Universal and accessible entropy estimation using a compression algorithm

TL;DR

This paper introduces a universal, efficient entropy estimation method using lossless compression algorithms, applicable across various simulated systems, enabling better detection of system states and free-energy calculations.

Contribution

The authors propose a model-independent entropy estimation scheme based on compression algorithms, validated on complex simulated systems, improving detection of system states and computational efficiency.

Findings

Accurate entropy estimation comparable to benchmark methods.

Enhanced detection of folded states in protein simulations.

Efficient computation enabling real-time entropy fluctuation analysis.

Abstract

Entropy and free-energy estimation are key in thermodynamic characterization of simulated systems ranging from spin models through polymers, colloids, protein structure, and drug-design. Current techniques suffer from being model specific, requiring abundant computation resources and simulation at conditions far from the studied realization. Here, we present a universal scheme to calculate entropy using lossless compression algorithms and validate it on simulated systems of increasing complexity. Our results show accurate entropy values compared to benchmark calculations while being computationally effective. In molecular-dynamics simulations of protein folding, we exhibit unmatched detection capability of the folded states by measuring previously undetectable entropy fluctuations along the simulation timeline. Such entropy evaluation opens a new window onto the dynamics of complex systems and allows efficient free-energy calculations.