An Information-theoretic Collective Variable for Configurational Entropy

TL;DR

This paper introduces the computable information density (CID), an information-theoretic metric derived from data compression, to quantify configurational entropy in molecular systems, enabling entropy measurement without prior structural knowledge.

Contribution

The paper presents CID as a novel, generalizable measure of configurational entropy applicable across diverse molecular systems, validated through multiple complex simulations.

Findings

CID accurately reflects local and long-range structural organization.

CID requires no prior knowledge of structural features.

CID is effective across various molecular systems and resolutions.

Abstract

Entropy governs molecular self-assembly, phase transitions, and material stability, yet remains challenging to quantify and directly control in molecular systems. Here, we demonstrate that the computable information density (CID), a data compression-based information theoretic metric, provides an instantaneous general measure of configurational entropy in molecular dynamics simulations, reflecting both local and long-range structural organization. We validate the CID across systems of increasing complexity, beginning with single-component Lennard-Jones melting before examining binary phase separation, polymer condensation and dispersion, and assembly of amorphous carbon networks at multiple densities. Unlike conventional order parameters, CID requires no a priori knowledge of relevant structural features and captures entropic signatures across a variety of molecular systems and discretization resolutions. By establishing entropy as a directly accessible structural metric, this framework lays a foundation for future entropy-driven materials design and optimization strategies.