1593 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 proves that general non-Haar random circuits form unitary designs at rates comparable to Haar random circuits, with required depths upper-bounded by a constant factor independent of system size across diverse architectures, thereby enabling more flexible and robust randomness generation for quantum applications.
This study demonstrates that extended two-dimensional hyperuniform patterns comprising tens of thousands of components can be nucleated using the low-temperature vortex structure in pristine Bi2Sr2CaCu2O8 samples as a template, offering a pathway to synthesize next-generation technological devices.
This paper presents a rigorous first-principle derivation of non-additive Tsallis entropy using Chaitin-Kolmogorov algorithmic information theory, demonstrating that non-local grammatical constraints induce power-law information costs that explain reduced heat dissipation in long-range correlated systems and offer a continuous measure of complexity via the parameter .
This paper investigates the statistical properties of the area and absolute area under subdiffusive random walk trajectories across various theoretical frameworks, deriving key moments, scaling laws, and ergodicity breaking parameters that are validated by Monte Carlo simulations.
This paper presents an exact method to derive the large deviation function for particle number fluctuations in product-kernel aggregation, revealing a phase diagram with a tricritical point that separates continuous and discontinuous transitions.
This study employs Q-learning in a Prisoner's Dilemma framework to demonstrate that varying information perception structures, particularly asymmetric information, critically shape the complex evolutionary dynamics and emergence of cooperation, offering new insights into human cooperative behavior.
This paper constructs a generic field theory to demonstrate that driven granular liquids can exhibit broken Kolmogorov scaling and form a new universality class due to unique symmetry properties, despite preserving most symmetries found in Newtonian flows.
This paper proposes a rigorous entropic-force theory demonstrating that stochasticity and discrete-time updates in neural network training generate emergent forces that break continuous symmetries to explain universal representation alignment, the Platonic Representation Hypothesis, and the reconciliation of sharpness- and flatness-seeking optimization behaviors.
This paper introduces a likelihood-based method to analyze discretely sampled trajectories of passive beads driven by active ameboid cells, revealing that the system's motility heterogeneity is non-stationary and time-dependent.
This paper derives the asymptotic expansion of the partition function for a Coulomb gas system on compact Riemann surfaces of any genus by employing a bosonization formula to relate analytic torsion and geometric quantities, thereby proving the geometric version of the Zabrodin-Wiegmann conjecture in the determinantal case.