2392 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 reports the ground-state entropy of the 2D Ising model on the Shastry-Sutherland lattice and investigates a generalized version of the model where the constraint on zero-temperature configurations is continuously removed.
This paper investigates a persistent random walk with time-dependent velocity reversal probabilities, identifying a critical transition at for power-law decay that separates super-diffusive and ballistic regimes, a phenomenon shown to be robust across various probability forms and arbitrary spatial dimensions under isotropy.
This paper presents a combined theoretical and experimental study using a dynamical high-temperature expansion method and an optical lattice quantum simulator to quantify spin diffusion in the two-dimensional square lattice XY model, achieving excellent agreement that validates quantum simulation platforms beyond one dimension.
This paper introduces a non-equilibrium Caldeira-Leggett model where a quantum particle interacts with squeezed and displaced thermal reservoirs, demonstrating how these engineered environments act as work sources that break the fluctuation-dissipation relation while satisfying the second law, and establishes a quantum-classical correspondence for heat statistics using a modified Keldysh contour approach to prove a fluctuation theorem for energy balance.
This paper introduces a multidimensional generalized elephant random walk model that replaces the standard linear memory dependence with a generic analytic map, utilizing stochastic approximation theory to derive its asymptotic behavior and establish new results on the phase transition between diffusive and non-diffusive regimes.
This paper demonstrates a highly frequency-selective quantum sensor for AC magnetic fields in the 0.5–50 kHz range by exploiting the resonant response of prethermal discrete time crystals formed in dipolar-coupled 13C nuclear spins in diamond, which achieves a lifetime extension of up to three orders of magnitude and offers robustness against drive errors and platform-specific inhomogeneities.
This paper introduces a linear-time algorithm for sampling tightly confined random equilateral closed polygons in three-space by leveraging symplectic geometry to map the problem to a combinatorial moment polytope, enabling the derivation of explicit vertex distance formulas and a precise conjecture for the asymptotics of total curvature.
This paper presents a universal improvement to the Robertson-Schrödinger uncertainty relation by introducing a new, experimentally accessible noncommutativity-induced term that tightens the bound for mixed states and becomes an exact equality for all states and observables in two-level quantum systems.
This paper proposes an experimental scheme using a periodically driven Bose-Hubbard system with cavity-mediated interactions to induce global kinetic constraints, enabling the direct implementation of long-range multi-body interactions and efficient realization of global quantum gates like the -qubit Toffoli gate without decomposing them into two-body operations.
This paper demonstrates that autoregressive neural networks can efficiently estimate reduced density matrices and calculate the continuum limit of bipartite entanglement entropies for quantum spin chains by leveraging their correspondence with classical two-dimensional systems, requiring only a single training session for a fixed discretization and volume.