Dynamical reweighting methods for Markov models

TL;DR

This paper reviews various dynamical reweighting methods for Markov State Models, highlighting their differences, limitations, and recent applications in analyzing biased molecular dynamics simulations.

Contribution

It provides a comprehensive overview and comparison of existing reweighting approaches, clarifying their methodological distinctions and practical uses.

Findings

Classifies reweighting methods into four categories

Discusses limitations of current approaches

Highlights recent successful applications

Abstract

Markov State Models (MSM) are widely used to elucidate dynamic properties of molecular systems from unbiased Molecular Dynamics (MD). However, the implementation of reweighting schemes for MSMs to analyze biased simulations, for example produced by enhanced sampling techniques, is still at an early stage of development. Several dynamical reweighing approaches have been proposed, which can be classified as approaches based on (i) Kramers rate theory, (ii) rescaling of the probability density flux, (iii) reweighting by formulating a likelihood function, (iv) path reweighting. We present the state-of-the-art and discuss the methodological differences of these methods, their limitations and recent applications.