1593 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 establishes that entanglement serves as the fundamental resource for generating global random quantum states via local operations, demonstrating that the quality of the resulting approximate state designs is tightly bounded by the second Rényi entanglement entropy of the initial state, which thus defines the maximal capacity for randomness generation under resource-free constraints.
This paper generalizes the concept of integrable boundary states to both even and odd lengths of the anisotropic Heisenberg chain, presenting factorized states for XXZ and XYZ models that utilize the KT-relation to explicitly select specific transfer matrix eigenstates via a defined Bethe root selection rule.
This study experimentally verifies the area law of quantum mutual information in gapped one-dimensional quantum field theories using an ultra-cold atom simulator, overcoming the challenges of measuring von Neumann entropy in spatially extended subsystems.
This paper establishes a duality between open many-body systems satisfying detailed balance and closed bilayer systems, revealing that the identity operator and specific eigen-operators of the Lindbladian map to quantum many-body scars in the form of thermofield double states with tunable entanglement and exponential decay dynamics.
Using Generalized Hydrodynamics, this study reveals that charge transport in the quantum sine-Gordon model is predominantly diffusive rather than ballistic, driven by non-diagonal scattering of internal charge degrees of freedom that causes the Onsager matrix to diverge near specific coupling strengths.
Inspired by the one-dimensional Potts model, this paper constructs a context-sensitive random language model that exhibits a Berezinskii--Kosterlitz--Thouless (BKT) phase transition, suggesting that the critical properties observed in natural languages may arise generically from underlying BKT phases rather than requiring fine-tuning or self-organized criticality.
This paper investigates the robustness of Floquet codes under local decoherence by deriving a 3D statistical mechanics model for their maximum likelihood decoder, identifying specific Pauli channels with decoupled thresholds, and proposing a diagnostic that distinguishes the code's anyon automorphism phase from the toric code via a phase transition at the decoherence threshold.
This paper introduces an efficient simulation framework that models Generalized Hydrodynamics as a gas of interacting semiclassical wave packets, enabling fast large-scale studies of quasi-integrable systems with integrability-breaking perturbations while revealing that long-range correlations can persist indefinitely even when local observables appear thermalized.
This perspective paper clarifies the scientific concept of emergence as a measurable, physically grounded phenomenon arising from local interactions constrained by global boundaries, using concrete examples to demonstrate that it offers genuine insight into complex systems rather than mysticism.
This paper establishes a new theory for the local dynamical stability of discrete attractor neural networks with graded activities and noise, revealing that all fixed points remain stable below a critical load determined by neural activity statistics and activation functions, thereby highlighting the computational advantages of threshold-linear activation and sparse patterns.