2356 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 conjectures and numerically verifies that the Mayer series coefficients for dimer gases on various regular bipartite lattices follow a specific asymptotic exponential form, while also exploring connections to renormalization group theory, Ising model susceptibility, and the properties of partition and prime number functions.
This paper presents analytical results on exact tensor network representations and correlation functions for the first examples of 2D ground states exhibiting quantum phase transitions between area law and extensive entanglement entropy, utilizing 3D tessellation-based networks that generalize 1D holographic models to reveal exotic phase characteristics through random surface scaling.
This paper establishes a concrete connection between quantum learning theory and thermodynamics by demonstrating that efficient learning algorithms can acquire the knowledge necessary to erase unknown quantum states at optimal energy cost, thereby linking thermodynamic efficiency to state complexity while revealing fundamental computational limits under cryptographic assumptions.
This paper introduces a generalized Hebbian Kuramoto model incorporating genuine four-body phase interactions to create a dense associative memory, demonstrating that such higher-order coupling induces discontinuous, hysteretic transitions and exponentially long memory retention times compared to classical pairwise models.
This paper applies stochastic resetting theory to analyze first-passage properties of synthesis-degradation processes, revealing that optimal reaction times and search efficiencies can be achieved by tuning synthesis rates, even in the presence of particle degradation.
This paper presents a recursively generated power series for the Wigner-Kirkwood pair commutation function in quantum statistical mechanics, which successfully reproduces the second virial coefficient of helium for temperatures above 65 K and suggests potential applications for a general quantum Monte Carlo algorithm.
This paper demonstrates a functional continuous microfluidic sorting device that harnesses differential motility and curved confinement geometry to achieve autonomous, collective demixing of binary active particle mixtures, as validated by both simulations and experiments with Quincke rollers.
By applying Q-learning to structured populations, this study reveals that cooperation levels follow an inverted U-shaped relationship with information availability, demonstrating that a moderate amount of information—rather than more—is optimal for fostering cooperation by balancing decision-making sufficiency with tractability.
This paper numerically investigates the distribution of selected states arising from a specific stochastic evolution process in Boltzmann's H-theorem, where one state's probability is imposed on all others, to explore its implications for equilibrium fluctuations in both thermal and non-thermal systems.
This paper derives an active form of Jurin's law demonstrating that spontaneous particle currents, rather than surface tension alone, drive capillary rise in active fluids, leading to shape-dependent phenomena that defy equilibrium predictions.