1572 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 article provides an accessible, non-technical introduction to soft active matter physics for students and early-career researchers, explaining its fundamental differences from equilibrium systems and its relevance to biological processes through core concepts and everyday examples, while concluding with a discussion of experimental findings on organism migration.
This paper resolves Loschmidt's paradox by arguing that macroscopic irreversibility arises not from a breakdown of microscopic time-reversal symmetry, but from the dynamical inaccessibility of time-reversed microstates once chaotic evolution drives phase-space structures below the quantum resolution scale, thereby establishing entropy increase as a consequence of operational information loss rather than fundamental information destruction.
This paper establishes a duality between decoding toric codes under coherent errors and 1+1D monitored Majorana dynamics, demonstrating that the Altland-Zirnbauer symmetry class of the dual system dictates the universal structure of decodability phase transitions, which differ fundamentally between class DIII (involving entanglement scaling changes) and class D (involving topological phase changes), while revealing that square-lattice codes are more vulnerable to spatially varying coherent errors than uniform ones.
This paper demonstrates that Chain of Thought improves large language model performance by decomposing complex classification tasks into a tree structure of smaller problems, revealing that while deeper "thinking" reduces error up to an optimal depth, performance cannot be further improved beyond a specific threshold determined by the number of classes per step.
This paper utilizes nonequilibrium Green's functions to demonstrate that steady-state phonon heat currents across a coupled harmonic junction obey Fourier's law and exhibit direction-independent thermal conductance that peaks when phonon spectra match, though low-temperature exclusion of high-frequency modes can shift this maximum.
This paper derives a unified closed hydrodynamic theory for scalar active matter with spatially regulated motility, demonstrating that orientation dynamics are captured by an autocorrelation tensor and revealing a novel "anti-MIPS" phase separation phenomenon in active polymers where dense regions exhibit enhanced activity.
This paper presents a general multiscale perturbative framework for coarse-graining dry scalar active matter with motility regulation, which successfully predicts large-scale equilibrium regimes or particle currents across diverse models—including active polymers and systems with density-mediated interactions—without relying on specific microscopic orientational dynamics.
This paper demonstrates that p4m-symmetric Group Convolutional Neural Networks (GCNNs), optimized via directed loop sampling, accurately reproduce ground-state properties of the quantum dimer model across various lattice sizes and irreducible representations, thereby confirming a four-fold degenerate ground state for and effectively narrowing the regime of possible mixed or plaquette phases.
This paper investigates the stability of logical information in quantum stabilizer codes under coherent unitary errors, identifying a phase transition where exceeding a critical threshold causes the syndrome state to shift to a different logical state, thereby signaling the breakdown of efficient error correction and inducing effective unitary rotations in the logical space.
Using the functional renormalization group within the LPA' approximation, this study computes the critical exponents of three-dimensional magnets with strong dipole-dipole interactions, identifying the scale-invariant Aharony fixed point and demonstrating that its critical behavior yields exponents numerically similar to, yet distinct from, the Heisenberg universality class.