2422 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 ferromagnetic coupling between two copies of a random hypergraph bicoloring problem lowers the clustering threshold and transforms the phase transition from discontinuous to continuous, thereby significantly impacting the convergence of Belief Propagation and highlighting the need for improved re-weighting strategies to enhance algorithmic performance.
This study demonstrates that membrane permeability to fluid but not solutes introduces osmotic forces that restrict the existence of canonical membrane relaxation modes and the validity of equipartition-based fluctuation predictions to a finite range of wavenumbers, which shrinks and eventually vanishes as surface tension increases.
This paper demonstrates that a constrained Brownian bridge evolves along an m-geodesic on the statistical manifold of Gaussian distributions, thereby establishing a physical realization of information geometry where random processes follow informational straight trajectories and highlighting the fundamental physical role of asymmetric informational distance.
This paper experimentally demonstrates how introducing hysteresis into a feedback-generated virtual potential creates a non-equilibrium steady state with an adjustable effective temperature, allowing a micro-resonator to erase information while consuming over 20% less energy than the Landauer bound by effectively acting as an embedded Maxwell demon.
This paper proposes a dynamical framework for autonomous learning that replaces traditional objective-based optimization with a stress-gated mechanism, where an internally generated stress variable accumulates evidence of dysfunction to trigger state-dependent structural reorganization and self-organized learning episodes without external supervision.
This paper establishes bounds on the Kullback–Leibler divergence to equilibrium for mass-action chemical reaction networks by linking decay rates to stoichiometric singular values and convexity parameters within a generalized gradient flow framework, offering a novel tool to quantify slow relaxation and plateau behaviors in biological systems.
This paper investigates the thermodynamic geometry of relativistic ideal gases across classical and quantum statistics, demonstrating that while the characteristic signs of thermodynamic curvature persist, relativistic effects introduce mass-dependent shifts in curvature singularities and corrections to the Bose-Einstein condensation temperature.
This study demonstrates that the anomalous mole fraction effect and anion leakage in negatively charged wide nanopores are governed by a delicate interplay between charge inversion, anion leakage, and ionic mobility, which can be accurately reproduced by matching the distance of closest approach between ions and surface charges regardless of the specific microscopic model used for surface groups.
This paper proposes a unified formalism defining classical and quantum chaos through the complexity of adiabatic transformations, quantified by fidelity susceptibility, which successfully distinguishes between integrable, chaotic non-thermalizing, and ergodic regimes while predicting the universal onset of chaos in coupled spin models.
This paper introduces a microcanonical inflection point analysis using parametric curves to characterize phase transitions across various models, while demonstrating a direct relationship between the linear arrangement of Fisher's zeros in the complex plane and the order of the transition, specifically linking latent heat to the distance between these zeros.