1536 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 revisits Otto-Villani functional inequalities to establish a geometric framework linking free energy dissipation and optimal transport for quantifying the relaxation speed of conservative stochastic systems toward steady states, with numerical validation on Landau-Ginzburg potentials.
This paper presents a quantitative, unsupervised approach that combines Natural Language Processing with complex systems theory to analyze a diachronic corpus of 600,000 Italian newspaper articles from 1985 to 2000, successfully detecting major historical turning points in media discourse without relying on prior labeling.
This paper proves that nonequilibrium steady states on large dense networks exhibit Boltzmann-like occupation probabilities despite extensive entropy production, driven by the principle that these systems spend more time in states they exit more slowly.
This study numerically demonstrates that the percolation of rod-like particles through a static bed of spheres is governed by a transition between trapping and passing regimes determined by rod length and pore geometry, where shorter rods move nearly twice as fast as longer ones due to reduced susceptibility to geometric trapping.
This paper establishes a tenfold classification of symmetry and topology for monitored free fermions by analyzing Kraus operators and their effective non-Hermitian generators, thereby elucidating the role of topology in measurement-induced phase transitions and demonstrating a bulk-boundary correspondence where nontrivial spacetime topology leads to protected dynamical slowdowns and gapless boundary states.
This paper establishes universal fluctuation relations and a Thermodynamic Uncertainty Relation for non-Abelian quantum transport, demonstrating how the non-commutativity of conserved charges can lead to apparent second-law violations, enhanced current precision, and current inversion against affinity biases.
This paper demonstrates that rotating the boundary of bilayer graphene cavities relative to the underlying lattice induces a quantum transition from integrable to chaotic dynamics, a phenomenon confirmed through both full quantum analysis and semiclassical ray dynamics.
By numerically studying a one-dimensional Hamiltonian-Potts model with fractional spatial derivatives under steady heat conduction, this paper demonstrates that a stationary interface between coexisting phases exhibits a temperature deviation from equilibrium values, thereby confirming the violation of local equilibrium and the stabilization of metastable states in nonequilibrium first-order phase transitions.
This paper predicts that cyclic interaction changes in an integrable one-dimensional Bose gas drive the system into a novel critical phase characterized by fractional Fermi seas with reduced occupancy, exhibiting correlation signatures distinct from conventional Tomonaga-Luttinger liquids.
This paper introduces Boltzmann attention, an energy-based mechanism that augments standard attention with learnable pairwise couplings modeled as an Ising system to explicitly capture cooperative and antagonistic inter-position dependencies, demonstrating improved performance in sequence modeling tasks and offering a pathway for quantum annealing-based training.