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 investigates a nonequilibrium bead-spring model to demonstrate that kinetic temperatures serve as the fundamental organizing variables unifying energy storage, heat transport, and irreversibility, revealing that while energy observables depend on mass arrangement and exhibit path dependence in singular limits, transport and entropy production remain invariant under mass exchange.
This paper demonstrates that inertia in a nonequilibrium two-bead system coupled to heat baths acts as a critical generator of information flow, revealing hidden divergences, symmetry-breaking geometric structures, and bimodal behaviors that are absent in the overdamped limit, thereby establishing a minimal framework for designing information-driven nanomachines.
This study employs molecular dynamics simulations to demonstrate that thermally activated membranes, which induce stochastic elastic scattering events, generally reduce the self-diffusion of SPC/E water in a manner consistent with the Arrhenius approximation at high temperatures, while having only a local effect on the water's tetrahedral order and hydrogen-bond lifetime.
This paper reformulates the statistical mechanics relaxation problem by identifying local time statistics of signals as the operational objects of relaxation, demonstrating that global irreversible prediction relaxation is possible for finite bounded systems, and interpreting entropy as mutual information between an observer and the system's unknown past.
This paper introduces the wavelet conditional renormalization group (WCRG) method, a learned multiscale sampling technique that overcomes critical slowing down in frustrated spin systems—where traditional cluster algorithms fail—by recursively generating configurations from coarse to fine scales with an overall sampling complexity of .
This paper analytically and numerically investigates the large- free energy of the critical vector model on with angular velocity, revealing that while the leading high-temperature pole matches the free theory, sub-leading terms exhibit non-analytic behavior that distinguishes the critical fixed point from the free limit.
Using exact diagonalization of Dirac fermions on a sphere to leverage full symmetry and circumvent the parity anomaly, this study demonstrates that the interaction-driven multicritical point of the two-dimensional Chern insulator transition belongs to the Gross-Neveu-Yukawa universality class, with extracted operator scaling dimensions showing strong agreement with conformal bootstrap predictions.
This paper investigates a nonlinear refrigerator operating between a finite-sized cold bath and an infinite hot bath, deriving the optimal temperature path to minimize work and analyzing how the coefficient of performance depends on input power and the specific nature of the finite cold bath.
This paper demonstrates that Clifford-augmented matrix product states (CAMPS) enable large-scale, real-time simulations of flux tube dynamics in (2+1)-dimensional Higgs gauge theories, revealing that effective string theory accurately describes collective behavior in the rough regime while uncovering a novel long-lived prethermal regime in the strong confinement limit.
This paper introduces an adaptive biased percolation process to demonstrate that the network dismantling problem exhibits a universal phase transition in the thermodynamic limit, characterized by the simultaneous collapse of the giant connected component and the largest 2-core across diverse network topologies.