Literature DB >> 31702252

Universal and Accessible Entropy Estimation Using a Compression Algorithm.

Ram Avinery1, Micha Kornreich1, Roy Beck1.   

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.

Entities:  

Year:  2019        PMID: 31702252     DOI: 10.1103/PhysRevLett.123.178102

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.185


  4 in total

1.  Machine-learning iterative calculation of entropy for physical systems.

Authors:  Amit Nir; Eran Sela; Roy Beck; Yohai Bar-Sinai
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-19       Impact factor: 11.205

2.  Multiscale structural complexity of natural patterns.

Authors:  Andrey A Bagrov; Ilia A Iakovlev; Askar A Iliasov; Mikhail I Katsnelson; Vladimir V Mazurenko
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-18       Impact factor: 11.205

3.  Estimating Differential Entropy using Recursive Copula Splitting.

Authors:  Gil Ariel; Yoram Louzoun
Journal:  Entropy (Basel)       Date:  2020-02-19       Impact factor: 2.524

4.  Hyperuniformity and phase enrichment in vortex and rotor assemblies.

Authors:  Naomi Oppenheimer; David B Stein; Matan Yah Ben Zion; Michael J Shelley
Journal:  Nat Commun       Date:  2022-02-10       Impact factor: 17.694

  4 in total

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