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 demonstrates that the first-detection return probability of long-range quantum walks undergoes a continuous phase transition at a critical hopping exponent , separating recurrent and transient behaviors, with nonanalytic decay exponents arising from measurement-induced interference between infrared and ultraviolet energy modes.
This paper demonstrates that in a self-propelled particle model with disordered, spin-glass-like alignment couplings, increasing interaction variance can paradoxically promote global polar order by enabling activity-driven local clustering that mitigates frustration, allowing flocks to emerge even when most interactions are antialigning.
This paper develops a random matrix theory framework to establish a distinct "critical load for stability" in attractor neural networks, demonstrating that memory patterns are either universally stable or unstable depending on their density and the network's activation functions, rather than merely on the total number of stored memories.
This paper establishes a -algebraic framework for nonequilibrium steady states in infinite quantum spin systems and provides a sufficient condition, involving a Liouvillian condition number and spectral gaps, to ensure that the steady state of the infinite system coincides with the thermodynamic limit of finite-system steady states, thereby addressing cases where time and thermodynamic limits fail to commute despite uniform spectral gaps.
This paper demonstrates that pure dephasing, rather than static disorder, is sufficient to induce integer quantum Hall criticality in quantum anomalous Hall systems by generating a topological nonlinear sigma model with instanton-driven flow, a prediction confirmed by simulations of the Qi-Wu-Zhang model.
This paper advances a theory of quantum phase transitions driven by Fermi surface topology changes by analyzing the exactly solvable -dimensional Hatsugai-Kohmoto model, where it confirms the Luttinger theorem, establishes gapless phases as Landau Fermi liquids, and demonstrates that a new universality class characterized by the order parameter (Fermi sea volume) and Euler characteristic robustly describes metal-insulator and gapless-to-gapless transitions.
This paper demonstrates that Chapman's non-Fickian diffusion current is an indispensable factor for accurately modeling the thermophoretic motion of colloidal proteins in water, successfully capturing the temperature-dependent variations of the Soret coefficient and showing strong agreement with experimental data for lysozyme, BLGA, and Poly-L-Lysine.
This paper introduces a differentiable Maximum Likelihood Estimation (dMLE) framework that enables efficient, gradient-based optimization of circuit-level noise parameters for quantum error correction, achieving near-exact precision in simulations and significantly reducing logical error rates on Google's experimental hardware compared to state-of-the-art methods.
This paper introduces a dual network approach that identifies two interlaced subnetworks with distinct hydraulic behaviors, enabling a universal analytical prediction of heavy-tailed flow statistics based on network topology and disorder.
This paper derives a continuous kinetic theory and hydrodynamic closure from an agent-based model to bridge the gap between individual jellyfish behaviors and large-scale swarm dynamics, enabling the prediction of massive jellyfish swarm formation and evolution within realistic ocean currents.