1537 papers
Statistical mechanics explores how the chaotic motion of countless tiny particles gives rise to the predictable laws governing heat, pressure, and phase transitions. This field bridges the gap between the microscopic world of atoms and the macroscopic reality we experience daily, offering deep insights into why materials behave the way they do.
On Gist.Science, we process every new preprint in this category as it appears on arXiv to make these complex findings accessible to everyone. For each paper, we provide both a plain-language explanation for the curious reader and a detailed technical summary for specialists, ensuring that groundbreaking research is never lost behind a wall of jargon.
Below are the latest papers in statistical mechanics, freshly curated and summarized to help you understand the cutting edge of this fascinating discipline.
This paper investigates how the internal pulsation of anisotropic, elliptical particles drives the emergence of synchronized deformation waves and diverse collective patterns in dense assemblies, providing a hydrodynamic framework that successfully captures these self-organizing dynamics inspired by cardiac tissue behavior.
This study reveals that the recovery of Gaussian statistics in long polymer chains is not caused by the disappearance of persistent local structural heterogeneities, but rather by a statistical masking effect where the accumulation of random conformational segments obscures the non-Gaussian signatures of enduring aligned domains, a process quantified by a -Gaussian distribution and a decreasing Tsallis entropy ratio.
This paper demonstrates that in pulsating active matter, motility emerges in topological defects without macroscopic flows or self-propulsion due to a ratchet effect caused by mechanochemical coupling that breaks spatial and time-reversal symmetries, thereby regulating a crossover between slow spiral and fast fiber-like wave patterns analogous to cardiac rhythm disorders.
This paper investigates how localized slow bonds modify the large deviation functions of particle current in the symmetric simple exclusion process across three distinct geometries, providing exact analytical expressions validated by rare-event simulations and offering an elementary derivation for the semi-infinite case.
Coarse-grained molecular dynamics simulations reveal that elastomeric polymer networks rupture at stresses far below covalent bond strength because deformation concentrates on a "minimum shortest path" of bonds, leading to the sequential failure of a small fraction of these critical bonds rather than the simultaneous breaking of the entire network.
This paper establishes a unified, covariant, and information-theoretic framework for stochastic dynamics by maximizing Shannon entropy over a joint distribution of actions and endpoints, thereby deriving a Boltzmann-like action-space distribution that reproduces standard Brownian motion, extends naturally to relativistic regimes, and bridges the principle of least action with statistical inference without relying on functional path integration.
This paper demonstrates that in a 2D quantum Ising model, specific initial-state entanglement—not merely entanglement entropy—suppresses false-vacuum decay to stabilize system-size macroscopic clusters, thereby offering a passive mechanism for preserving global information in quantum many-body systems.
This paper introduces an open coupled-top Dicke model realized by a Bose-Josephson junction in a lossy cavity to demonstrate how photon loss drives spontaneous synchronization and reveals two distinct types of dissipative quantum scarring, including one protected and another linked to chaos-assisted macroscopic quantum tunneling.
This paper presents exact solutions for generalized gauge-invariant -chain Ising models () with arbitrary multi-spin interactions by deriving explicit partition functions and correlation formulas via transfer-matrix methods, thereby enabling the identification of confinement and deconfinement regimes through Wilson loop analysis.
This paper introduces the Deep Microcanonical Graph Generator (DMGG), a reinforcement learning framework that efficiently generates microcanonical graph ensembles with exact assortativity constraints through degree-preserving rewirings, thereby overcoming the limitations of traditional exponential random graph models and enabling the precise isolation of structural effects on network function.