1592 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 compares three frameworks for modeling complex fluid flows—local conservation laws, the GENERIC formalism, and the Onsager principle—by illustrating their application to isothermal and incompressible polymeric fluids.
This paper proposes a "field digitization scaling" framework that treats the number of discrete field values as a renormalization group coupling, successfully applying it to relate the 2D classical clock model to the XY model and its quantum gauge theory counterpart to enable continuum limit analysis in quantum simulations.
This study reveals that defect creation and annihilation in diverse active living systems exhibit spatial symmetry breaking and irreversibility driven by a dualism between nematic structure and polar forces, challenging conventional active nematic theories and highlighting these processes as major sources of entropy production.
This paper reviews and extends the generalized Keffer form of the Dzyaloshinskii constant by combining three known contributions and suggesting a fourth, analyzing their macroscopic consequences for spin, momentum, and polarization evolution while also proposing an analogous form for the exchange integral in symmetric Heisenberg Hamiltonians.
This paper employs bond-weighted tensor renormalization group calculations to demonstrate that dimensionless partition-function ratios for the Ising and Potts models obey finite-size scaling and yield critical values consistent with conformal field theory predictions, while also revealing logarithmic corrections in the four-state Potts model.
This paper establishes a geometric control framework for boundary-catalytic branching processes by utilizing a Steklov spectral problem to determine the critical absorption rates required to balance population growth and achieve a steady state, while identifying a threshold beyond which such control becomes impossible.
This paper introduces minimal models, including the Arithmetic Ising Model and specific gas models, to demonstrate how entropic effects can drive generic high-energy states into ordered phases, resulting in spontaneous symmetry breaking at arbitrarily high temperatures.
This paper proposes a hardware-efficient framework for constrained optimization on probabilistic machines by introducing native multi-state p-dits and mean-field constraints to eliminate dense couplings, thereby restoring sparsity and enabling orders-of-magnitude acceleration via FPGA implementation.
The paper introduces **peapods**, a high-performance, open-source Python package leveraging a Rust core to efficiently simulate Ising spin systems on periodic Bravais lattices using a comprehensive suite of single-spin-flip, cluster, and replica-based Monte Carlo algorithms validated against exact critical temperatures.
This paper proposes group entropies as a unifying framework that defines universality classes of extensive entropies for strongly correlated systems, successfully deriving consistent thermodynamic laws and demonstrating that black holes naturally exhibit negative specific heat within this formalism.