cond-mat.stat-mech 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.

Reconstruction of spin structures from topological charge distributions via generative neural network systems

This paper demonstrates that a physics-constrained Wasserstein generative adversarial network can successfully reconstruct microscopic spin configurations from macroscopic topological charge distributions in the 2D XY model, accurately reproducing key thermodynamic properties while revealing the method's limitations in capturing higher-order energy fluctuations and the added value of topological data analysis for characterizing critical behavior.

Kyra H. M. Klos, Jan Disselhoff, Michael Wand, Karin Everschor-Sitte, Friederike Schmid2026-05-04🔬 cond-mat

Entanglement capacity of complex networks from quantum walks

This paper introduces a "source-target" entanglement measure for discrete-time quantum walks on general complex networks, demonstrating that network connectivity imposes an upper bound on entanglement generation governed by graph matchings, where increased connectivity in random graphs paradoxically reduces attainable quantum correlations.

Pravy Prerana, Sascha Wald2026-05-04⚛️ quant-ph

Non-Hermitian pseudo mobility edge in a coupled chain system

This study reveals that coupling a non-Hermitian chain exhibiting skin localization with a delocalized chain induces a pseudo mobility edge in the complex energy plane that separates localized and extended states while enabling unidirectional transport, with the system's transition to trivial extended phases characterized by a quantized winding number under specific boundary conditions.

Sen Mu, Longwen Zhou, Linhu Li, Jiangbin Gong2026-05-01🔬 cond-mat.mes-hall

Quantum transport on Bethe lattices with non-Hermitian sources and a drain

This paper investigates quantum transport on finite-generation Bethe lattices with non-Hermitian sources and a drain, revealing that the current reaches a maximum at a zero mode—often an exceptional point in $\mathcal{PT}$ -symmetric systems—where only a limited number of eigenstates effectively penetrate from the periphery to the center, while the remaining states remain localized.

Naomichi Hatano, Hosho Katsura, Kohei Kawabata2026-05-01🔬 cond-mat.mes-hall

Structural Order Drives Diffusion in a Granular Packing

This study demonstrates that structural ordering, specifically crystallization and hexatic order, significantly enhances the diffusion length and governs macroscopic flow behavior in bidisperse granular silo flows, with pressure gradients further stabilizing this orientational order to increase transport properties with height.

David Luce, Adrien Gans, Sébastien Kiesgen de Richter, Nicolas Vandewalle2026-05-01🔬 cond-mat

Thermodynamics of the Fermi-Hubbard Model through Stochastic Calculus and Girsanov Transformation

This paper applies stochastic calculus and Girsanov transformations to the Fermi-Hubbard model to derive a factorization-independent representation of thermodynamic correlation functions, which analytically proves the antiferromagnetic nature of spin-spin correlations at half-filling and enables the approximation of ground state energies via ordinary differential equations.

Detlef Lehmann2026-05-01🔢 math-ph

Predicting parameters of a model cuprate superconductor using machine learning

This study demonstrates that a U-Net deep learning architecture can effectively solve the inverse problem of predicting cuprate superconductor Hamiltonian parameters from phase diagrams, achieving high accuracy and revealing physically interpretable patterns of parametric sensitivity.

V. A. Ulitko, D. N. Yasinskaya, S. A. Bezzubin, A. A. Koshelev, Y. D. Panov2026-05-01🔬 cond-mat

Renormalization group for spectral collapse in random matrices with power-law variance profiles

This paper proposes a renormalization group framework that utilizes a size-dependent normalization to collapse eigenvalue densities of random matrix ensembles with power-law variance profiles, deriving fixed-point equations and Beta functions to demonstrate spectral collapse across different system sizes.

Philipp Fleig2026-05-01🔬 cond-mat

Nonlinear Dynamical Friction from the Doppler-Shifted Equilibrium Memory Kernel

This paper establishes a computationally efficient statistical mechanics framework using the Generalized Langevin Equation and equilibrium memory kernels derived from the Fluctuation-Dissipation Theorem to accurately model non-Markovian friction and drag in non-equilibrium steady states, a theory validated by Particle-in-Cell simulations and shown to recover the standard Chandrasekhar formula in the Markovian limit.

N. R. Sree Harsha, Zhenyuan Yu, Chuang Ren, Virginia Billings, Michael Huang2026-05-01🔬 cond-mat

The Most Dispersed Subset of Random Points in $\mathbb{R}^d$

This paper analytically derives the full statistical properties of the maximally dispersed subset of $N$ random points in $\mathbb{R}^d$ using mean-field theory and the replica method, revealing that for large populations and rotationally symmetric distributions, the optimal subset comprises all points lying outside a self-consistently determined $d$ -dimensional ball.

Fabio Deelan Cunden, Noemi Cuppone, Giovanni Gramegna, Pierpaolo Vivo2026-05-01🔢 math-ph

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cond-mat.stat-mech