1593 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 study investigates the antiferromagnetic Ising model on a kagome lattice with ferromagnetic next-nearest-neighbor interactions, identifying two Berezinskii-Kosterlitz-Thouless transitions and a three-phase diagram that exhibits six-state clock universality.
This paper develops a variational method to construct a second-quantized Hamiltonian for interacting surface systems with higher-form symmetries, deriving a functional Schrödinger equation that describes both gapless -form fields and massive topological phases with anyonic excitations.
This paper investigates the stationary density profiles and phase transitions of a Totally Asymmetric Simple Exclusion Process (TASEP) integrated with Langmuir kinetics and connected to particle reservoirs at both ends, revealing that this finite-resource model produces phase diagrams significantly different from—and in some ways more complex than—the standard open TASEP with Langmuir kinetics.
This paper presents a Reinforcement Learning framework for adaptive homing in continuous 2D environments, demonstrating that agents can optimize navigation through a balance of goal-directed correction and stochastic reorientation, with performance improving through both optimal noise levels and collective multi-agent interactions.
This paper demonstrates that uphill transport—where particle flow moves against the concentration gradient—emerges naturally from multispecies exclusion processes and provides a theoretical bridge between microscopic particle models and continuum descriptions like the Poisson-Nernst-Planck model, highlighting its potential significance in nanoscale and membrane-based technologies.
This study demonstrates that negative energetic elasticity is a fundamental and universal property of polymer chains, arising from effective soft-repulsive interactions and governed by a common scaling exponent across random, self-avoiding, and neighbor-avoiding walk crossovers.
This paper demonstrates that the Family-Vicsek scaling framework, traditionally used for classical surface growth, universally describes the infinite-temperature dynamics of one-dimensional $SU(N)$ 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.