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.

Inviscid scaling in the Kuramoto-Sivashinsky equation from functional renormalization group and direct numerical simulations

This paper demonstrates that the one-dimensional Kuramoto-Sivashinsky equation exhibits an intermediate scaling regime with dynamical exponent $z=1$ , belonging to the inviscid-Burgers universality class, which arises from the vanishing of effective viscosity between the large-scale KPZ and small-scale non-universal behaviors, as evidenced by both functional renormalization group analysis and direct numerical simulations.

Liubov Gosteva, Dipankar Roy, Nicolás Wschebor, Léonie Canet2026-05-25🌀 nlin

Multi-field Return Point Memory

This paper generalizes the concept of partial ordering to multi-field control systems, demonstrating that applying sequences of multiple fields to a zero-temperature Ising model enables precise return-point memory where the system returns to its exact previous microstate, thereby offering new insights into how physical systems can learn and be trained.

Nathaniel Croce, Hossein Salahshoor, D. Zeb Rocklin2026-05-25🔬 cond-mat

Orientable Surfactants on Thin Liquid Films: A Dynamic Density-Functional Theory Approach

This paper presents a dynamic density-functional theory approach to derive thermodynamically consistent thin-film equations for surfactant-laden liquid films that account for the polar, uniaxial shape of surfactant molecules, revealing a novel generalization of surface tension dependent on both surfactant concentration and polarization.

Toby Kay, Serafim Kalliadasis2026-05-25🔬 cond-mat

Phases of decodability in the surface code with unitary errors

This paper investigates the maximum likelihood decoding of the surface code under unitary errors by mapping it to a (1+1)D transfer matrix contraction, revealing a distinct phase where ferromagnetic order coexists with volume-law entanglement, rendering the encoded information theoretically retained but effectively undecodable.

Yimu Bao, Sajant Anand2026-05-22⚛️ quant-ph

Generalized CV Conjecture and Krylov Complexity in Two-Mode Hermitian Systems via Information Geometry

This paper extends the Complexity=Volume (CV) conjecture to two-mode Hermitian systems by demonstrating that the Krylov complexity of both closed and open quantum states exactly matches the volume of the Fubini-Study metric, thereby establishing a direct link between operator growth and information geometry.

Ke-Hong Zhai, Lei-Hua Liu, Hai-Qing Zhang2026-05-22⚛️ hep-th

Crosscap Quenches and Entanglement Evolution

This paper introduces a novel "crosscap quench" protocol to investigate the relaxation of highly structured thermal pure states into typical ones, deriving universal entanglement entropy features in conformal field theories and holographic models while validating these findings through numerical simulations of both integrable and nonintegrable quantum spin systems.

Zixia Wei, Yasushi Yoneta2026-05-22⚛️ hep-th

Macroscopic Particle Transport in Dissipative Long-Range Bosonic Systems

This paper establishes a generalized optimal transport theory for dissipative long-range bosonic systems, revealing that while one-body and multi-body losses fundamentally alter maximal transport speeds and distances, the presence of even minimal gain or decoherence-free subspaces can enable long-distance, perfect particle transport, with derived bounds on transport probability guiding future experimental protocols.

Hongchao Li, Cheng Shang, Tomotaka Kuwahara, Tan Van Vu2026-05-22🔢 math-ph

Su-Schrieffer-Heeger model driven by sequences of two unitaries: periodic, quasiperiodic, aperiodic, and random protocols

This paper investigates the topological and dynamical properties of the Su-Schrieffer-Heeger model driven by sequences of two unitaries under periodic, quasiperiodic, aperiodic, and random protocols, revealing discrepancies between end mode counts and winding numbers in periodic drives, and characterizing the distinct Loschmidt echo behaviors—ranging from long-lived oscillations to rapid decay—across different driving sequences.

Maitri Ganguli, Diptiman Sen2026-05-22🔬 cond-mat.mes-hall

Complexity of Quantum Trajectories

This paper introduces a data-driven framework based on intrinsic dimension to characterize the complexity of quantum trajectories in open systems, revealing how conservation laws and dynamical constraints like integrability or Hilbert-space fragmentation lead to pronounced reductions in complexity amidst typically chaotic Lindblad evolution.

Luca Lumia, Emanuele Tirrito, Mario Collura, Fabian H. L. Essler, Rosario Fazio2026-05-22⚛️ quant-ph

MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Well-Tempered Metadynamics

The paper introduces MetaDNS, a framework that integrates well-tempered metadynamics into discrete neural samplers to overcome mode collapse and enable efficient exploration of high-energy barriers for accurate free energy estimation in complex discrete distributions.

Xiaochen Du, Juno Nam, Jaemoo Choi, Wei Guo, Sathya Edamadaka, Junyi Sha, Elton Pan, Yongxin Chen, Molei Tao, Rafael Gómez-Bombarelli2026-05-22🔬 cond-mat

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