1558 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 introduces a theoretically grounded, Python-implemented framework that translates Dirac notation into weighted model counting (WMC) instances, enabling the systematic application of automated reasoning heuristics to calculate partition functions for diverse quantum and classical physical models.
The paper introduces RGFlow, a bijective, flow-based deep neural network framework that autonomously learns real-space renormalization group transformations by minimizing mutual information, successfully reproducing classical decimation rules and identifying the Wilson-Fisher critical point in two-dimensional field theory.
This study demonstrates that the critical scaling behavior of a one-dimensional anisotropic XY model persists in a non-Hermitian framework with a complex transverse field, where the ferromagnetic and Luttinger liquid phases are governed by symmetry breaking and emergent symmetry with spectral degeneracy, respectively, thereby revealing a robust pathway for observing conventional quantum phase transitions in open systems.
Using scaled-charge molecular dynamics simulations, this study reveals that while negatively charged silica nanopores exhibit conventional cation selectivity for NaCl, they lose this selectivity for CaCl due to calcium ion immobilization and charge inversion, which shifts dominant conduction to chloride ions in the pore interior.
This paper demonstrates the measurement of entanglement entropy and the observation of a disorder-induced transition from chaotic to localized dynamics in a programmable neutral-atom quantum simulator (QuEra's Aquila) by employing a novel randomised measurement protocol that bypasses the need for local gate control through the combination of local energy tuning and a global field.
This paper validates the "asdf" information-theoretic framework by analytically demonstrating that its residual mapping approach to entropy estimation exactly reproduces the classical thermodynamic entropy for solvable systems like the ideal gas and harmonic oscillator, thereby establishing consistency with statistical mechanics and supporting the interpretation of entropy as a geometrically encoded information measure.
By employing a Markovian embedding to establish a hierarchy of entropy production, this paper demonstrates how non-Markovian memory effects can be exploited to enhance thermodynamic performance, including improved precision-to-dissipation ratios and finite heat currents at vanishing entropy production, while deriving extended trade-off relations for uncertainty, speed limits, and power-efficiency.
This paper utilizes Floquet-Born-Markov theory to demonstrate that the topological classification of periodically driven Su-Schrieffer-Heeger chains, characterized by ensemble geometric phases and protected edge modes in both $0$ and quasienergy gaps, robustly extends to dissipative, finite-temperature open quantum systems.
This study employs a modified Cahn-Hilliard-Cook framework to demonstrate that phase separation patterns in binary liquids driven by a moving cooling source are governed by the interplay between the source's translation speed and the thermal front's propagation speed, allowing for the engineering of specific structures by tuning these competing velocities.
This paper demonstrates that measurement-free random quantum circuits with reset channels undergo a continuous, second-order entanglement phase transition for qubits (), a behavior that significantly deviates from the classical statistical predictions derived in the large- limit.