2392 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 demonstrates that the discrete time crystal phase transition in a periodically modulated Lipkin-Meshkov-Glick model, characterized by long-range interactions, can be harnessed for quantum-enhanced high-precision sensing of field strength through diverging quantum Fisher information near criticality.
This paper investigates the large- limit of the model on graphs, demonstrating that the system's free energy is governed by the spectrum of the Laplacian matrix at low temperatures and the Adjacency matrix at high temperatures, with exact solutions derived for trees and decorated lattices to highlight the critical role of coordination number and the loss of translational invariance.
Using advanced off-lattice simulations of extremely large self-avoiding polygons, this study determines precise knot types to confirm that the number of prime knot summands follows a Poisson distribution, estimate the characteristic knotting length at approximately 656,500, and validate both knot localization and the knot entropy conjecture.
This paper demonstrates that the Kibble-Zurek mechanism's universal defect scaling does not strictly depend on crossing a quantum critical point, as defect suppression can occur at criticality while conventional scaling persists in non-critical regimes within quasi-one-dimensional Fermi systems.
This paper extends a self-referential statistical operator framework by demonstrating the structural stability of the derived Tsallis index, establishing a rigorous H-theorem for both discrete iterations and continuous gradient flow within the local kernel approximation, and characterizing the non-perturbative emergence of a re-entrant disordered phase driven by the self-coupling parameter.
This paper demonstrates that a single-particle dark state in a spin chain with correlated dissipation fundamentally alters long-time many-body dynamics, inducing a universal scaling behavior characterized by a decay of the zero-momentum mode and a decay of total density.
This paper derives the linear relaxation and fluctuation dynamics of weakly deformed interfaces separating stable phases using fluctuating hydrodynamics and the dynamical-action formalism, extending results from equilibrium to non-equilibrium systems like active model A while cautioning against the uncontrolled application of popular equilibrium ansätze to active field theories.
This paper generalizes the Euler-Maruyama method to simulate non-Wiener processes driven by non-Gaussian Lévy noise, demonstrating its physical superiority over geometric Brownian motion in specific examples and validating the additive noise results through a derived master equation.
This paper introduces a general framework of branching under first-passage resetting to demonstrate how endogenous stochastic threshold-crossing events drive population growth, revealing that timing fluctuations generally enhance growth rates while exposing a fundamental trade-off between offspring yield and replication delay that optimally explains bacteriophage lysis strategies.
This topical review provides a unified perspective on the computationally intractable but optimal Maximum Likelihood Decoding (MLD) of quantum error correction codes by surveying recent advances through the complementary lenses of statistical mechanics, tensor networks, and artificial intelligence, while discussing their connections, applications, and future challenges.