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 non-reciprocally interacting Ornstein-Uhlenbeck processes to demonstrate how exceptional points induce anomalous polynomial relaxation, how disorder creates non-self-averaging singularities, and how complete asymmetry leads to pseudo-equilibrium states and boundary refrigeration effects.
This paper demonstrates that, contrary to conventional wisdom, particle collisions can enhance self-diffusion in odd-diffusive systems by inducing a mutual rolling effect that facilitates motion rather than hindering it.
This paper demonstrates that in equilibrium odd-diffusive systems, the force autocorrelation function can exhibit negative values and temporal oscillations, leading to an unexpected enhancement of the self-diffusion coefficient due to particle interactions.
This paper presents a first-principles field-theory for active chiral hard disks that explicitly accounts for steric interactions to derive a hydrodynamic hierarchy, ultimately recovering established active matter models and demonstrating how chirality can invert the sign of phenomenological activity parameters in the resulting Active Model B+.
This paper provides an exact solution for two interacting hard-disk-like odd-diffusing particles in the Fourier-Laplace domain, demonstrating that their "mutual rolling" dynamics cause a transient overshoot in relative rotation that explains the oscillating force autocorrelation function observed in such systems.
Through analytical and computational studies, this paper demonstrates that interparticle interactions in a chiral fluid can cause a driven tracer to exhibit a complete reversal of both its transverse Hall drift and longitudinal advection, a phenomenon driven by the interplay between odd mobility and interaction-mediated forces.
This paper proposes a biophysically motivated associative memory network with activity-dependent gating that mimics neuromodulation, demonstrating that such a mechanism fundamentally reorganizes the attractor landscape to bypass classical capacity limits, eliminate catastrophic breakdown, and enable robust retrieval of pattern clusters beyond the standard Hopfield limit.
This paper presents a comprehensive spectral atlas of classical dipolar Heisenberg models on all Archimedean and Laves planar lattices, systematically characterizing their ground states, spin-wave dispersions, and quantum corrections to reveal that the failure of the Luttinger-Tisza strong condition precisely predicts incommensurate canting while demonstrating that frustration and fluctuation classifications are independent.
This paper establishes a unified framework for classifying dipolar ordering across all fourteen Bravais lattices by combining Ewald-summed interactions with Luttinger-Tisza minimization and spin-wave corrections, revealing that the entire landscape collapses into four distinct attractors while identifying a unique non-circular ground state in face-centered orthorhombic lattices that defies standard single-k approximations.
The paper introduces FrOGS, a hybrid discrete neural sampler that couples an autoregressive model with a continuous-time Markov chain to efficiently generate independent alloy configurations and provide unbiased free energy estimates across diverse chemical conditions on a common absolute scale, outperforming existing methods like MCMC and SEGAL in accuracy and stability.