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 proposes a new, uniquely defined self-similar approximation method for summing divergent virial expansions to finite densities, which avoids the ambiguities and spurious poles of Padé approximants while achieving accuracy comparable to Monte Carlo simulations without requiring fitting parameters.
This paper applies entropy production estimation tools to the macroscopic organism *A. subaru*, revealing that while entropy production provides a lower bound for energy consumption, it falls short by approximately 25 orders of magnitude, a finding supported by a survey of irreversibility methods and a novel kNN estimator.
This paper demonstrates that negative differential thermal conductivity can emerge in a linear harmonic chain solely due to the specific nature of an overdamped particle reservoir and its coupling, where the heat current vanishes at large temperature differences because the effective dissipation scales inversely with the square of the bath's temperature, leading to asymptotic decoupling.
This paper introduces Boltzmann-machine-prior Variational Autoencoders (BM-VAEs) trained and sampled using a multi-mode quantum annealing framework on a D-Wave Advantage2 processor, demonstrating superior convergence and generative capabilities—including both unconditional and conditional generation—compared to traditional Gaussian-prior VAEs.
This paper challenges the conventional understanding of the Vicsek model by demonstrating that it lacks the widely assumed rotational symmetry, a finding that leads to the disappearance of its reported phase transition when the global phase is adaptively chosen.
This study demonstrates that finite-temperature phase transitions in random language models arise from the intrinsic nature of context-sensitive grammars with short-range interactions, rather than being solely dependent on long-range interactions.
This paper demonstrates that spontaneous symmetry breaking can persist up to arbitrarily high temperatures in four-dimensional ultraviolet-complete quantum field theories by identifying explicit finite- benchmark models where a negative thermal mass prevents symmetry restoration despite asymptotic freedom.
This paper presents an efficient algorithm that learns to generate arbitrary mixed states in the trivial phase from measurement data alone by outputting a shallow local channel circuit, thereby establishing a structural foundation for quantum generative models and inspiring efficient classical diffusion models.
This paper mathematically analyzes an inhomogeneous Erdős-Rényi random graph model driven by infinite-mean Pareto-distributed vertex fitness variables, characterizing the asymptotic behavior of typical degrees, correlations, and local clustering while identifying a phase transition for the existence of isolated vertices.
This paper employs a time-dependent Newns-Anderson-Schmickler model with Keldysh Green's functions and semiclassical trajectories to demonstrate how adsorbate motion and solvent coupling non-adiabatically suppress electron transfer while facilitating energy dissipation into electron-hole pairs, ultimately deriving an analytical expression for the average energy transfer rate in the slow-motion limit.