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 |
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 |