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 establishes that a successful variational autoencoder inherently learns a latent mean-field theory by demonstrating that its conditionally independent decoder is structurally identical to a finite-size mean-field factorization, a finding validated on both solvable statistical physics models and real neural population data to recover underlying interaction patterns.
This paper demonstrates that stochastic motion at fluid interfaces generates finite wave resistance below the deterministic radiation threshold and regularizes singular responses, providing explicit scaling laws for drifted Brownian trajectories and closed-form solutions for drifted Lévy flights.
This paper presents a generalized Brownian motion model consistent with the GKSL equation to derive an analytical expression for steady-state heat flow that satisfies Fourier's law and captures thermal boundary resistance, while also revealing unique transient heat current behaviors and continuous, nowhere-differentiable trajectories that distinguish it from standard models.
This paper presents a perturbative framework using Gaussian random matrices to derive explicit expressions for energy transfer rates and heat conductance in randomly coupled quantum systems, providing leading- and next-to-leading-order results for various density of states in the large- limit.
This paper utilizes the MSRDJ formalism to demonstrate that in slow isothermal processes for gapped systems, the -th cumulant of work scales as with arbitrary smooth protocols, while deriving coefficients that link these cumulants to equilibrium thermodynamic geometric tensors.
This paper proposes a theory based on time- and phase-averaging of fast energy modulations involving next-nearest-neighbor atoms to explain excess specific heat and energy fluctuations in crystals that deviate from Debye's -law, offering new insights into amorphous materials and quantum device noise.
This paper demonstrates that in finite-dimensional Bayesian inverse problems with equality constraints, sampling via penalized residuals in the full parameter-state space yields a posterior distinct from the reduced-space posterior due to a missing Jacobian determinant factor, and it derives specific determinant corrections required to ensure that zero-noise residual limits correctly recover the graph-lifted reduced posterior.
This paper derives the exact solution for the ferromagnetic two-dimensional Ising model with a transverse field by establishing its equivalence to the three-dimensional Ising model, a result that can also be extended to antiferromagnetic cases without frustration.
This paper establishes a framework for topological quantum statistical mechanics and topological quantum field theories by analyzing the nonlocal and topological features of the 3D Ising model, demonstrating that these theories require the Jordan-von Neumann-Wigner framework, violate the ergodic hypothesis at finite temperatures, and exhibit topological phase transitions near extreme temperatures that signify a breaking of time-reversal symmetry.
This paper formulates elastic and elasto-inertial turbulence within the Martin-Siggia-Rose path-integral framework to derive nonperturbative Ward identities via a symmetry algorithm, which are then applied to an extended Burgers equation model to constrain closure schemes and elucidate scaling behavior near fixed points.