# Efficient exploration of discrete energy landscapes

## TL;DR

This paper introduces a Markov chain Monte-Carlo approach to efficiently approximate transition probabilities in large energy landscapes, enabling better analysis of complex physical and chemical processes with reduced computational effort.

## Contribution

The paper presents a novel Monte-Carlo method for estimating transition probabilities in large energy landscapes, improving computational efficiency over full enumeration.

## Key findings

- Accurate probability estimates achieved with reduced computational cost.
- Method successfully applied to number partitioning and RNA switch landscapes.
- Significant reduction in computational resources compared to traditional methods.

## Abstract

Many physical and chemical processes, such as folding of biopolymers, are best described as dynamics on large combinatorial energy landscapes. A concise approximate description of dynamics is obtained by partitioning the micro-states of the landscape into macro-states. Since most landscapes of interest are not tractable analytically, the probabilities of transitions between macro-states need to be extracted numerically from the microscopic ones, typically by full enumeration of the state space. Here we propose to approximate transition probabilities by a Markov chain Monte-Carlo method. For landscapes of the number partitioning problem and an RNA switch molecule we show that the method allows for accurate probability estimates with significantly reduced computational cost.