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 functional renormalization group can accurately compute the universal probability distribution functions and large deviation rate functions of the order parameter for the three-dimensional Ising model at criticality, showing excellent agreement with numerical simulations.
This paper demonstrates that a density-difference-dependent Hamiltonian, characterized by an emergent chiral symmetry, hosts two distinct classes of quantum many-body scars—a charge density wave ordered scar and an edge-mode scar—that exhibit robust thermalization breaking dynamics.
This paper demonstrates that free and interacting many-body systems can be isospectral yet exhibit fundamentally distinct phase structures and operator complexity dynamics, linked by a nonlocal unitary transformation that maps local operators to extended many-body strings.
This paper establishes a rigorous lower bound on the entropy production per oscillation period for stochastic systems that refines the Oberreiter-Barato-Seifert conjecture by incorporating a mode-uniformity factor to account for localized eigenmodes, while also providing methods to estimate this factor from data and demonstrating that translation-invariant ring systems saturate the bound.
This paper establishes conditions under which translation-invariant Gaussian quantum cellular automata drive locally normal many-body bosonic lattice states with bounded particle density toward thermalization at infinite temperature, utilizing a novel many-body generalization of the Riemann-Lebesgue lemma to bound local Weyl operator expectations.
This paper demonstrates that the assumption of statistically independent thermal fluctuations in fluctuational electrodynamics breaks down at subnanometric separations due to the hybridization of surface fields, leading to significant cross-correlations that substantially modify radiative heat transfer predictions for polar materials.
This paper proposes that the Dual Model of Liquids bridges macroscopic and mesoscopic behaviors by demonstrating how the interaction between solid-like molecular aggregates and lattice excitations establishes a privileged time arrow in dissipative processes, despite the underlying interaction remaining temporally reversible.
This paper rigorously establishes the thermodynamic consistency of the dissipative Diósi-Penrose collapse model in the strongly non-Gaussian regime by employing a novel exact pseudo-spectral simulation approach to demonstrate that the system settles into a non-equilibrium steady state with asymptotic non-Gaussianity scaling as the cube of the dissipation parameter, thereby resolving the unphysical heating issue while confirming the necessity of exact numerical methods for capturing critical distribution tails.
This study reveals that early psychosis is characterized not by a loss of critical-like brain dynamics, but by a systematic reorganization of scaling exponents within a preserved scale-invariant regime, as demonstrated by combining phenomenological renormalization group, power spectral density, and detrended fluctuation analysis on resting-state fMRI data.
This paper identifies and validates an optimal Langevin Dynamics integration scheme that significantly improves the accuracy and computational efficiency of molecular configurational sampling, particularly for biomolecules, by minimizing stepsize-dependent bias without compromising conformational exploration rates.