2439 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 develops a parameterized equation of state based on a new potential energy landscape theory that accurately describes the pressure, compressibility, and thermodynamic properties of hard sphere systems across the fluid, metastable, and glassy regions, while also identifying the breakdown of standard transport property scaling laws.
This paper introduces a lattice model of a chiral random walk that bridges classical stochastic diffusion and quantum evolution, demonstrating that the topological protection of edge currents persists from the unitary quantum limit into the dissipative classical regime.
This paper demonstrates that binary autoencoders improve the efficiency of FMQA-based black-box optimization by learning latent representations that better preserve the original problem's feasibility, neighborhood structure, and geometric properties compared to manual encodings.
This paper uses discrete element method simulations and -rheology continuum modeling to demonstrate that the velocity profile and surface flow layer thickness in rotating drums follow specific scaling laws derived through dimensional analysis.
This paper demonstrates that observed scaling signatures in brain activity can be artifacts of autocorrelated inputs or limited sampling, and proposes a robust framework to reveal that resting-state brain dynamics are actually near-critical due to reverberant network activity.
This paper demonstrates that in the Greenberg-Hastings neuronal model, the spontaneous activation probability acts as an external field that drives a dynamical phase transition, exhibiting clear finite-size scaling behavior and critical exponents as this probability approaches zero.
This paper uses numerical simulations of -FPUT atom chains with harmonic pinning to support the existence of a hydrodynamic limit and to investigate the relationship between energy current and forcing periods across different momentum flip rates.
The paper introduces a data-informed quantum-classical dynamics (DIQCD) approach that uses a flexible, time-dependent Lindblad equation to accurately predict the evolution of open quantum systems by optimizing a Hamiltonian to fit sparse and noisy experimental or simulated data.
This paper establishes a theoretical framework to analyze stabilizer Rényi entropy in the large- limit of solvable Sachdev-Ye-Kitaev models, revealing a series of first-order transitions in the Maldacena-Qi model—including an intrinsic transition undetectable by thermodynamic quantities—that positions stabilizer Rényi entropy as a novel order parameter for quantum magic.
The paper proposes a probabilistic statistical framework using Monte Carlo spectral decimation and -step gap distributions to distinguish between integrable and non-integrable quantum Hamiltonians by analyzing the probability of vanishing energy gaps.