2356 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 derives exact scaling laws for the ageing behavior of the voter model on a fractal substrate, demonstrating that while the dynamic and autocorrelation exponents depend on both the geometric and spectral dimensions, the explicit dynamic scaling functions are determined solely by the spectral dimension.
This paper analytically investigates two-dimensional heterogeneous diffusion with a radially decaying coefficient under stochastic resetting, deriving exact probability densities, stationary states, and first-passage properties for various noise interpretations and memory effects, while demonstrating how these factors influence mean squared displacement saturation and the existence of an optimal resetting rate.
This paper investigates the emergence of non-equilibrium steady states in stochastic processes under threshold resetting, deriving a general condition linking the steady-state distribution to the ratio of mean local time and mean first-passage time, and demonstrating through a one-dimensional drift-diffusion model that this protocol induces rich intermediate-time dynamics such as anomalous relaxation and damped oscillations.
This dissertation introduces an effective mass approach to bridge Markovian and non-Markovian regimes, demonstrating that short-time memory effects can significantly alter microscopic dynamics by reversing particle transport and enabling memory-based information generation.
This paper determines the optimal radius for probing current fluctuations in a driven Brownian gyrator under a quadrupolar shear by maximizing the ratio of second-order response to Onsager-Machlup cost, revealing that the optimal scale is governed by the system's faster timescale—either the thermal trap radius or the rotational diffusion length—depending on the driving strength.
This paper investigates how slow out-of-equilibrium modes reshape the power spectrum of dynamical density fluctuations near the critical point, revealing that these modes introduce an extensivity constraint on velocity coupling and significantly reduce fluctuation decay rates, thereby manifesting the characteristic slowing down of the system.
This paper proposes a theoretical method for measuring correlation functions, including non-Gaussian ones, in quantum field systems by analyzing how the system's average field evolution depends on its initial state following local or nonlocal quantum quenches.
This paper demonstrates how to engineer a quantum Mpemba effect with parametrically separated cooling timescales by utilizing Stark localization and energy-dependent incoherent hopping, a phenomenon that is further enhanced by collective bound-pair dynamics and is accessible in current experimental setups.
This paper demonstrates how a quantum non-Markovian Hatano-Nelson model with frequency-dependent nonreciprocal hopping and dissipation arises microscopically in quasi-one-dimensional dissipative lattices using non-equilibrium Green's functions, revealing unique non-Markovian phenomena like unidirectional frequency blocking and specific dissipative phase transitions that cannot be captured by Markovian or reciprocal theories.
This paper introduces a novel Boltzmann generator framework tailored for amorphous particle systems by embedding physical symmetries and periodic boundary conditions into Riemannian stochastic interpolants, which significantly improves sampling accuracy but ultimately reveals that accumulated numerical errors in continuous-flow formulations compromise the exact thermodynamic reweighting required for equilibrium sampling.