2392 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 a topological classification of dynamical quantum phase transitions in the 1D XY model by linking integer and half-integer winding number jumps to interior and boundary critical modes, respectively, thereby identifying six distinct topological classes and providing a framework applicable to various one-dimensional two-band models.
This paper completes the classification of 4x4 solutions to the quantum Yang-Baxter equation by identifying the remaining non-regular solutions and demonstrating that, unlike regular cases, non-regular Lax operators can yield R-matrices that satisfy a modified rather than the standard Yang-Baxter equation.
This contribution presents a general microchannel simulated annealing procedure (MicSA) that drastically reduces the computational cost of random number generation in massively parallel Monte Carlo simulations, and demonstrates its effectiveness and dynamic equivalence to standard methods through rigorous benchmarks on three-dimensional Ising spin glasses using GPUs and the Janus-II supercomputer.
This paper extends the class of free-fermionic and free-parafermionic quantum chains with symmetry by introducing models with non-homogeneous, site-dependent interaction ranges, deriving the necessary conditions for their free-particle spectra, and analyzing their critical properties and dynamical exponents.
This paper demonstrates a quantum Mpemba effect in the generation of nonstabilizerness (quantum magic) within symmetric random circuits and nonintegrable Hamiltonian dynamics, revealing that states with lower initial magic can evolve into highly magical states faster than those with higher initial magic, a phenomenon driven by the specific spatial structure of the initial state rather than just conserved charge distributions.
This paper provides a pedagogical review of using Conformal Field Theory techniques to compute Schramm-Loewner Evolution observables in the upper half-plane, successfully recovering known results like Schramm's left-passage probability and anchored cluster densities while deriving new formulas for the densities of pivotal points in critical Fortuin-Kasteleyn clusters.
This paper reveals a hierarchical entanglement structure in chaotic many-body dynamics where, following local quantum quenches, the full state exhibits a Renyi-index-tuned transition with area-law scaling for and volume-law scaling for , while the linear response is dominated by a low-dimensional Schmidt sector that itself undergoes an area-to-volume-law transition.
This paper proposes a minimal theoretical framework incorporating stochastic mass fluctuations to explain the anomalous center-of-mass diffusion scaling () observed in clusters of active Brownian particles, demonstrating that a fluctuation-driven term dominates over conventional thermal noise to reproduce simulation results.
This paper develops a general framework based on form factors to analyze the real-time dynamics of integrable boundary quantum quenches, specifically investigating the time evolution of the pre-quench vacuum in the Lee-Yang model and validating these analytical results with numerical calculations using the truncated conformal space approach.
This paper proposes enhancing the Factorization Machine with Quadratic-optimization Annealing (FMQA) algorithm by designing initial training data using Latin hypercube and Sobol' sampling methods to ensure complete marginal bit coverage in one-hot encoding, thereby improving optimization performance on integer and discretized continuous variable problems.