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 introduces a multiscale generative sampler that combines conditional Gaussian mixture models and masked continuous normalizing flows to overcome critical slowing down in lattice field theories, achieving significantly reduced autocorrelation times and enabling unbiased Multilevel Monte Carlo variance reduction for the two-dimensional scalar theory near criticality.
This paper develops an exact finite-time multispectral theory for a Brownian particle in a harmonic trap that characterizes the joint distribution of spectral estimators and their inter-frequency correlations, enabling more accurate parameter inference from single trajectories than traditional asymptotic methods.
Using molecular dynamics simulations, this study investigates heat conduction in momentum-conserving fluids across quasi-2D to 3D systems, identifying three distinct transport regimes and revealing a dimensional crossover from anomalous logarithmic divergence in 2D-like systems to normal Fourier behavior in 3D.
By combining deep potential molecular dynamics with high-fidelity electronic-structure data, this study resolves decades of uncertainty to precisely locate aluminum's liquid-vapor critical point at approximately 6531–6576 K, 0.637 g/cm³, and 1.6 kbar, establishing a transferable framework for predicting critical phenomena in metals under extreme conditions.
This paper derives a novel information-theoretic bound on thermodynamic efficiency that surpasses the traditional Carnot limit by accounting for statistical correlations between an engine's internal state and its Hamiltonian, a bound that is achievable in finite-time cycles by quantum dot engines and applicable to both classical and quantum systems.
This paper introduces a large deviation framework utilizing the Landau distribution to accurately forecast extreme precipitation return times, revealing that future birth cohorts will face a sharply increased lifetime exposure to such events under most emission scenarios.
This paper extends the statistical-mechanics framework for analyzing dynamical regimes in diffusion models to discrete data by proposing an effective Ising model that identifies speciation and collapse transitions through second-order phase transition and Random Energy Model analyses, respectively, with theoretical predictions validated by numerical simulations and real-world experiments.
This paper introduces a class of quantum many-body systems featuring an -invariant scar subspace stabilized by algebraic closure, which enables multifrequency nonthermal dynamics and robustness against perturbations without requiring exact solvability or equally spaced spectra.
This paper proposes a Floquet-engineering framework that creates emergent hierarchical symmetries to suppress Hilbert space violations and extend the lifetime of quantum simulations for lattice gauge theories, even when local constraints are imperfectly implemented.
This paper derives exact analytical expressions for pore-surface and surface-surface correlation functions within the general class of dead-leave models, demonstrating their validity for arbitrary grain shapes and dimensions while highlighting distinct structural differences compared to numerically reconstructed Debye random media.