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arXiv · 2602.22440

An Information-theoretic Collective Variable for Configurational Entropy

Ashley Z. Guo·Kaelyn Chang·Nicholas J. Corrente
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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 a general per-configuration structural descriptor that tracks configurational entropy changes 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 organizational signatures across a variety of molecular systems and discretization resolutions. By establishing a data compression-based structural complexity metric as a practical proxy for configurational entropy, this framework lays a foundation for future entropy-driven materials design and optimization strategies.

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BibTeXRIS

Ashley Z. Guo, Kaelyn Chang, Nicholas J. Corrente. 2026-02-25. An Information-theoretic Collective Variable for Configurational Entropy. https://doi.org/10.1039/d6me00083e

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