1593 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 demonstrates that isotropic three-dimensional Fermi liquids exhibit a hierarchy of long-lived, non-hydrodynamic modes where odd-parity relaxation is significantly slower than even-parity relaxation due to Pauli blocking and interaction effects, a phenomenon previously thought unique to two dimensions.
This paper investigates operator growth in Brownian spin SYK models by deriving a closed master equation for the full operator-size distribution, enabling exact leading-order solutions and a systematic expansion that reveals the crucial role of higher-order corrections in quantum scrambling and chaos.
This first-principles multiscale computational study demonstrates that a ferrocene-functionalized covalent organic framework (MSUCOF-4-FeCp) significantly exceeds U.S. Department of Energy hydrogen storage targets with 18.0 wt% gravimetric and 72.6 g H2/L volumetric capacities at 298 K and 700 bar, offering a cost-effective alternative to precious metal-based materials.
This paper demonstrates that dissipation, typically viewed as a detrimental source of decoherence, can be harnessed as a resource in a Bose-Josephson junction to engineer distinct dynamical phases—including synchronization, coherence recovery, and controlled chaos—thereby enabling the manipulation of quantum coherence and the duration of information scrambling.
This paper rigorously establishes a universal lower bound on the local equilibration time of quantum many-body systems, proving that hydrodynamic behavior cannot emerge faster than a dimension-dependent fraction of the Planckian time , regardless of the underlying thermalization mechanism or microscopic details.
This paper investigates the effectiveness of various variational quantum eigensolver ansatzes, including hardware-efficient and physics-inspired approaches, in simulating the transverse-field Ising model across one, two, and three dimensions by benchmarking their performance on systems up to 27 qubits using metrics such as energy variance, entanglement entropy, and spin correlations.
This paper introduces a hydrodynamic framework for monitored classical stochastic processes, demonstrating that conditional ensembles can exhibit measurement-induced sharpening phase transitions and emergent relativistic invariance, while revealing novel critical phases and fixed points driven by global symmetries and monitoring rates.
This paper constructs the canonical ensemble of a self-gravitating thin shell in anti-de Sitter space using the Euclidean path integral approach to derive its thermodynamic properties, identify a fully stable equilibrium configuration, and demonstrate a first-order phase transition to a Hawking-Page black hole phase that occurs below a maximum temperature limit.
This paper establishes a nonequilibrium physical framework for biological adaptation by decomposing fitness into static generalism and dynamic tracking components, demonstrating how their interplay dictates optimal survival strategies and offering new methods for pathogen control through targeted environmental manipulation.
This paper introduces "diffusion codes," a class of classical and quantum LDPC codes constructed by applying tunable-depth random SWAP networks to graphs, which achieve a tradeoff between code optimality and geometric locality while ensuring self-correction and single-shot decoding with stabilizer sizes growing as an arbitrarily small power law.