2356 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.
Through numerical simulations and analytical theory, this study demonstrates that chiral active particles inside a deformable vesicle generate non-monotonic rotational dynamics with an optimal chirality, revealing a universal scaling law that links collective alignment and active stress transmission to vesicle motion.
This paper demonstrates that a stochastic-generator expansion method, originally developed for quantum kinetically constrained models, can be systematically applied to classical models to reveal a nested hierarchy of metastable configurations and their associated relaxation time scales based on the smallest domain lengths.
This paper analyzes an overdamped Brownian motor with hidden internal variables to demonstrate that while the observed mechanical current vanishes at stall, the system still produces entropy due to unresolved hidden currents, a dissipation that can be exactly quantified through a rank-one coupling structure despite being invisible to single-coordinate monitoring.
Using density matrix renormalization group and Quantum Monte Carlo simulations, this study maps the zero- and finite-temperature phase diagrams of a spin-1/2 Heisenberg antiferromagnet on an extended Lieb lattice, revealing field-driven quantum phase transitions characterized by and magnetization plateaus and a gapless spin-canted phase, while finding no evidence for thermal phase transitions.
This paper introduces the Flag Game, a toy model that reveals how collective belief formation in AI swarms transitions from collapse to polarization as population size increases, and proposes a dual approach of social circuit attribution and statistical mechanical theory to achieve mechanistic interpretability of these emergent behaviors.
This paper demonstrates that a single non-relativistic quantum particle linearly coupled to a thermal bath can exhibit emergent hydrodynamic behavior at late times, where spatial decoherence allows the reduced density matrix to be approximated by dissipative equations that asymptotically reduce to the Navier-Stokes equations for a compressible fluid with drag.
This paper proposes a game-theoretic framework for pricing data as a non-rival commodity by modeling strategic competition among players with asymmetric information, revealing that Nash equilibrium analysis yields unique market dynamics—such as zero-cost sharing and counterintuitive rivalry effects—that establish a theoretical foundation for valuing intangible goods in digital markets.
This study demonstrates quantum-projection-noise-resolved readout of a strongly-interacting 2D ensemble of nitrogen-vacancy centers in diamond, enabling the direct observation of how intrinsic dipolar interactions transform isotropic quantum noise into an anisotropic profile and paving the way for entanglement-enhanced solid-state sensing.
This study demonstrates that while both layer-symmetric and layer-antisymmetric optical drives can enhance superconducting-like optical conductivity in phase-disordered bilayers, only the symmetric drive strengthens relative-phase locking and magnetic screening, providing a crucial symmetry-based diagnostic for interpreting transient superconductivity in materials like YBCO.
This paper demonstrates that combining the locality-preserving Derby-Klassen fermion-to-qubit mapping with a constraint-incorporating Hamiltonian Variational Ansatz enables accurate and resource-efficient quantum simulation of two-dimensional interacting fermionic systems, offering significant advantages over Jordan-Wigner transformations by reducing operator nonlocality and circuit overhead in higher dimensions.