2422 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 proposes a novel mass matrix tuning strategy based on seismic acquisition geometry to enhance the efficiency and accuracy of Hamiltonian Monte Carlo sampling for solving the ill-posed acoustic Full-Waveform Inversion problem under noisy and limited data conditions.
This paper proposes a Bayesian sequential approach for time-lapse Full Waveform Inversion using Hamiltonian Monte Carlo to effectively quantify uncertainties in high-dimensional seismic problems by integrating baseline survey data as prior knowledge, demonstrating accuracy comparable to parallel schemes while managing computational costs.
This paper presents a pedagogical study of extreme value statistics in a continuous-time branching process by mapping it to an "agitated random walk," enabling the derivation of exact analytical results for the maximal population distribution across subcritical, critical, and supercritical phases, which are validated by numerical simulations.
This proof-of-concept study proposes that mesoscopic feedback mechanisms naturally generate infinite-range interactions in one-dimensional field theories, leading to phase transitions, spontaneous symmetry breaking, and new universality classes with significant implications for achieving room-temperature ferromagnetism in monolayer spintronics.
This paper demonstrates that a pair of antiparallel asymmetric exclusion processes competing for finite particle reservoirs exhibits an extended region of delocalized domain walls in its phase diagram, leading to large particle number fluctuations in the thermodynamic limit, a behavior distinct from standard TASEP models where such walls typically exist only along a specific line.
This paper demonstrates that thermal precondensation, a phenomenon characterized by condensates forming only over finite length scales, occurs in gauge-fermion theories near the thermal chiral phase transition, becomes more pronounced with an increasing number of fermion flavors, and holds potential relevance for physics beyond the Standard Model.
This study utilizes classical molecular dynamics simulations of argon-based two- and three-region models to analyze the intermediate fluctuations, correlations, and temperature distributions that characterize the process of heat conduction leading to thermal equilibrium, thereby providing a detailed microscopic perspective on the Zeroth Law of Thermodynamics.
This paper employs conformal bootstrap techniques to derive analytical expressions for two-point functions of bulk fields in critical loop models on the upper-half plane, specifically determining two-point connectivities for the Fortuin-Kasteleyn random cluster model under free and wired boundary conditions and validating these continuum predictions against lattice numerics through universal amplitude ratios.
This paper presents a purely geometric model of ordered fragmentation that establishes sharp upper bounds on the occupied volume of disordered granular matter, revealing non-monotonic volume evolution, asymptotic packing fraction scaling, and the existence of distinct geometric phases that align with experimental observations.
This paper develops an analytical framework to evaluate stochastic Verlet-type integrators by deriving closed-form expressions for their accuracy in simulating diffusion, drift, and harmonic potential sampling, ultimately identifying the GJ set of integrators as the most robust option for high-quality thermodynamic simulations.