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 study characterizes the fluctuation-driven nonlinear dynamics of a nanomechanical Duffing resonator by demonstrating that increasing force noise induces a transition from harmonic to nonlinear behavior, marked by a reduction in effective thermal energy and a statistical crossover from Gaussian to platykurtic distributions.
This paper presents a framework that predicts the thermodynamics and phase behavior of complex fluids, such as colloid-polymer mixtures, directly from structural correlations measured at a single state point, thereby bypassing the need for explicit knowledge of underlying interaction potentials.
This paper introduces a partial-information and finite-resources quantum Maxwell's demon protocol that optimizes work extraction from state copies by balancing measurement and application resources, deriving a universal trade-off bound where optimal performance scales as and demonstrating that finite-copy work extraction is fundamentally a task-dependent inference problem best judged by thermodynamic efficiency rather than reconstruction fidelity alone.
This paper refines the phase diagram of the two-dimensional and Potts models with long-range quenched disorder by utilizing Fortuin-Kasteleyn cluster wrapping probabilities to map the transition between finite- and infinite-disorder fixed points, while simultaneously clarifying the origin of double-peak magnetic susceptibility structures and confirming the validity of hyper-scaling relations across all investigated regimes.
This study reveals that active star polymers exhibit a non-equilibrium phenomenon called "active embracement," where self-propelled arms overcome steric repulsion to autonomously collapse and capture surrounding particles, effectively functioning as microscopic tweezers for targeted cargo manipulation.
This paper demonstrates that in complex systems, the choice of data representation is a critical scientific variable determining predictive success, as trajectory-level structural representations outperform even higher-correlation scalar statistics by preserving the underlying dynamics relevant to prediction.
This paper demonstrates that time-delayed feedback in a confining potential can fundamentally alter thermal escape dynamics by destabilizing the well and amplifying noise into deterministic "slingshot" trajectories, thereby transforming the mean escape time from an exponential Arrhenius scaling to a logarithmic dependence on the barrier height.
This paper demonstrates that the anomalous density decay scaling in two-species pair annihilation, previously thought to be exclusive to hardcore exclusion systems, can also be achieved in models without hardcore exclusion by employing nonlinear asymmetric diffusion driven by microscopic pair interactions.
This paper introduces a Hamiltonian Active Brownian Particle (HABP) model that bridges equilibrium and non-equilibrium dynamics by coupling translational and rotational degrees of freedom to distinct heat baths, thereby recovering standard Active Brownian Particle behavior in specific limits and providing a foundational framework for studying activity-induced phenomena in soft matter.
This paper extends learning rules from real binary to complex-valued spins to enable robust grayscale pattern representation in oscillator networks, demonstrating a superior encoding method that outperforms standard Hebbian learning while maintaining local and incremental properties.