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 proposes a scalable deep-learning method to overcome computational barriers in estimating Stochastic Information Flow (SIF) from time-series data, demonstrating its utility as a data-driven indicator of cooperative structures across theoretical models and empirical biological trajectories.
Through Monte Carlo simulations of the three-dimensional nonconserved XY and Ising models, this study reveals anomalously slow phase ordering growth at zero temperature and demonstrates a robust Mpemba effect where systems quenched from higher initial temperatures reach equilibrium faster, with findings that hold across different initial magnetization distributions and offer significant experimental relevance.
This paper establishes a novel Lieb-Schultz-Mattis theorem for a one-dimensional gauge theory coupled to matter, demonstrating that kinematic Gauss law constraints generate a U(1) symmetry which forbids trivial gapped ground states and necessitates either spontaneous symmetry breaking or gapless excitations, the latter exhibiting free Dirac fermion behavior with specific correlation decay.
This paper derives transient-time correlation function expressions for both linear and nonlinear dynamic elastic moduli using equilibrium stress fluctuations and DOLLS/SLLOD equations, thereby enabling the computation of anharmonic viscoelastic responses from equilibrium molecular dynamics simulations without explicit nonequilibrium deformation protocols.
The paper introduces Thermal Tensor Network Renormalization (TTNR), a novel algorithm combining global optimization with finite-temperature density matrix construction to accurately extract conformal field theory data and efficiently identify phase transitions in two-dimensional quantum systems.
This paper demonstrates that the exact marginalization of binary correction variables in physics data is mathematically equivalent to the Ising model, enabling the use of efficient statistical physics tools to handle exponentially complex configurations and accurately quantify uncertainties in applications like Type Ia supernova calibration.
This paper presents a pedagogical tutorial on replica tensor-network techniques for analyzing random quantum circuits, demonstrating how to map circuit-averaged observables to classical statistical-mechanics models and providing an accompanying open-source library for implementation.
This paper bridges a gap in the study of the 3D dipolar universality class by introducing a constrained lattice model and a hybrid Monte Carlo algorithm to simulate systems up to , thereby confirming a continuous phase transition and providing new estimates for critical exponents and the Binder ratio.
This paper investigates a one-dimensional swarmalator model with asymmetric time delay, revealing that the delay's internal structure fundamentally reshapes the collective phase diagram by systematically expanding the active state and establishing that the delay's form, rather than just its magnitude, is a decisive factor in emergent swarmalator behavior.
This paper employs field-theoretic renormalization group analysis to demonstrate that in non-reciprocally coupled conserved spin systems where non-reciprocity arises solely from nonlinear interactions, the critical dynamics for asymptotically recover detailed balance and exhibit reduced scaling exponents, rendering the large-scale behavior independent of microscopic non-reciprocity.