2407 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 introduces TT-Metadynamics, a scalable method that compresses the bias potential in metadynamics into a low-rank tensor train representation using a sketching algorithm, thereby enabling efficient free energy exploration in high-dimensional systems with up to 14 collective variables without the exponential computational cost of standard approaches.
This paper introduces a weighted graph model (FIU) where information-driven topology evolution governed by spectral curvature exhibits a sharp phase transition at a critical coupling strength, revealing a stable discrete Poisson relation between curvature and information flux that spontaneously demonstrates dimensional sensitivity through distinct system-size collapse thresholds in 2D versus 3D lattices.
This paper resolves the long-standing questions of uniqueness and weak Gibbsianity for gauge-invariant quasi-free fermionic states by proving that, under specific conditions on their momentum-space two-point functions, these states uniquely maximize entropy among translation-invariant states and are indeed weak Gibbs states, with both properties derived directly from thermodynamic formalism.
This paper assesses the application of boundary functionals of random risk processes to nuclear safety, proposing that these mathematical tools can accurately model neutron clustering in advanced reactors and accident scenarios by replacing normal distributions with stable limiting distributions to bridge abstract percolation theory with engineering protection settings.
This paper demonstrates that nearly critical topological quantum codes, such as the transverse-field toric code, retain a finite intrinsic error threshold against local Pauli decoherence because the resulting inter-replica defects are perturbatively irrelevant to the bulk critical point, preventing the destruction of encoded information despite strong quantum fluctuations.
This paper demonstrates through numerical simulations and analytical treatment that the long-range Persistent Voter Model in one and two dimensions belongs to the same universality class as the long-range Ising model, showing that opinion inertia mitigates interfacial noise to restore Ising-like coarsening kinetics regardless of the interaction range exponent .
This paper investigates how sign-indefinite helicity conservation shapes weak turbulence in rotating and odd-viscous flows by demonstrating that helical decomposition reorganizes energy cascades into sign-definite branch-specific transfers, leading to systematic backscatter and unique scale-invariant spectra validated by numerical analysis.
This paper demonstrates that naively applying stochastic and dissipative terms to nonholonomic systems like the Chaplygin sleigh violates the second law of thermodynamics, but this paradox is resolved by modeling the constraint as a viscous limit that necessitates accompanying stochastic forces, thereby restoring thermodynamic consistency and establishing fundamental limits on the physical realizability of idealized nonholonomic constraints.
This paper investigates the first-return time of random walkers undergoing fractional diffusion with Mittag-Leffler distributed waiting times, demonstrating that for symmetric jump distributions, the return time density depends solely on the waiting-time distribution and providing exact results for both Markovian and non-Markovian settings under different temporal orderings of jumps and waits.
This paper introduces a scalable framework combining any-order autoregressive models with marginalization models to efficiently generate atomic configurations for lattice thermodynamics, enabling accurate free energy predictions and phase transition analysis in larger systems at reduced computational cost compared to traditional sampling methods.