2377 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 introduces a general method using a kinetic potential as a Lyapunov function to demonstrate that diffusion inevitably equilibrates in non-ideal reaction-diffusion systems driven by autonomous chemostats, provided their reaction networks are either pseudo-detailed balanced or complex balanced.
This paper investigates the sequential barrier crossing dynamics of an overdamped dimer in a bistable potential under thermal fluctuations and weak periodic forcing, demonstrating that the Conditional Residence Time (CRT) reveals a non-monotonic dependence on drive frequency due to the competition between escape times and forcing cycles, thereby establishing CRT as a key metric for quantifying transition initiation and completion in coupled stochastic systems.
This paper demonstrates that in a reciprocal Brownian motor, the entropy production hidden behind a mechanically stalled coordinate can be exactly reconstructed from measurements of that coordinate alone, as force-torque reciprocity and translational symmetry ensure the Harada-Sasa heat equals the coordinate's dissipation without requiring time-scale separation.
This review paper explores the deep connection between statistical mechanics and analytic number theory by demonstrating how semiclassical methods, particularly the trace formula and periodic orbit theory, can be used to derive the asymptotic behavior and oscillatory patterns of integer partitions, including those involving distinct squares and prime numbers.
This paper introduces a robust, bounded, and sign-preserving Signed Multifractal Detrended Cross-Correlation Coefficient () that resolves numerical instabilities associated with negative fluctuation orders, enabling reliable analysis of scale- and amplitude-dependent cross-correlations in complex systems like financial markets and climate records.
Using density-matrix renormalization-group calculations, this study demonstrates that two static nonmagnetic vacancies in the critical -- transverse-field Ising chain behave as a one-parameter family of partially transmissive conformal defects, characterized by algebraic interaction decay, a -dependent transmission ratio, and a nearly constant boundary entropy.
This paper demonstrates that in an exactly solvable model of monitored quantum dynamics, non-perturbative logarithmic corrections arising in the physically relevant replica limit () eliminate the purifying phase observed at finite replica numbers, thereby ensuring that the purification time remains exponentially long in system size regardless of the measurement rate.
This paper proposes a data-centric training framework for quantum machine learning that gradually prioritizes informative samples, inspired by curriculum learning, to establish effective inductive bias and improve model optimization performance on tasks like quantum phase recognition.
This paper demonstrates that a one-dimensional stochastic system with a nonuniform temperature profile and open boundaries exhibits time irreversibility near the temperature interface through asymmetric transition probabilities and a hidden-gyration mechanism, despite the absence of net probability current in its nonequilibrium steady state.
This paper identifies a generic crossover between distinct "Enhance" and "Reduce" long-range ordered scaling regimes in continuous spin models with power-law interactions, pinpointing the transition points for both XY and Heisenberg models in one and two dimensions and characterizing their critical exponents through finite-size scaling analysis.