1537 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 introduces color Heisenberg-Lie (super)algebras graded by specific abelian groups to unify commutators and anticommutators via mixed brackets, thereby establishing a framework for both permutation-based and anyonic parastatistics that recovers braided Majorana qubits through nilpotent parafermions and characterizes parabosons via measurable probability densities.
This paper investigates the self-assembly of cross junctions in -dimensional space, revealing an odd-even effect where uniaxial order dominates for odd dimensions and specific even dimensions (), while isotropic states prevail for even dimensions .
Using the functional renormalization group, this paper demonstrates that while supersymmetry alone does not guarantee time-reversal invariance in Model A dynamics, TRI emerges as an effective large-scale symmetry and the system's non-equilibrium flow reproduces the equilibrium effective action, allowing the recovery of the Ising model's magnetization distribution.
This paper extends the wave turbulence framework to the -FPUT lattice with site-dependent coefficients, deriving a new kinetic equation that reveals how spatial modulation creates a resonant manifold for three-wave interactions, leading to faster thermalization and wave-action isotropization via a Bragg-scattering mechanism.
This paper presents an exact analytical solution for heterogeneous chains of overdamped, harmonically coupled particles with momentum-conserving dissipation, revealing that a free end induces a peculiar staircase response where particle interactions are independent of intervening chain properties and that rank-deficient matrices lead to a distinct separation between steady-state and relaxation dynamics.
This paper introduces a physics-inspired continuous relaxation framework that maps discrete binary variables to complex phases, leveraging an implicit regularization mechanism derived from phase dynamics to achieve superior convergence and robustness in solving NP-hard combinatorial optimization problems like QUBO, sparse coding, and planted-solution Ising models.
This paper presents a framework for analyzing rare events in small-noise Doob conditioned processes by reinterpreting the conditioned ensemble as a post-selected original process, thereby deriving an optimal-control variational principle for the generating function without requiring the explicit construction of the Doob drift.
This study combines experiments, simulations, and theory to demonstrate that nonlinear friction enables the transfer of chiral active fluctuations from a non-equilibrium bath to a symmetric passive tracer, resulting in circular trajectories and a systematic transverse drift known as odd transport.
This paper derives exact analytical formulas for the variance and Fano factor of level crossings in stationary Gaussian processes, revealing how temporal correlation structures determine whether crossing events cluster or remain regular, thereby extending beyond the traditional Kac-Rice mean rate to provide deeper insights into higher-order crossing statistics.
This paper introduces a general simulation framework that accelerates the study of extreme first-passage problems by bypassing computationally expensive full trajectory tracking in favor of a recursive algorithm based on asymptotic first-passage distributions to efficiently generate order statistics for both instantaneous and time-dependent particle emission.