2392 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 presents a novel derivation of the overdamped limit for kinetic Langevin dynamics with position-dependent coefficients using -hypocoercivity, which clarifies the origin of noise-induced drift, extends to coarse-grained and variable-mass models relevant to computational chemistry, and corrects an error in related literature.
This paper revisits the one-dimensional Ising model using Landau free energy and the density of states to rigorously prove the absence of spontaneous magnetization at any finite temperature by demonstrating that the free energy is an increasing function of magnetization with a positive, non-analytic second derivative at zero.
This paper establishes an operator-theoretic foundation for nonextensive statistical mechanics by demonstrating that a variational framework based on a self-referential nonlinear operator naturally yields Tsallis statistics in the mean-field limit, with the entropic index emerging directly from the operator's structural exponents rather than being postulated.
This paper introduces a rigorous semidefinite programming method that adapts the Lasserre hierarchy to produce convergent two-sided bounds on ground state energy densities and correlation functions for translation-invariant classical frustrated magnets, overcoming limitations of prior analytical techniques by handling non-quadratic Hamiltonians and non-Bravais lattices with high efficiency.
This paper establishes a volume-uniform spectral stability theorem for energy-truncated effective Hamiltonians in bounded finite-range quantum spin systems, proving that low-energy spectral subspaces remain stable with exponentially small errors in both finite and infinite volumes, thereby extending previous finite-volume results to the thermodynamic limit.
This paper demonstrates that spatially modulated nonreciprocal interactions in a two-species McKean-Vlasov system drive Hopf bifurcations leading to self-organized travelling waves and oscillatory states, establishing a minimal framework for nonequilibrium collective dynamics that persists at the particle level.
This paper investigates the hydrodynamic behavior of the -species particle-exchange process, deriving explicit solutions for its coupled inviscid Burgers equations and characterizing the stationary phase diagram of the open system, which exhibits boundary-induced phases analogous to the single-species asymmetric simple exclusion process.
This paper identifies a new form of universal far-from-equilibrium dynamics in Ising models following a symmetry-breaking quench, characterized by a previously unknown dynamical critical exponent and a lower critical effective dimension that distinguishes observable scaling in higher-dimensional systems from lower-dimensional ones.
This paper reveals a multiscale structure in the eigenstate thermalization hypothesis by demonstrating that the statistical properties of matrix elements in macroscopic systems depend not only on macrostate parameters but also on the fluctuation scale of the sampling ensemble, leading to non-analytic, scale-dependent algebraic exponents.
This paper demonstrates that minimal neural networks with as few as three parameters can probabilistically invert the renormalization group procedure to generate scale-invariant critical configurations of the 2D Ising model, capturing essential universality and RG eigenvalues without requiring complex architectures.