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

L. Donati, M. Weber, B. G. Keller – 2022

Dynamical reweighting methods permit to estimate kinetic observables of a stochastic process governed by a target potential V~(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.

Title
A review of Girsanov Reweighting and of Square Root Approximation for building molecular Markov State Models
Author
L. Donati, M. Weber, B. G. Keller
Date
2022
Identifier
https://doi.org/10.48550/arXiv.2209.10544
Source(s)
Type
Text
BibTeX Code
arXiv:2209.10544