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 completeness of the logarithmic characteristic function (LCF) is crucial for distinguishing symmetry-restoration dynamics in many-body systems, as it overcomes the limitations of entanglement asymmetry by remaining extensive and sensitive to initial states, thereby enabling the detection of phenomena like the Mpemba effect that other measures miss.
This paper demonstrates that hidden kinetic correlations, which remain invisible to standard thermodynamic diagnostics like mean dissipation and stationary states, fundamentally control the collective phases of entropy-conditioned histories by converting hidden driven cycles into effective interactions that determine whether systems exhibit segregation, fluctuating states, or critical susceptibility.
This paper demonstrates that chiral mass-transport processes on a square lattice, characterized by odd diffusion, generically induce scale-invariant power-law density correlations () in nonequilibrium steady states, revealing a distinct mechanism for long-range order in isotropic driven systems where fluctuations exhibit nonmonotonic dependence and cusp singularities with increasing chirality.
This paper establishes a theoretical framework linking the trajectory statistics of active bath particles to the mechanical power transfer and drag forces experienced by a moving probe, demonstrating how this relationship predicts phenomena like drag reversal and spontaneous motion while identifying specific active particle models that either permit or exclude positive power transfer.
The paper introduces SpiKIsing, a latent-variable framework that combines a discrete-time kinetic Ising model for collective network dynamics with continuous-time point processes for spike generation, enabling robust inference of sparse, Dale-consistent neural connectivity from spike trains while explicitly accounting for refractoriness and history effects.
This paper investigates quantum Brownian motion in a temperature gradient using a generalized system-plus-reservoir model, deriving the quantum influence functional and an effective Langevin equation to demonstrate the existence of quantum thermophoresis in both low- and high-temperature limits.
This paper establishes that the optimal linear-rate conversion of unknown mixed qubit states (both concentration and dilution) is determined by the eigenvalues of the complex right-logarithmic-derivative Fisher information matrix and can be achieved using only SWAP tests and ancillary maximally mixed qubits, thereby revealing that the matrix's antisymmetric imaginary part encodes essential geometric information beyond statistical distance.
This paper investigates the relaxation dynamics of approximately conserved charges in interacting quantum systems near local equilibrium by demonstrating that Zubarev's non-equilibrium statistical operator approach and a simpler local-equilibrium construction both yield a leading relaxation rate determined by equilibrium correlation functions, which can be extended to diffusion-relaxation equations and validated against kinetic descriptions in models like electroweak B+L washout.
This paper introduces Transformer-Augmented Parallel Tempering (TAPT), a framework that integrates a Transformer-based generator (IsingFormer) to provide global proposal moves, significantly accelerating mixing and reducing time-to-solution for sampling and optimization tasks like 3D spin-glass instances and integer factorization compared to standard Parallel Tempering.
This study demonstrates that while annealed disorder eliminates the odd-even dimensional dichotomy in fundamental Kuramoto models by enforcing continuous transitions, the inclusion of higher-harmonic interactions restores a discontinuous transition in dimensions greater than two, thereby revealing a unique special role for two dimensions in a novel correlation-driven phase transition.