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
Keywords: Molecular dynamics, Markov chains, stationary distributions, long time dynamics, coarse graining, resampling, weighted ensemble
Aristoff, David 1
@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},
year = {2018},
publisher = {EDP Sciences},
volume = {52},
number = {4},
doi = {10.1051/m2an/2017046},
mrnumber = {3875284},
zbl = {07006974},
language = {en},
url = {https://www.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 - https://www.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 https://www.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
[1] , and , Forward flux sampling-type schemes for simulating rare events: Efficiency analysis. J. Chem. Phys. 124 (2006) 463102. | DOI
[2] , and , A mathematical framework for exact milestoning. Multiscale Model. Simul. 14 (2016) 301–322. | MR | Zbl | DOI
[3] and , Beyond Microscopic Reversibility: Are Observable Nonequilibrium Processes Precisely Reversible? J. Chem. Theory Comput. 7 (2011) 2520–2527. | DOI
[4] and , Exact milestoning. J. Chem. Phys. 142 (2015) 094102.
[5] and , and , Steady-state simulations using weighted ensemble path sampling. J. Chem. Phys. 133 (2010) 014110. | DOI
[6] and , Adaptive Multilevel Splitting for Rare Event Analysis. Stoch. Anal. Appl. 25 (2007) 417–443. | MR | Zbl | DOI
[7] , , and , A multiple replica approach to simulate reactive trajectories. J. Chem. Phys. 134 (2011) 054108. | DOI
[8] , , and . Analysis of the accelerated weighted ensemble methodology, Supplement, Discrete and Continuous Dynamical Systems (2013). | MR
[9] and , Computing reaction rates in bio-molecular systems using discrete macro-states, Innovations in Biomolecular Modeling and Simulations, RSC publishing (2012).
[10] , Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications. Probability and Its Applications. Springer (2004). | MR | Zbl
[11] and , Particle methods: an introduction with applications. ESAIM: Proc. 44 (2014) 1–46. | MR | Zbl | DOI
[12] and , Genealogical particle analysis of rare events. Ann. Appl. Probab. 15 (2005) 2496–2534. | MR | Zbl | DOI
[13] , and , Sequential Monte Carlo Methods in Practice, Statistics for Engineering and Information Science, Springer (2001). | MR | Zbl | DOI
[14] , Probabiltiy: Theory and Examples. Duxbury Press, 3rd edn (2005). | Zbl
[15] and , Computing time scales from reaction coordinates by Milestoning. J. Chem. Phys. 120 (2004) 10880–10889. | DOI
[16] , and , Boxed Molecular Dynamics: Decorrelation Time Scales and the Kinetic Master Equation. J. Chem. Theory Comput. 7 (2011) 1244–1252. | DOI
[17] , Free Energy Transduction and Biochemical Cycle Kinetics. Dover, New York (1989). | DOI
[18] and , Weighted-ensemble Brownian dynamics simulations for protein association reactions. Biophys. J. 70 (1996) 97–110. | DOI
[19] , and , Transition Path Theory for Markov jump processes. Multiscale Model. Simul. 7 (2009) 1192–1219. | Zbl | MR | DOI
[20] , and , On the approximation quality of Markov State Models (2010). | MR | Zbl
[21] and , Metastability and Markov State Models in Molecular Dynamics. Courant Lecture Notes. AMS (2013). | MR | Zbl | DOI
[22] , Molecular modeling and simulation: an interdisciplinary guide (2010). | MR | Zbl | DOI
[23] , , , and , and , Simultaneous computation of dynamical and equilibrium information using a weighted ensemble of trajectories. J. Chem. Theory Comput. 10 (2014) 2658–266. | DOI
[24] , , and , Estimating first-passage time distributions from weighted ensemble simulations and non-Markovian analyses. Protein Sci. 25 (2016) 67–78. | DOI
[25] , , , and , Trajectory Stratification of Stochastic Dynamics. Preprint, (2017). | arXiv | MR
[26] and , Exact rate calculations by trajectory parallelization and tilting. J. Chem. Phys. 131 (2009) 1–7, 0904.3763 | DOI
[27] , and , A novel path sampling method for the calculation of rate constants. J. Chemical Phys. 118 (2003) 7762–7774. | DOI
[28] , and , Umbrella sampling for nonequilibrium processes. J. Chem. Phys. 127 (2007) 154112. | DOI
[29] , and , 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
Cité par Sources :





