1561 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 proposes a graphical model and develops both message-passing algorithms and a replica theory based on cumulant expansion to analyze and perform tensor factorization and completion of relatively high-rank tensors under sparse, random graph-based sampling in the high-dimensional dense limit.
This paper employs Koopman mode decomposition to establish a general framework that linearizes nonlinear Langevin dynamics, thereby decomposing thermodynamic dissipation into interpretable, frequency-dependent contributions from individual oscillatory modes and demonstrating how these modes govern energy loss in phenomena like coherent resonance within the noisy FitzHugh-Nagumo model.
This paper demonstrates that sudden-quench quantum Otto cycles utilizing simultaneous multi-parameter control significantly outperform single-parameter cycles in both net work and efficiency for engines, as well as in coefficient of performance for refrigerators, across experimentally realistic many-body systems like one-dimensional Bose gases and transverse-field Ising models.
This paper presents a method to extract geometrically local conserved operators, including parent Hamiltonians, from infinite projected entangled pair states (iPEPS) by analyzing the quantum geometry of parameter-deformed manifolds via static structure factors, successfully identifying both frustration-free and non-frustration-free Hamiltonians with improved locality for states like the short-range RVB and deformed toric code.
Experiments on dense colloidal silica suspensions in rotating microfluidic drums reveal that while thermal agitation causes the smallest particles to exhibit zero angle of repose, larger particles arrest at finite angles that remain below the minimal value for athermal frictionless granular materials, a phenomenon explained by a rheological model linking the arrest to a crossover between glass and jamming transitions driven by the competition between gravitational and thermal pressures.
This paper investigates the emergence and detection of Hawking radiation in a quenched 1D chiral spin chain by mapping its dynamics to a curved spacetime Dirac fermion, revealing that while the radiation spectrum exhibits greybody-like deviations, a weakly coupled qubit detector can faithfully measure the Hawking temperature, whereas strong coupling obscures the thermal signature by thermalizing with the global environment.
This paper presents a framework for constructing physics and causally constrained discrete-time neural models that accurately capture the statistics and forcing responses of turbulent dynamical systems by enforcing energy-preserving nonlinearities and suppressing spurious interactions.
This paper reviews novel approaches for measuring physical entropy in nonequilibrium steady and absorbing states, highlighting their successful application to diverse systems like jammed packings and swarming bacteria while distinguishing these physical applications from general statistical entropy estimation challenges.
This paper provides a complete mechanistic characterization of in-context learning in transformers trained on discrete Markov chains, revealing four algorithmic phases implemented by distinct multi-layer subcircuits and identifying the specific data diversity thresholds and training dynamics that govern the transition between memorization and generalization strategies.
This paper theoretically investigates steady-state and non-equilibrium dynamics in noisy networks by deriving conditions for equilibrium, analyzing non-equilibrium steady states through probability currents and drift velocities, establishing a general fluctuation-dissipation relation, and demonstrating that conventional overdamped Brownian dynamics is a special case of this broader framework.