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.
This paper demonstrates that metastability in monitored quantum systems, such as associative memories, cannot be fully characterized by ensemble-averaged Liouvillian spectra alone, as individual quantum trajectories exhibit distinct behaviors—ranging from bypassing to preserving memory states—depending on the specific monitored channel and measurement record.
This review reassesses nanothermodynamics and Hill's subdivision potential, demonstrating their necessity for resolving thermodynamic heterogeneity, stabilizing finite Ising chains, extending entropy to be exactly extensive, and providing a counterexample to reversible statistical mechanics through irreversible entropy maximization.
This paper proposes using algorithmic randomness to precisely define physical typicality, demonstrating how this framework allows the distribution postulate in Bohmian mechanics to be formulated as a statistical law that guarantees standard Born statistics for computable experimental protocols.
This paper investigates the de Almeida-Thouless (AT) stability of the replica symmetric solution for a blind spread-spectrum watermarking model with image restoration, revealing that the solution's instability explains the discrepancies previously observed between theoretical predictions and simulation results in specific parameter ranges.
This paper presents an experimental realization of a macroscopic triangular Ising antiferromagnet using mechanically driven NdFeB cylinders, demonstrating the ability to reach its ground state and probe magnetic correlations within manually preselected topological sectors to visualize and manipulate frustrated magnetism phenomena.
This paper develops a dynamical mean-field theory for nonreciprocal associative neural networks, demonstrating that the spectral structure of coupling matrices—specifically the distribution of eigenphases—determines memory retrieval capacity by controlling the interplay between retarded self-interactions and quenched noise, thereby enabling the storage of an extensive number of dynamical attractors ranging from limit cycles to strange attractors.
This paper establishes an exact mapping of non-Markovian optimal paths for active Ornstein-Uhlenbeck particles onto a higher-dimensional Hamiltonian dynamical system, enabling the systematic computation of rare event trajectories and revealing distinct dynamical regimes, such as closed periodic orbits, that emerge only at non-zero energies.
This study reveals that mixtures of passive and active particles following Vicsek-like rules can exhibit remarkably fast, potentially exponential, liquid-liquid phase separation kinetics after temperature quenches, driven by velocity field coarsening despite high system density.
This paper determines exact finite-volume spin form factors and two-point correlations for a massive Kramers-Wannier interface in the scaling Ising model by combining complementary lattice realizations, factorized Fredholm determinants, and independent numerical confirmations to characterize the interface separating ordered and disordered phases.
This paper provides a thermodynamic analysis of the Microcanonical Hamiltonian Monte Carlo algorithm, demonstrating its adherence to the Helmholtz theorem and its representation of a microcanonical ensemble, while ultimately arguing that canonical Markov Chain Monte Carlo methods are more natural from both thermodynamic and information-theoretic perspectives.