# Non-equilibrium Markov state modeling of periodically driven biomolecules

## TL;DR

This paper introduces a method for constructing Markov state models for biomolecules under periodic external driving, enabling the study of non-equilibrium dynamics beyond traditional equilibrium-based models.

## Contribution

The paper presents a novel approach to build Markov state models for periodically driven biomolecular systems, extending modeling capabilities to non-equilibrium conditions.

## Key findings

- Successfully applied to alanine dipeptide under electric field
- Demonstrates modeling of non-equilibrium biomolecular dynamics
- Extends Markov state modeling to driven systems

## Abstract

Molecular dynamics simulations allow to study the structure and dynamics of single biomolecules in microscopic detail. However, many processes occur on time scales beyond the reach of fully atomistic simulations and require coarse-grained multiscale models. While systematic approaches to construct such models have become available, these typically rely on microscopic dynamics that obey detailed balance. In vivo, however, biomolecules are constantly driven away from equilibrium in order to perform specific functions and thus break detailed balance. Here we introduce a method to construct Markov state models for systems that are driven through periodically changing one (or several) external parameter. We illustrate the method for alanine dipeptide, a widely used benchmark molecule for computational methods, exposed to a time-dependent electric field.