,et al.  Practical Markov Chain Monte Carlo , 1992 .

[42]  Thomas F. Miller,et al.  Comparison of Experimental vs Theoretical Abundances of 13CH3D and 12CH2D2 for Isotopically Equilibrated Systems from 1 to 500 °C , 2019, ACS Earth and Space Chemistry.

[43]  Michele Parrinello,et al.  Efficient stochastic thermostatting of path integral molecular dynamics. , 2010, The Journal of chemical physics.

[44]  J. Vaníček,et al.  Path integral evaluation of equilibrium isotope effects. , 2009, The Journal of chemical physics.

[45]  M. Tuckerman,et al.  An open-chain imaginary-time path-integral sampling approach to the calculation of approximate symmetrized quantum time correlation functions. , 2018, The Journal of chemical physics.

[46]  Edgar A. Engel,et al.  Ab initio thermodynamics of liquid and solid water , 2018, Proceedings of the National Academy of Sciences.

[47]  Thorsten Gerber,et al.  Handbook Of Mathematical Functions , 2016 .

[48]  Thomas E. Markland,et al.  Competing quantum effects in the dynamics of a flexible water model. , 2009, The Journal of chemical physics.

[49]  Jian Liu,et al.  A unified thermostat scheme for efficient configurational sampling for classical/quantum canonical ensembles via molecular dynamics. , 2017, The Journal of chemical physics.

[50]  Jian Liu,et al.  Path integral Liouville dynamics for thermal equilibrium systems. , 2014, The Journal of chemical physics.

[51]  D. Manolopoulos,et al.  How to remove the spurious resonances from ring polymer molecular dynamics. , 2014, The Journal of chemical physics.

[52]  Robert D. Skeel,et al.  Comparing Markov Chain Samplers for Molecular Simulation , 2017, Entropy.

[53]  A. Sokal Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms , 1997 .

[54]  Dominik Marx,et al.  On the applicability of centroid and ring polymer path integral molecular dynamics for vibrational spectroscopy. , 2009, The Journal of chemical physics.

[55]  A. Sokal,et al.  The pivot algorithm: A highly efficient Monte Carlo method for the self-avoiding walk , 1988 .

[56]  Jesús María Sanz-Serna,et al.  Geometric integrators and the Hamiltonian Monte Carlo method , 2017, Acta Numerica.

[57]  D. C. Rapaport,et al.  The Art of Molecular Dynamics Simulation: Contents , 2004 .

[58]  Jian Liu,et al.  A simple and accurate algorithm for path integral molecular dynamics with the Langevin thermostat. , 2016, The Journal of chemical physics.

[59]  Nawaf Bou-Rabee,et al.  Cayley modification for strongly stable path-integral and ring-polymer molecular dynamics. , 2019, The Journal of chemical physics.

[60]  Thomas F. Miller,et al.  Quantum diffusion in liquid water from ring polymer molecular dynamics. , 2005, The Journal of chemical physics.

[61]  J. M. Sanz-Serna,et al.  Randomized Hamiltonian Monte Carlo , 2015, 1511.09382.

[62]  Michele Ceriotti,et al.  Fine tuning classical and quantum molecular dynamics using a generalized Langevin equation. , 2017, The Journal of chemical physics.