2356 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 demonstrates that the "magic" (nonstabilizerness) of equilibrium pure states in chaotic quantum many-body systems is a universal thermodynamic property determined solely by temperature and captured by the thermal Scrooge ensemble, independent of microscopic details or initial conditions.
This paper proposes that a second law of quantum complexity, which dictates that complexity grows toward an exponential saturation value in equilibrium but can decrease out of equilibrium, is derivable from a quasistatic information quantization rule linked to the principle of least action and minimum entropy production.
This paper proposes a physical response-and-memory model for training dynamics that explains the efficacy of the Muon optimizer and introduces the Bi-Maxwell optimizer, which utilizes a dual-timescale memory kernel to achieve faster convergence on large language model benchmarks.
This study demonstrates through Monte Carlo simulations and field theory that self-avoiding walks on one-dimensional long-range random ring graphs (Sparse-SAW) belong to the same universality class as clean superdiffusive Lévy self-avoiding walks, as the long-range kinetic operator dominates over the induced disorder under coarse-graining.
The paper introduces EquiNET, a method that leverages repeated trajectory fluctuations to accurately estimate free-energy differences in high-dissipation regimes where conventional fluctuation theorems fail due to the statistical rarity of negative entropy production events.
This paper develops an exact Doi-Peliti field theory for run-and-tumble particles with arbitrary non-uniform reorientation distributions, revealing that their spatial dynamics are governed by the first Fourier modes of the tumble distribution and can be formally mapped to chiral active Brownian particles.
This paper determines the aging phase diagram and exact asymptotic energies of mixed spherical -spin glasses quenched to zero temperature, revealing complex transitions between aging states with varying effective temperatures and identifying conditions under which gradient descent relaxation either reaches or remains above the algorithmic energy lower bound.
This paper demonstrates that resonant inelastic x-ray scattering (RIXS) selection rules act as an operator filter in spin- Heisenberg antiferromagnetic chains, revealing distinct quantum wake dynamics with slower dominant velocities compared to conventional neutron scattering, thereby providing complementary experimental pathways, access to quantum Fisher information, and benchmarks for quantum simulations.
This paper presents lecture notes from a 2025 summer school that introduce random quantum circuits as simple models for generic many-body systems, outlining their motivation rooted in random matrix theory and sketching calculations of key physical quantities such as operator spreading, entanglement dynamics, and spectral correlations.
This paper presents analytical results for the time-dependent and asymptotic degree distributions of networks evolving under a balance of random node addition and deletion, revealing that while growing networks converge to a steady-state distribution with a Poisson-like tail, contracting networks exhibit distinct convergence behaviors depending on the specific rate of contraction relative to the network's eventual disappearance.