A review of Girsanov reweighting and of square root approximation for building molecular Markov state models
Luca Donati, Marcus Weber, Bettina G. Keller
Abstract
Dynamical reweighting methods permit to estimate kinetic observables of a stochastic process governed by a target potential Ṽ(x) from trajectories that have been generated at a different potential V(x). In this article, we present Girsanov reweighting and square root approximation: the first method reweights path probabilities exploiting the Girsanov theorem and can be applied to Markov state models 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 and then present two applications relevant to molecular dynamics, highlighting their strengths and weaknesses.