Dynamical reweighting methods for Markov models

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.

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• Understanding the role of predictive time delay and biased propagator in RAVE
• Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles
• Markov State Models for Tracking Reaction Dynamics on Catalytic Nanoparticles
• Assessing Position-Dependent Diffusion from Biased Simulations and Markov State Model Analysis