2439 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 demonstrates that the Family-Vicsek scaling framework, traditionally used for classical surface growth, universally describes the infinite-temperature dynamics of one-dimensional quantum spin chains, revealing distinct ballistic, superdiffusive, and diffusive transport regimes characterized by specific dynamical exponents that are determined by the system's integrability and symmetry properties.
This paper generalizes exponential random graph models by introducing pairwise link interactions to derive a closed-form renormalization group transformation for low-coordination networks, demonstrating the formal equivalence of induced disorder to time-reversed drift-diffusion and establishing the long-wavelength irrelevance of certain conditioning effects for applications in social, neural, and inference problems.
This paper introduces Tensor Network Dynamical Message Passing (TNDMP), a novel framework grounded in "Susceptible-Induced Factorization" that resolves the trade-off between computational efficiency and predictive accuracy in epidemic modeling by offering both exact and scalable algorithms that outperform existing heuristics while mathematically unifying them as low-order limits.
This paper resolves the classical Gibbs paradox without invoking the quantum correction by applying the equal probability principle within an informational framework that interprets Gibbs entropy as Shannon entropy, thereby clarifying the link between information and extractable work in gas mixing processes.
This paper investigates a sixth-order convective-viscous Cahn-Hilliard equation derived from a modified thermodynamic potential, deriving exact static and traveling wave solutions and analyzing their dependence on system parameters.
This study presents an algorithm for the automatic structural search of tensor network states, including entanglement renormalization, which optimizes local structures based on variational energy to improve accuracy in representing non-uniform entangled states, particularly when initialized with existing design methods like the strong disordered renormalization group.
This paper extends the Landauer-Büttiker formula to open quantum systems with gain or loss using the Lindblad-Keldysh formalism, revealing novel transport phenomena such as current generation from symmetry breaking or disorder, and the restoration of the Wiedemann-Franz law beyond the energy band.
This paper elucidates the detailed structure of heat dissipation in linear time-delayed Langevin systems by decomposing it into a frequency spectrum via the Harada-Sasa equality, revealing characteristic oscillatory behaviors that reflect the system's nonequilibrium nature and offering a viable experimental approach for analyzing dissipation through the violation of the fluctuation-response relation.
Using a 78-qubit superconducting processor, researchers experimentally demonstrated long-lived prethermal phases in many-body systems driven by structured random multipolar protocols, revealing a doubly tunable heating suppression mechanism and observing non-equilibrium dynamics that exceed the capabilities of classical tensor-network simulations.
This paper establishes a unifying framework for nonlinear voter models by demonstrating that while symmetric absorbing states lead to Generalized Voter transitions, the introduction of noise eliminates these states to create a phase diagram featuring continuous Ising transitions, discontinuous Modified Generalized Voter transitions, and a tricritical point, all of which exhibit universal scaling behavior.