Inferring entropy from structure

Gil Ariel, Haim Diamant

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The thermodynamic definition of entropy can be extended to nonequilibrium systems based on its relation to information. To apply this definition in practice requires access to the physical system's microstates, which may be prohibitively inefficient to sample or difficult to obtain experimentally. It is beneficial, therefore, to relate the entropy to other integrated properties which are accessible out of equilibrium. We focus on the structure factor, which describes the spatial correlations of density fluctuations and can be directly measured by scattering. The information gained by a given structure factor regarding an otherwise unknown system provides an upper bound for the system's entropy. We find that the maximum-entropy model corresponds to an equilibrium system with an effective pair interaction. Approximate closed-form relations for the effective pair potential and the resulting entropy in terms of the structure factor are obtained. As examples, the relations are used to estimate the entropy of an exactly solvable model and two simulated systems out of equilibrium. The focus is on low-dimensional examples, where our method, as well as a recently proposed compression-based one, can be tested against a rigorous direct-sampling technique. The entropy inferred from the structure factor is found to be consistent with the other methods, superior for larger system sizes, and accurate in identifying global transitions. Our approach allows for extensions of the theory to more complex systems and to higher-order correlations.

Original languageEnglish
Article number022110
JournalPhysical Review E
Volume102
Issue number2
DOIs
StatePublished - Aug 2020

Bibliographical note

Publisher Copyright:
© 2020 American Physical Society.

Funding

We thank Roy Beck, Zohar Nussinov, and Salvatore Torquato for helpful suggestions. G.A. acknowledges support from The Israel Science Foundation's Grant No. 373/16 and the Deutsche Forschungsgemeinschaft (The German Research Foundation DFG) Grant No. BA1222/7-1. H.D. acknowledges support from the Israel Science Foundation (Grant No. 986/18).

FundersFunder number
Deutsche ForschungsgemeinschaftBA1222/7-1, 986/18
Israel Science Foundation373/16

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