1581 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 establishes a quantum metrological framework linking information scrambling to timekeeping by deriving a generalized Cramér-Rao bound that relates time estimation precision to OTOC decay and subsystem quantum Fisher information, while demonstrating that this Fisher information exhibits universal critical amplification near quantum phase transitions.
This paper demonstrates that interspersing scarred quantum dynamics with stochastic resets to simple product states enables the experimental preparation of local properties of highly entangled many-body scar eigenstates, yielding a stationary state that is locally equivalent to a single pure scarred eigenstate in the sparse-resetting limit.
This paper proposes a singular-value-decomposition-free method for constructing initial tensor networks and demonstrates that while Tensor Renormalization Group (TRG) results depend heavily on these initial tensors, this dependence can be eliminated and numerical robustness significantly improved by employing the boundary TRG technique.
This paper introduces a generalized definition of dynamical activity valid at arbitrary system-reservoir coupling to derive a novel Quantum Kinetic Uncertainty Relation (QKUR) that accounts for quantum coherence and corrects the breakdown of standard relations in the strong-coupling regime of mesoscopic conductors.
This paper analytically and numerically demonstrates that free liquid diffusion develops a quasi-steady regime of long-range concentration correlations, characterized by a linear growth in time followed by distinct spatial decay behaviors ( inside and outside the diffusion length), thereby elucidating the dynamic emergence of non-equilibrium "giant concentration fluctuations."
This paper establishes that the fidelity of noisy graph states maps to a classical spin system partition function, revealing that noise robustness and the emergence of fidelity phase transitions are governed by the interplay between graph connectivity and spatial dimensionality, where moderate connectivity induces fragility while extreme connectivity restores robustness.
This study demonstrates that quantum Stirling engines utilizing multilayer graphene, particularly AB-stacked bilayer graphene, can achieve robust performance and Carnot efficiency under low magnetic fields and moderately low temperatures, with distinct operational advantages identified for each stacking configuration.
This paper resolves the paradox of connected low-loss basins versus localized optimization dynamics in overparameterized neural networks by demonstrating that curvature-induced entropic barriers, rather than energetic ones, confine noisy trajectories to specific minima despite the existence of flat connecting paths.
This paper proposes a quantum-coherent thermodynamic framework based on "minimum-variance foliation" of state space, introducing a "leaf typicality" hypothesis that extends eigenstate thermalization to non-equilibrium dynamics by demonstrating that local observables are determined solely by a state's leaf label and energy.
This paper extends the Prometheus unsupervised learning framework to 3D classical and quantum many-body systems, successfully detecting critical points, extracting accurate critical exponents, and identifying distinct universality classes—including exotic infinite-randomness criticality—without analytical guidance.