Evidence map›Paper›PMID 37353499›Full record

ArticleNature communications2023

Nonequilibrium thermodynamics of the asymmetric Sherrington-Kirkpatrick model.

Miguel Aguilera, Masanao Igarashi, Hideaki Shimazaki

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. A note on the dynamics of extended-context disordered kinetic spin models.Journal of physics. A, Mathematical and theoretical · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Miguel AguileraBCAM - Basque Center for Applied Mathematics, Bilbao, Spain. sci@maguilera.net.ORCID http://orcid.org/0000-0002-3366-4706
Masanao IgarashiGraduate School of Engineering, Hokkaido University, Sapporo, Japan.
Hideaki ShimazakiGraduate School of Informatics, Kyoto University, Kyoto, Japan.ORCID http://orcid.org/0000-0001-7794-3064

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Marie Skłodowska-Curie Actions (H2020 Excellent Science - Marie Skłodowska-Curie Actions) 892715"la Caixa" Foundation (Caixa Foundation) LCF/BQ/PI23/11970024MEXT | Japan Society for the Promotion of Science (JSPS) 20K11709, 21H0524MEXT | National Institutes of Natural Sciences (NINS) 1112005, 01112102
6 · The paper itself

Abstract

Most natural systems operate far from equilibrium, displaying time-asymmetric, irreversible dynamics characterized by a positive entropy production while exchanging energy and matter with the environment. Although stochastic thermodynamics underpins the irreversible dynamics of small systems, the nonequilibrium thermodynamics of larger, more complex systems remains unexplored. Here, we investigate the asymmetric Sherrington-Kirkpatrick model with synchronous and asynchronous updates as a prototypical example of large-scale nonequilibrium processes. Using a path integral method, we calculate a generating functional over trajectories, obtaining exact solutions of the order parameters, path entropy, and steady-state entropy production of infinitely large networks. Entropy production peaks at critical order-disorder phase transitions, but is significantly larger for quasi-deterministic disordered dynamics. Consequently, entropy production can increase under distinct scenarios, requiring multiple thermodynamic quantities to describe the system accurately. These results contribute to developing an exact analytical theory of the nonequilibrium thermodynamics of large-scale physical and biological systems and their phase transitions.

Indexed as

ThermodynamicsEntropy

Identifiers

PMID37353499
PMCPMC10290083

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.