1836 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 rigorously proves that systems of hard rods in a one-dimensional harmonic trap possess no additional analytic conserved quantities beyond total energy and center-of-mass energy, while demonstrating that such extra quantities do exist when the rods are reduced to zero-length point particles.
This paper reveals that a spatially extended bistable gene toggle model exhibits a unique deterministic coarsening mechanism characterized by pinned domain walls that disappear via abrupt cascade events, resulting in a sub-diffusive power-law decay with log-periodic oscillations driven by discrete scale invariance rather than classical curvature-driven dynamics.
This paper establishes a theoretical framework linking discrete probability distributions to the statistical mechanics of small systems, demonstrating how concepts like infinite divisibility and scalability underpin quasiparticle descriptions, virial expansions, and phase transitions, while extending these principles to non-extensive thermodynamics and deformed distributions.
This paper introduces a concrete example of a substochastic Markov chain derived from an open Pelikan random map that exhibits a spectrum of infinitely many quasi-stationary distributions with distinct escape rates, while also establishing the existence of unique quasi-ergodic distributions for certain parameters and validating these findings through numerical simulations.
This paper proposes that quantum mechanics becomes consistent with the second law of thermodynamics and naturally explains the measurement process, irreversibility, and the transition from pure states to mixtures by modeling quantum systems with continuous spectra in the thermodynamic limit, thereby deriving time-symmetry breaking, equilibrium, and the Born rule without ad hoc assumptions.
Using large deviations theory, this paper derives a general free energy functional for dense associative memories with polynomial and Log-Sum-Exponential interactions, enabling the analysis of memory retrieval dynamics, ground-state energies, and exact full-retrieval thresholds across diverse architectures.
This paper derives the commutation relations required for energy-conserving nonlinear terms to block macroscopic superpositions, demonstrating that while spatial confinement terms satisfy these constraints, previously proposed terms for spin models do not, and further illustrates the resulting energy barrier for forming spatial "cats" through a toy model.
This paper establishes a generalized BBGKY hierarchy for near-integrable many-body systems with both contact and long-range interactions, demonstrating that while single-particle distributions relax via a Boltzmann-type scattering integral, higher-order correlations remain non-thermal, a framework that successfully explains experimental observations in dipolar quantum gases and extends to various other physical systems.
This paper demonstrates that the conventional deep thermalisation framework fails for mixed initial states, necessitating a new paradigm where mixed-state deep thermalisation emerges dynamically in chaotic systems as a maximum-entropy ensemble on an augmented system that explicitly depends on the initial state's entropy.
This paper introduces a geometry-aware deep learning framework that utilizes layer-wise local training to sculpt internal representations for enhanced adversarial robustness, supported by data-dependent statistical mechanics and a phenomenological Hebbian coupling model.