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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.
Author:Luca DonatiORCiD, Marcus WeberORCiD, Bettina G. KellerORCiD
Document Type:Article
Parent Title (English):Journal of Mathematical Physics
First Page:123306-1
Last Page:123306-21
Publisher:AIP Publishing
Date of first Publication:2022/12/14
Page Number:21
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