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 investigates caging phenomena in Sokoban-type models where a random walker can push obstacles, revealing that survival probabilities exhibit distinct intermediate-time exponential decay and long-time stretched-exponential relaxation (with exponents of 1/3 in one dimension and 1/2 in two dimensions) consistent with classical trapping theories, alongside a nonmonotonic dependence of mean trap size on obstacle density.
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 presents a three-slab model for the tensorial dielectric permittivity of lipid bilayers, derived from molecular dynamics simulations, which overcomes limitations in microscopic theories by averaging electric field gradients to accurately capture the membrane's dipole potential and anisotropic response to applied electric fields.
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 introduces a differentiable Maximum Likelihood Estimation (dMLE) framework that enables exact, efficient, and gradient-based optimization of circuit-level noise parameters for quantum error correction, achieving near-exact error probability recovery and significantly reducing logical error rates on both repetition and surface codes compared to state-of-the-art methods.
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.
This paper proposes a Bayesian framework that integrates neural-network priors based on node covariates into the inference of spreading processes on graphs, deriving a hybrid BP-AMP algorithm to demonstrate how combining structural dynamics with covariate information can enhance state recovery while revealing regimes of first-order phase transitions that create statistical-to-computational gaps.