2377 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 presents an exact solution for a two-temperature Brownian dimer with odd mobility, demonstrating how antisymmetric transport coefficients generate handed correlations and counter-rotating circulating currents that enhance thermal conductance without altering net heat flow or total dissipation.
This paper demonstrates that the macroscopic fluctuation theory for a multispecies symmetric simple exclusion process on an infinite line constitutes an integrable system of Landau–Lifshitz type, allowing the derivation of the current cumulant generating function and conditioned density profiles via inverse scattering methods, ultimately revealing that the generating function depends on a single scalar variable and coincides with the single-species result regardless of the number of particle species.
This paper investigates a non-reciprocal XY model with vision-induced anisotropy to demonstrate how reactive and proactive terms in Langevin dynamics drive both local and global spin pinning along preferred lattice directions, thereby clarifying the mechanisms of orientational selection and resolving previous discrepancies in the field.
This paper establishes a unified framework for diagnosing quantum complexity and chaos in many-qudit doped Clifford circuits by combining analytical Weingarten calculus with large-scale simulations to identify distinct dynamical phase transitions in magic spreading and chaotic behavior that depend on qudit dimension and non-Clifford gate density.
This paper argues that while typicality—the phenomenon where expectation values are indistinguishable for most states—holds for both quantum and classical many-body systems regarding macroscopic observables, it applies to classical systems only for macroscopic and not for microscopic observables.
This paper employs conformal field theory and a replica trick to derive closed-form expressions for the statistics of measurement-induced entanglement in one-dimensional Tomonaga-Luttinger liquids, revealing distinctive critical behavior, fat-tailed bimodal distributions, and an equivalence between microscopic Born averaging and conformal boundary condition averaging at low energies.
By developing Weingarten calculus for the real Clifford group, this paper establishes that doped real Clifford circuits exhibit a distinct orthogonal Porter-Thomas distribution and achieve Haar-like statistics with minimal magic resources, revealing a sharp hierarchy in resource requirements for state design.
This paper analytically and numerically investigates three non-Hermitian random matrix universality classes (A, AI, and AII) by characterizing their complex edge statistics through spacing ratios and nearest-neighbour distributions, revealing varying degrees of level repulsion consistent with a 2D Coulomb gas description and confirming universal cubic repulsion in the small-argument limit.
This paper demonstrates that a minimal frustrated Vicsek–Kuramoto active-particle model with Sakaguchi-type phase lags realizes a non-Hermitian Chern bulk-boundary correspondence, where linear hydrodynamic instabilities select a topologically nontrivial spectrum with Chern numbers that drives robust one-way boundary flows saturated by nonlinear particle dynamics.
This paper introduces "minimal normalizing flows" (MNFs), a computationally efficient, physically informed bijective mapping strategy that significantly accelerates chemical potential and free energy calculations in molecular simulations by overcoming the training complexity and limited sampling improvements of traditional normalizing flows.