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 paper presents a non-phenomenological influence functional framework to model simultaneous fermionic and bosonic dissipation, revealing how electronic friction and solvent effects distinctly influence quantum vibrational relaxation and charge transfer dynamics in electrochemical systems.
Using time-domain terahertz emission spectroscopy, researchers observed a dynamic magneto-chiral instability in photoexcited tellurium, where an electric current parallel to a magnetic field amplifies electromagnetic waves, offering a promising mechanism for THz-wave amplification in chiral materials.
This paper establishes the mathematical foundations for deriving first-order constitutive equations of a relativistic charged gas by applying a generalized projection method within the Chapman-Enskog expansion, arguing that the trace-fixed particle frame yields a causal, stable, and strongly hyperbolic fluid theory with frame-independent transport coefficients.
This paper demonstrates that structured quantum noise, specifically engineered ancilla errors on IBM superconducting processors, can paradoxically stabilize discrete time crystals in a kicked Ising model by acting as spatiotemporal disorder that prevents thermalization, thereby enabling robust subharmonic oscillations that are absent in noiseless simulations.
This paper formalizes a classification of three distinct types of local parent Hamiltonians that share a given quantum state as an exact eigenstate, rigorously deriving the complete set of such Hamiltonians for the state to reveal its role as a Quantum Many-Body Scar and establishing general constraints for product and short-range-entangled states.
This study employs a machine learning-enhanced classical density functional theory to demonstrate that in confined azeotropic mixtures, adsorption selectivity vanishes at the bulk azeotropic composition due to specific bulk thermodynamic conditions (equal partial molar volumes and extremal compressibility) that create a corresponding "aneotrope" in the interfacial free energy, a phenomenon that persists even in the supercritical regime.
This paper derives exact analytical results showing that U(1) symmetry substantially suppresses non-stabilizerness (magic) in random states compared to the unconstrained case, and validates these predictions against chaotic many-body models, revealing that magic is more robust to charge density fluctuations than entanglement and that interaction locality significantly influences the agreement between theory and specific system eigenstates.
This paper introduces a numerical tensor-network method based on the process tensor to compute full counting statistics of charge transport in interacting one-dimensional quantum systems, successfully benchmarking the approach on the XXZ spin chain to recover known transport exponents and confirm the breakdown of Kardar-Parisi-Zhang universality in higher-order cumulants at the isotropic point.
This paper introduces a non-Hermitian causal generative model that produces statistically significant temporal correlations invisible to conventional spectral analysis, providing experimentally verified signatures of causal memory in open quantum systems.
This paper investigates the scaling behavior and distributional properties of the longest weakly increasing subsequences in discrete random walks with heavy-tailed increments, finding that the average length scales as for finite variance cases and as (with ) for infinite variance cases, while the overall distribution is well-approximated by a lognormal model.