We give a mathematical framework for weighted ensemble (WE) sampling, a binning and resampling technique for efficiently computing probabilities in molecular dynamics. We prove that WE sampling is unbiased in a very general setting that includes adaptive binning. We show that when WE is used for stationary calculations in tandem with a coarse model, the coarse model can be used to optimize the allocation of replicas in the bins.
Accepté le :
DOI : 10.1051/m2an/2017046
Mots-clés : Molecular dynamics, Markov chains, stationary distributions, long time dynamics, coarse graining, resampling, weighted ensemble
@article{M2AN_2018__52_4_1219_0, author = {Aristoff, David}, title = {Analysis and optimization of weighted ensemble sampling}, journal = {ESAIM: Mathematical Modelling and Numerical Analysis }, pages = {1219--1238}, publisher = {EDP-Sciences}, volume = {52}, number = {4}, year = {2018}, doi = {10.1051/m2an/2017046}, mrnumber = {3875284}, zbl = {07006974}, language = {en}, url = {http://archive.numdam.org/articles/10.1051/m2an/2017046/} }
TY - JOUR AU - Aristoff, David TI - Analysis and optimization of weighted ensemble sampling JO - ESAIM: Mathematical Modelling and Numerical Analysis PY - 2018 SP - 1219 EP - 1238 VL - 52 IS - 4 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/m2an/2017046/ DO - 10.1051/m2an/2017046 LA - en ID - M2AN_2018__52_4_1219_0 ER -
%0 Journal Article %A Aristoff, David %T Analysis and optimization of weighted ensemble sampling %J ESAIM: Mathematical Modelling and Numerical Analysis %D 2018 %P 1219-1238 %V 52 %N 4 %I EDP-Sciences %U http://archive.numdam.org/articles/10.1051/m2an/2017046/ %R 10.1051/m2an/2017046 %G en %F M2AN_2018__52_4_1219_0
Aristoff, David. Analysis and optimization of weighted ensemble sampling. ESAIM: Mathematical Modelling and Numerical Analysis , Tome 52 (2018) no. 4, pp. 1219-1238. doi : 10.1051/m2an/2017046. http://archive.numdam.org/articles/10.1051/m2an/2017046/
[1] Forward flux sampling-type schemes for simulating rare events: Efficiency analysis. J. Chem. Phys. 124 (2006) 463102. | DOI
, and ,[2] A mathematical framework for exact milestoning. Multiscale Model. Simul. 14 (2016) 301–322. | DOI | MR | Zbl
, and ,[3] Beyond Microscopic Reversibility: Are Observable Nonequilibrium Processes Precisely Reversible? J. Chem. Theory Comput. 7 (2011) 2520–2527. | DOI
and ,[4] Exact milestoning. J. Chem. Phys. 142 (2015) 094102.
and ,[5] Steady-state simulations using weighted ensemble path sampling. J. Chem. Phys. 133 (2010) 014110. | DOI
and , and ,[6] Adaptive Multilevel Splitting for Rare Event Analysis. Stoch. Anal. Appl. 25 (2007) 417–443. | DOI | MR | Zbl
and ,[7] A multiple replica approach to simulate reactive trajectories. J. Chem. Phys. 134 (2011) 054108. | DOI
, , and ,[8] Analysis of the accelerated weighted ensemble methodology, Supplement, Discrete and Continuous Dynamical Systems (2013). | MR
, , and .[9] Computing reaction rates in bio-molecular systems using discrete macro-states, Innovations in Biomolecular Modeling and Simulations, RSC publishing (2012).
and ,[10] Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications. Probability and Its Applications. Springer (2004). | MR | Zbl
,[11] Particle methods: an introduction with applications. ESAIM: Proc. 44 (2014) 1–46. | DOI | MR | Zbl
and ,[12] Genealogical particle analysis of rare events. Ann. Appl. Probab. 15 (2005) 2496–2534. | DOI | MR | Zbl
and ,[13] Sequential Monte Carlo Methods in Practice, Statistics for Engineering and Information Science, Springer (2001). | DOI | MR | Zbl
, and ,[14] Probabiltiy: Theory and Examples. Duxbury Press, 3rd edn (2005). | Zbl
,[15] Computing time scales from reaction coordinates by Milestoning. J. Chem. Phys. 120 (2004) 10880–10889. | DOI
and ,[16] Boxed Molecular Dynamics: Decorrelation Time Scales and the Kinetic Master Equation. J. Chem. Theory Comput. 7 (2011) 1244–1252. | DOI
, and ,[17] Free Energy Transduction and Biochemical Cycle Kinetics. Dover, New York (1989). | DOI
,[18] Weighted-ensemble Brownian dynamics simulations for protein association reactions. Biophys. J. 70 (1996) 97–110. | DOI
and ,[19] Transition Path Theory for Markov jump processes. Multiscale Model. Simul. 7 (2009) 1192–1219. | DOI | MR | Zbl
, and ,[20] On the approximation quality of Markov State Models (2010). | MR | Zbl
, and ,[21] Metastability and Markov State Models in Molecular Dynamics. Courant Lecture Notes. AMS (2013). | DOI | MR | Zbl
and ,[22] Molecular modeling and simulation: an interdisciplinary guide (2010). | DOI | MR | Zbl
,[23] Simultaneous computation of dynamical and equilibrium information using a weighted ensemble of trajectories. J. Chem. Theory Comput. 10 (2014) 2658–266. | DOI
, , , and , and ,[24] Estimating first-passage time distributions from weighted ensemble simulations and non-Markovian analyses. Protein Sci. 25 (2016) 67–78. | DOI
, , and ,[25] Trajectory Stratification of Stochastic Dynamics. Preprint, (2017). | arXiv | MR
, , , and ,[26] Exact rate calculations by trajectory parallelization and tilting. J. Chem. Phys. 131 (2009) 1–7, 0904.3763 | DOI
and ,[27] A novel path sampling method for the calculation of rate constants. J. Chemical Phys. 118 (2003) 7762–7774. | DOI
, and ,[28] Umbrella sampling for nonequilibrium processes. J. Chem. Phys. 127 (2007) 154112. | DOI
, and ,[29] The “weighted ensemble” path sampling method is exact for a broad class of stochastic processes and binning procedures. J. Chem. Phys. 132 (2010) 05417. | DOI
, and ,Cité par Sources :