1537 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 establishes the universality of the Random Energy Model for a linear random energy system with i.i.d. real random variables and Ising spins under exponential thinning, proving that the energy levels converge to a Poisson point process while the Gibbs weights converge to a Poisson–Dirichlet law and the free energy exhibits a freezing transition.
This paper proves that operator growth in the disordered Ising chain with strictly positive couplings and fields exhibits almost factorial scaling in both time and spatial support, thereby rigorously ruling out dynamical localization at any disorder strength and revealing a structural path-entropy obstruction to perturbative many-body localization.
This paper introduces an agent-guided multi-fidelity learning framework that employs a structural agent to diagnose numerical instabilities in GW-Bethe-Salpeter calculations and applies machine learning corrections to accurately predict quasiparticle and excitonic properties in strained MoS2-WS2 bilayers, demonstrating that explicit detection of numerical fragility is essential for reliable surrogate modeling of excited-state materials.
This paper demonstrates that metastable phases, typically suppressed in equilibrium, can be tracked and characterized as regions in the complex plane of thermal fields delineated by Lee-Yang zeros, offering a new framework for understanding and engineering non-equilibrium collective states in periodically driven systems.
This paper investigates collective dynamics in a one-dimensional anisotropic Heisenberg ferromagnetic spin chain, demonstrating that while large numbers of spins tend to desynchronize, field-like torque can restore synchronous oscillations, with numerical results confirming analytical predictions for inphase frequencies.
This paper derives an exact closed-form expression for the ground-state Bures metric of massive Dirac fermions under Aharonov-Bohm flux, revealing a universal Lorentzian profile controlled by the Dirac mass that diverges in the chiral limit and serves as a geometric counterpart to thermodynamic critical behavior, independent of topological invariants.
This paper formalizes structure and patterns in three one-dimensional spin-lattice models by deriving their Boltzmann distributions as stochastic processes and analyzing them through computational mechanics, where information-theoretic measures and epsilon-machines successfully characterize the systems' configurations in agreement with statistical mechanics.
This paper numerically validates a nonlinear elasticity theory by demonstrating that shear banding in amorphous solids arises from a screened soft mode instability driven by topological screening and nonlinear coefficients, fundamentally distinguishing the phenomenon from fracture.
This paper establishes that a successful variational autoencoder inherently learns a latent mean-field theory by demonstrating that its conditionally independent decoder is structurally identical to a finite-size mean-field factorization, a finding validated on both solvable statistical physics models and real neural population data to recover underlying interaction patterns.
This paper demonstrates that stochastic motion at fluid interfaces generates finite wave resistance below the deterministic radiation threshold and regularizes singular responses, providing explicit scaling laws for drifted Brownian trajectories and closed-form solutions for drifted Lévy flights.