1838 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 conventional deep thermalisation framework fails for mixed initial states, necessitating a new paradigm where mixed-state deep thermalisation emerges dynamically in chaotic systems as a maximum-entropy ensemble on an augmented system that explicitly depends on the initial state's entropy.
This paper introduces a geometry-aware deep learning framework that utilizes layer-wise local training to sculpt internal representations for enhanced adversarial robustness, supported by data-dependent statistical mechanics and a phenomenological Hebbian coupling model.
This paper extends the statistical geometry of insertion space from equilibrium hard spheres to repulsive active matter in one and two dimensions, deriving closed-form expressions that reveal how activity increases total insertion volume, enhances connectivity, and encodes signatures of collective phase behaviors.
This paper presents and validates a practical framework for extracting thermodynamic properties of materials, specifically nitrogen-doped graphene, on neutral-atom quantum computers by mapping DFT-derived energetics to a Rydberg-atom Hamiltonian and employing a single-parameter rescaling strategy to overcome hardware energy scale limitations, thereby enabling the sampling of Boltzmann-like distributions at an effective temperature.
This paper extends the classical Turing instability analysis to Matrix-Weighted Networks by introducing a novel framework for coherent systems that utilizes orthonormal transformations to reduce matrix-weighted diffusion to scalar diffusion, thereby enabling the characterization of how network topology and inter-node transformations jointly drive pattern formation.
This paper proposes the normalized Wigner negativity rate as a second-moment diagnostic for operator spreading in AdS, demonstrating that at conformal dimension , this rate precisely tracks the growth of Krylov variance and corresponds to the tidal stretch of infalling geodesics via the product of radial position and momentum.
This paper presents a reformulation of the Feynman path integral for quantum statistical mechanics as a Gaussian sampling method that eliminates numerical cancellation issues and multiple temperature nodes, demonstrating its accuracy through analytic and simulation results for the second virial coefficient of He that align with laboratory measurements.
This paper derives a unified mean-field Fokker-Planck equation from microscopic equations of motion to describe non-equilibrium electrostatics in nonlocal nonlinear systems, yielding a generalized Poisson-Boltzmann equation and revealing that linear approximations produce anisotropic susceptibility in isotropic fluids.
By demonstrating that steady shear suppresses equilibrium stringlike cooperative rearrangements and can be quantitatively described using a shear-dependent effective temperature, this study establishes a unified microscopic theory linking structural relaxation in glass-forming liquids across both equilibrium and nonequilibrium conditions without requiring additional fitting parameters.
This paper analytically solves the Glauber dynamics and equilibrium properties of athermal random field Blume-Capel and Blume-Emery-Griffiths models on a complete graph, revealing that the variance of the random field acts as a temperature-like control parameter determining the coincidence of steady states, the nature of phase transitions, and the complex shapes of hysteresis loops which depend non-trivially on initial conditions and crystal field values.