2446 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 the relaxation critical dynamics of measurement-induced phase transitions in one-dimensional quantum circuits, revealing distinct entanglement entropy scaling behaviors for different initial states and proposing a unified scaling framework that significantly reduces experimental post-selection overhead.
This paper extends Boltzmann's microcanonical ensemble to nonequilibrium systems by counting equally probable trajectories to derive a "microcanonical caliber" principle, which provides a microscopic foundation for maximum caliber, clarifies the statistical origins of transport phenomena, and offers an independent derivation of stochastic thermodynamics equations for nonequilibrium steady states.
This paper argues that data-driven climate emulators often fail to capture causal forced responses due to inadequate coarse-grained representations and parameterizations, advocating instead for tailored reduced-order stochastic models guided by linear response theory to better resolve multiscale dynamics and enable causal studies.
This paper generalizes the relationship between the clock model and the Temperley-Lieb algebra by introducing a coupled TL algebra with a planar parafermion pictorial representation, which utilizes the string Fourier transform to describe the Hamiltonian, Hilbert space, and related spin chains.
This paper demonstrates that a Sokoban random walker's ability to push obstacles eliminates the classic percolation transition by inducing a dynamical crossover at a critical density, shifting the system from self-trapping to pre-existing trapping mechanisms and placing it within the Balagurov-Vaks-Donsker-Varadhan universality class characterized by stretched-exponential relaxation.
This paper reveals that slow, heterogeneous relaxation in quantum kinetically constrained models arises from a nested hierarchy of frozen states, where relaxation time scales are determined by the spatial separation between active regions and can be systematically characterized through an expansion in the inverse coupling parameter.
This paper presents a Metropolis Monte Carlo algorithm capable of handling complex phase space weights in quantum statistical mechanics and demonstrates its accuracy by successfully calculating the saturation liquid density of He near the -transition using a third-order Wigner-Kirkwood expansion.
This paper investigates the phase transition of the largest connected component in duplication-divergence growing graphs with symmetric coupled divergence, identifying a critical divergence rate and demonstrating how the inclusion or exclusion of non-interacting vertices in duplication events influences the transition's characteristics and its relationship to bond percolation.
This paper defines entropic efficiency as the ratio of information gain to memory erasure cost to demonstrate that while sequential and parallel Bayesian inference paradigms achieve identical minimal costs when all correlations are exploited, the parallel approach outperforms the sequential one when hidden correlations remain unexploited.
This paper proposes a stochastic differential equation framework to model social evolution as continuous-time dynamics, enabling the quantification of irreversibility, detection of exogenous perturbations, and imputation of missing data to overcome the limitations of deterministic approaches in analyzing historical trajectories.