Adaptively restrained particle simulations.

Interaction potentials used in particle simulations are typically written as a sum of terms which depend on just a few relative particle positions. Traditional simulation methods move all particles at each time step, and may thus spend a lot of time updating interparticle forces. In this Letter we introduce adaptively restrained particle simulations (ARPS) to speed up particle simulations by adaptively switching on and off positional degrees of freedom, while letting momenta evolve. We illustrate ARPS on several numerical experiments, including (a) a collision cascade example that demonstrates how ARPS make it possible to smoothly trade between precision and speed and (b) a polymer-in-solvent study that shows how one may efficiently determine static equilibrium properties with ARPS.

[1]  Aneesur Rahman,et al.  Correlations in the Motion of Atoms in Liquid Argon , 1964 .

[2]  Charles H. Bennett,et al.  Mass tensor molecular dynamics , 1975 .

[3]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

[4]  Piet Hut,et al.  A hierarchical O(N log N) force-calculation algorithm , 1986, Nature.

[5]  Leslie Greengard,et al.  A fast algorithm for particle simulations , 1987 .

[6]  D. Brenner,et al.  Empirical potential for hydrocarbons for use in simulating the chemical vapor deposition of diamond films. , 1990, Physical review. B, Condensed matter.

[7]  R. Elber,et al.  Modeling side chains in peptides and proteins: Application of the locally enhanced sampling and the simulated annealing methods to find minimum energy conformations , 1991 .

[8]  Mark E. Tuckerman,et al.  Reversible multiple time scale molecular dynamics , 1992 .

[9]  T. Darden,et al.  Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems , 1993 .

[10]  S. Rao Kosaraju,et al.  A decomposition of multidimensional point sets with applications to k-nearest-neighbors and n-body potential fields , 1995, JACM.

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

[12]  A. Voter.  Hyperdynamics: Accelerated Molecular Dynamics of Infrequent Events , 1997 .

[13]  Berk Hess,et al.  Improving efficiency of large time‐scale molecular dynamics simulations of hydrogen‐rich systems , 1999, Journal of computational chemistry.

[14]  Y. Sugita,et al.  Replica-exchange molecular dynamics method for protein folding , 1999 .

[15]  J. Monaghan.  Smoothed particle hydrodynamics , 2005 .

[16]  J. Izaguirre.  Longer Time Steps for Molecular Dynamics , 1999 .

[17]  S. Tremaine,et al.  A Class of Symplectic Integrators with Adaptive Time Step for Separable Hamiltonian Systems , 1999, astro-ph/9906322.

[18]  김삼묘,et al.  “Bioinformatics” 특집을 내면서 , 2000 .

[19]  Berend Smit,et al.  Understanding Molecular Simulation , 2001 .

[20]  Alexander D. MacKerell,et al.  Computational Biochemistry and Biophysics , 2001 .

[21]  Wolfgang Straßer,et al.  Deriving a Particle System from Continuum Mechanics for the Animation of Deformable Objects , 2003, IEEE Trans. Vis. Comput. Graph..

[22]  E. Hairer,et al.  Geometric Numerical Integration: Structure Preserving Algorithms for Ordinary Differential Equations , 2004 .

[23]  Sebastian Reich,et al.  Modified potential energy functions for constrained molecular dynamics , 1998, Numerical Algorithms.

[24]  D. Truhlar,et al.  QM/MM: what have we learned, where are we, and where do we go from here? , 2007 .

[25]  Wilfred F van Gunsteren,et al.  Multigraining: an algorithm for simultaneous fine-grained and coarse-grained simulation of molecular systems. , 2006, The Journal of chemical physics.

[26]  David Cohen.  Trigonometric schemes for stiff second-order SDEs , 2006 .

[27]  Klaus Schulten,et al.  Coarse grained protein-lipid model with application to lipoprotein particles. , 2006, The journal of physical chemistry. B.

[28]  D. Tieleman,et al.  The MARTINI force field: coarse grained model for biomolecular simulations. , 2007, The journal of physical chemistry. B.

[29]  Stéphane Redon,et al.  Adaptive torsion-angle quasi-statics: a general simulation method with applications to protein structure analysis and design , 2007, ISMB/ECCB.

[30]  Michele Parrinello,et al.  Energy Conservation in Adaptive Hybrid Atomistic/Coarse-Grain Molecular Dynamics. , 2007, Journal of chemical theory and computation.

[31]  G. Voth.  Coarse-Graining of Condensed Phase and Biomolecular Systems , 2008 .

[32]  Matej Praprotnik,et al.  Multiscale simulation of soft matter: from scale bridging to adaptive resolution. , 2008, Annual review of physical chemistry.

[33]  Andreas Heyden,et al.  Solving the equations of motion for mixed atomistic and coarse-grained systems , 2009 .

[34]  Petr Plechác,et al.  Implicit Mass-matrix Penalization of Hamiltonian Dynamics with Application to Exact Sampling of Stiff Systems , 2009, Multiscale Model. Simul..

[35]  T. Lelièvre,et al.  Free Energy Computations: A Mathematical Perspective , 2010 .

[36]  Bernd Ensing,et al.  Recent progress in adaptive multiscale molecular dynamics simulations of soft matter. , 2010, Physical chemistry chemical physics : PCCP.

[37]  Henk A. van der Vorst,et al.  Numerical Algorithms , 2011, Encyclopedia of Parallel Computing.

[38]  Stéphane Redon,et al.  Interactive physically-based structural modeling of hydrocarbon systems , 2012, J. Comput. Phys..

[39]  Francesco Rao,et al.  Thermodynamics and kinetics of large‐time‐step molecular dynamics , 2012, J. Comput. Chem..