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 argues that the non-Hermitian skin effect originates from spectral instability and non-reciprocity rather than point-gap topology, demonstrating that the commonly assumed link between spectral winding and boundary localization is a non-generic consequence of translational invariance.
Using square-pulsed source thermoreflectance, this study reveals that interfacial thermal conductance at solid-liquid interfaces increases with modulation frequency due to weak coupling between diffusional and phonon-like modes over a finite nonequilibrium length, challenging the assumption of fully equilibrated liquid modes.
Using unbiased quantum Monte Carlo simulations, this study confirms the existence of a symmetric mass generation transition in a bilayer honeycomb lattice model at a critical coupling of , identifies its novel universality class distinct from mean-field theory, and demonstrates that its nonequilibrium dynamics obey generalized finite-time scaling despite the breakdown of standard Kibble-Zurek prerequisites.
This paper critiques the limitations of the standard mean-squared displacement (MSD) criterion for assessing ergodicity in single-particle tracking, demonstrating that it can yield spurious results, and proposes a more robust alternative based on the mean-squared increment (MSI) that accurately characterizes genuine (non-)ergodicity and stationarity in anomalous diffusion systems.
This paper introduces a tensor-product regularized qubit Hamiltonian that interpolates between Rokhsar-Kivelson dimer models and the -flux toric code, revealing a phase diagram with deconfined topological liquids and identifying a deconfined multicritical point where three distinct quantum phase transitions meet.
This paper defines a generalized Golomb-Dickman constant representing the limiting expected proportion of the longest cycle in random permutations under the Ewens measure and derives an explicit integral representation for it using Kingman's Poisson process construction, thereby extending the classical results of Shepp and Lloyd to the Ewens setting.
This paper introduces a fractional generalization of Tsallis entropy using the -Caputo operator to derive a closed series representation involving the -Gamma function, demonstrating that the standard entropy is recovered as the fractional order approaches one and analyzing the parameter domains where the resulting entropy remains non-negative.
This paper derives a family of thermodynamic uncertainty relations valid for any system obeying a fluctuation theorem by utilizing higher-order moments of entropy production, establishing a bound that applies to both classical and quantum regimes, can always be saturated, and is linked to correlations between entropy production and thermodynamic quantities.
This paper extends the study of nonreciprocally coupled models to general symmetries, demonstrating the emergence of nonequilibrium fixed points characterized by fluctuation-dissipation violations, underdamped oscillations, and exceptional-point-induced discrete scale invariance, while also identifying a distinct universality class for extreme nonreciprocity.
This paper presents a generalized linear response theory for mixed jump-diffusion models that derives comprehensive response formulas and decomposes system behavior into Kolmogorov eigenmodes, successfully demonstrating its predictive power in diagnosing ENSO variability and projecting climate change in energy balance models.