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 demonstrates that non-Markovian dynamics significantly enhance the stability of boundary time crystals across a broad parameter range and can induce higher-order limit cycles, offering a promising pathway for realizing robust time crystals in dissipative quantum systems.
This paper extends the functional renormalization group framework to compute momentum-dependent critical observables at fixed magnetization for the Ising universality class, demonstrating that the second-order derivative expansion accurately reproduces universal rate functions and correlation functions in three dimensions and qualitatively agrees with simulations in two dimensions, where lower-order approximations fail.
This paper proposes a geometric decomposition of information flow in autonomous bipartite Markov systems into housekeeping and excess components, distinguishing between cyclic currents that maintain correlations and conservative forces that alter mutual information, thereby generalizing key results in information thermodynamics.
This article demonstrates that the learnability and scalability of quantum reservoir computing can be continuously optimized by adjusting the proportion of non-Clifford gates, thereby establishing a direct link between reservoir performance, entanglement statistics, and non-stabilizer resources to navigate the boundary between classically simulable and computationally complex quantum dynamics.
This article proposes that the Euclidean path integral on non-time-orientable elliptic de Sitter spacetime defines a no-boundary density matrix rather than a wave function, as demonstrated by the explicit calculation of entanglement entropies for free Dirac fermions, revealing a unique property in which the global Hilbert space is one-dimensional while the Hilbert spaces of individual observers remain nontrivial.
This paper establishes that strong-to-weak spontaneous symmetry breaking (SWSSB) in continuous symmetries implies extensive charge fluctuations under specific conditions, introduces a twist overlap correlator to unify the distinction between nonlinear SWSSB order, local charge indefiniteness, and coherent charge fluctuations, and demonstrates that these phenomena are not always equivalent.
This paper demonstrates that statistically mixing activation functions (e.g., Tanh and Swish) creates a controllable, smooth phase transition to criticality at a specific mixing fraction, resolving the historical trade-off between scale-invariant signal propagation and differentiability while enhancing generalization and training performance.
This paper introduces a graph-theoretic framework that systematically identifies two distinct mechanisms for constructing quantum many-body scars in frustrated Rydberg atom arrays on arbitrary lattices, demonstrating their existence on hexagonal lattices and establishing scarring as a generic feature for encoding protected information beyond bipartite systems.
This paper provides a comprehensive, multi-method analysis (perturbative, semiclassical large-, and integrability) of the Gross-Neveu model, demonstrating that at large chemical potential, the system enters a consistent crystalline phase characterized by the condensation of bound states and the emergence of two new dynamically generated scales that govern nonperturbative effects and the oscillatory chiral condensate.
This paper proposes an Expectation-Maximization (EM) algorithm for estimating kappa distribution parameters by treating inverse temperature as a latent gamma-distributed variable within a superstatistical framework, thereby overcoming the lack of exponential family structure to enable analytically tractable maximum likelihood inference.