2439 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 explores the application of quantum-inspired tensor network methods to enhance the efficiency of classical image processing and optical simulations, such as wave-front propagation and image formation.
This paper establishes that the robustness of self-organized systems is fundamentally limited by their information-carrying capacity, demonstrating that while short-range systems face bounds similar to quantum area laws, long-range correlations and global constraints can bypass these limits to enable more stable pattern formation.
By utilizing a "trampoline mechanism" where short-term synaptic plasticity dynamically lowers the energy landscape around recently visited patterns, the study demonstrates that rapid synaptic changes can trap transient dynamics and enable the recall of stored memories that would otherwise be lost in a standard Hopfield network.
This study investigates pattern formation in two-dimensional coupled Chialvo maps, demonstrating that different nearest-neighbor couplings produce distinct ringlike or spiral structures whose complex evolutionary dynamics can be quantified through the persistence of a velocity gradient tensor analogue.
This study investigates the antiferromagnetic Ising model on a kagome lattice with ferromagnetic next-nearest-neighbor interactions, identifying two Berezinskii-Kosterlitz-Thouless transitions and a three-phase diagram that exhibits six-state clock universality.
This paper develops a variational method to construct a second-quantized Hamiltonian for interacting surface systems with higher-form symmetries, deriving a functional Schrödinger equation that describes both gapless -form fields and massive topological phases with anyonic excitations.
This paper investigates the stationary density profiles and phase transitions of a Totally Asymmetric Simple Exclusion Process (TASEP) integrated with Langmuir kinetics and connected to particle reservoirs at both ends, revealing that this finite-resource model produces phase diagrams significantly different from—and in some ways more complex than—the standard open TASEP with Langmuir kinetics.
This paper presents a Reinforcement Learning framework for adaptive homing in continuous 2D environments, demonstrating that agents can optimize navigation through a balance of goal-directed correction and stochastic reorientation, with performance improving through both optimal noise levels and collective multi-agent interactions.
This paper demonstrates that uphill transport—where particle flow moves against the concentration gradient—emerges naturally from multispecies exclusion processes and provides a theoretical bridge between microscopic particle models and continuum descriptions like the Poisson-Nernst-Planck model, highlighting its potential significance in nanoscale and membrane-based technologies.
This study demonstrates that negative energetic elasticity is a fundamental and universal property of polymer chains, arising from effective soft-repulsive interactions and governed by a common scaling exponent across random, self-avoiding, and neighbor-avoiding walk crossovers.