1537 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 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.
This paper introduces a statistical method based on the statistical Jacobi approximation to derive a flow equation for resonance density, successfully explaining the finite-size drifts toward delocalization in many-body localization models by characterizing how resonance proliferation drives the transition to thermalization.
This paper demonstrates that chiral order in two-dimensional nonreciprocal flocking mixtures is generically unstable and collapses into spatiotemporal defect chaos due to the proliferation of topological defects driven by density-order coupling, resulting in scale-free fluctuations with nonuniversal exponents below a finite correlation length that diverges as nonreciprocity vanishes.
This paper introduces a renormalization-group inspired, lattice-based framework for piecewise generalized linear models that offers explicit interpretability and structured parameter sharing, while utilizing replica analysis to derive principled guidelines for lattice design and regularization scaling to maintain generalization performance.
This paper demonstrates that complete knowledge of non-Markovian island-state dynamics in Majorana transport systems is thermodynamically incomplete, as it fails to uniquely determine lead-specific transport statistics like charge and heat noise due to a fundamental loss of information regarding the specific reservoir channels involved in electron exchange.
This paper demonstrates that irreversible reactions in chemical reaction networks and Markov chains generate emergent conservation laws and broken cycles, resolving a recent conundrum regarding non-integer conservation laws by deriving a new law that links conserved quantities, broken cycles, and a "co-production index" to correct existing undercounting methods.
This paper proposes a method to derive genuine large-deviation rate functions for open quantum systems with strong symmetries by applying the Gärtner-Ellis theorem within operator space blocks and minimizing the resulting local rate functions, a scheme justified by dissipative freezing and demonstrated through analytical and three-spin models where nonanalyticities manifest as avoided level crossings.