1572 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 establishes that the score field of diffusion generative models obeys a viscous Burgers equation, providing a unified PDE framework to characterize speciation transitions as the sharpening of inter-mode interfaces with universal profiles and quantifying associated error amplification.
This paper proposes a statistical mechanical model that explains the emergence of musical meter as an ordered phase resulting from the optimization of a trade-off between pattern recognition and variety, successfully reproducing rhythmic characteristics found in Johann Sebastian Bach's compositions.
This study utilizes a one-dimensional capillary fiber bundle model to demonstrate that hydraulic fracturing enhances fluid extraction efficiency by lowering capillary thresholds, identifying an optimal pressure gradient that maximizes flow rates and enables the detection of extraction conditions through computationally efficient analysis of local flow profiles and Shannon entropy.
This paper establishes a general stochastic thermodynamics framework for autoregressive generative models that enables the efficient estimation of entropy production in non-Markovian processes, decomposing it into interpretable information-theoretic components like compression loss and model mismatch, and validates the approach on both linear Gaussian systems and the GPT-2 language model.
This study employs machine-learning molecular dynamics to demonstrate that the Jahn-Teller structural phase transition in LaMnO at approximately 750 K is an order-disorder process driven by the ordering of distortions, while revealing the persistence of dynamic local distortions above the transition temperature and validating the method's ability to distinguish such mechanisms from displacive behaviors.
This paper presents and validates a stochastic mean-reverting jump-diffusion model using long-term rainfall data from North-East India, demonstrating its ability to accurately simulate realistic rainfall statistics, including extreme events and multifractal features, while offering a controllable framework for generating synthetic time series.
This paper establishes that discrete harmonic morphisms provide the minimal condition for exact random walk projection during network renormalization, introducing a "harmonic degree" metric to evaluate how well various coarse-graining methods preserve dynamical invariants and revealing that Laplacian renormalization can spontaneously achieve exact dynamical preservation in real-world networks.
This paper proposes and analyzes a strategy for directed molecular transport in chemical gradients, where active carriers utilize polymerization to generate effective drift, ultimately deriving an effective Fokker-Planck equation to optimize the arrangement of active units for enhanced accumulation and motility.
This paper demonstrates that non-reciprocity stabilizes the homogeneous mixed state against compositional disorder in multicomponent non-reciprocal Cahn-Hilliard mixtures, deriving a general condition for spinodal instability via random matrix theory and linking the statistics of the most unstable eigenvalue to emergent nonlinear dynamics.
This paper introduces "Integral Decimation," a quantum-inspired algorithm that decomposes multidimensional integrals into a spectral tensor train representation to overcome the curse of dimensionality, enabling efficient and accurate calculations of free energy, entropy, and quantum dynamics in high-dimensional systems where conventional methods fail.