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A review of Girsanov Reweighting and of Square Root Approximation for building molecular Markov State Models

  • Dynamical reweighting methods permit to estimate kinetic observables of a stochastic process governed by a target potential U(x) from trajectories that have been generated at a different potential V(x). In this article, we present Girsanov reweighting and Square Root Approximation (SqRA): the first method reweights path probabilities exploiting the Girsanov theorem and can be applied to Markov State Models (MSMs) to reweight transition probabilities; the second method was originally developed to discretize the Fokker-Planck operator into a transition rate matrix, but here we implement it into a reweighting scheme for transition rates. We begin by reviewing the theoretical background of the methods, then present two applications relevant to Molecular Dynamics (MD), highlighting their strengths and weaknesses.
Metadaten
Author:Luca DonatiORCiD, Marcus WeberORCiD, Bettina G. KellerORCiD
Document Type:Article
Parent Title (English):Journal of Mathematical Physics
Volume:63
Issue:12
First Page:123306-1
Last Page:123306-21
Publisher:AIP Publishing
Date of first Publication:2022/12/14
Page Number:21
DOI:https://doi.org/10.1063/5.0127227
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