1537 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 constructs families of periodic planar lattices, specifically Apollonian lattices, that achieve arbitrarily high critical temperatures for the ferromagnetic Ising model by demonstrating that scales logarithmically with the maximal coordination number and conjecturing this family to be optimal for such systems.
This paper demonstrates that apparent double-transition temperatures observed in transport experiments on anisotropic two-dimensional superconductors can arise as artifacts of finite-size and finite-current effects in a single BKT transition, implying that robust splittings observed in real materials like KTaO interfaces must stem from physics beyond this minimal anisotropic baseline.
This paper introduces a general framework for constructing real-space order parameters from dominant Fock-state patterns that reveal hidden sub-structures within topological phases, offering a more refined classification and robust diagnostic tool for both ordered and disordered quantum many-body systems.
This paper extends the renormalization group framework to nonunitary quantum dynamics by demonstrating that the competition between decoherence and coherent evolution drives the flow toward chaotic behavior without fixed points, exemplified by a measurement-induced parity-time transition belonging to the one-dimensional Yang-Lee edge singularity universality class.
This paper reviews three main classes of numerical approaches for computing transport coefficients in molecular dynamics—nonequilibrium, equilibrium time-correlation, and transient methods—while providing numerical analysis to quantify errors and discussing recent variance reduction techniques to improve computational efficiency.
This paper derives an exact, closed non-Markovian diffusion equation for the probability density of particle displacements driven by arbitrary Gaussian velocity processes by constructing a systematic hierarchy of equations based on Wick's theorem, which generalizes the Fokker-Planck description while preserving Gaussianity only in the infinite-order limit.
The paper introduces GG-PA, a training-free framework that combines pretrained diffusion priors with explicit physical context via a generative Gibbs sampler to achieve asymptotically exact sampling and recover context-induced distribution shifts in scientific systems without retraining.
This paper establishes a rigorous, duality-invariant definition of critical states based on the simultaneous absence of exponential localization in both real and momentum space (the Liu–Xia condition), transforming it from a phenomenological criterion into an exact solvability principle that predicts critical lines and surfaces in diverse quasiperiodic and non-Hermitian models.
This paper employs statistical physics to precisely characterize the storage capacity of linear associative memories, demonstrating that a decoupled model equivalent to the original system can store up to associations and revealing that optimal solutions achieve this by raising correct scores just above the extreme-value threshold of competing outputs rather than broadly boosting alignments.
This paper demonstrates that multipartite entanglement, quantified by the scaling of quantum Fisher information, serves as a robust diagnostic tool for identifying topological and long-range phases in disordered Kitaev chains with variable-range pairing and modulated chemical potentials.