Statistical physics articles from across Nature Portfolio

Entanglement entropy is a crucial quantity in several research fields, yet its calculation in higher-dimensional systems remains challenging. Here the authors develop a quantum Monte Carlo-based algorithm to efficiently extract large-scale entanglement entropy data and apply it to quantum critical systems.

The lack of spontaneously broken continuous symmetries due to long wavelength density fluctuations was first established in 2D crystals and recently generalized to glass-forming liquids and amorphous solids. Here the authors extend this result to crystalline and glassy systems with active forces in 2D and even 3D.

A neural network approach grounded in transition-path theory is shown to uncover committor-consistent transition pathways, resolving competing mechanisms across dynamical regimes and advancing the modeling of rare events in biomolecular systems.

Mobility-targeted interventions are known to reduce airborne disease transmission, but their effects on vector-borne diseases are less clear. The authors integrate mobility data with vulnerability matrices to reveal that such interventions in Santiago de Cali, Colombia, may inadvertently heighten vector-borne disease risks, proposing strategies to mitigate both disease types simultaneously.

Stability analysis of complex dynamical systems is crucial across disciplines, impacting ecosystems and biological organs. Here, the authors introduce a data-driven method using weighted adjacency matrices to assess stability, capturing higher-order interactions and nonlinear feedback, with applications demonstrated on systems like the Nordic Power Grid, highlighting its broad applicability and robustness.

Bose-Einstein Condensation (BEC), a remarkable manifestation of quantum mechanics in systems with many particles, has over the past century reshaped our understanding of the fundamental phases of matter. The author explores its historical development, with emphasis on the diverse physical systems exhibiting BEC, and the broader implications to fundamental science and quantum technologies.

We introduce free-energy machine (FEM), an efficient and general method for solving combinatorial optimization problems. FEM combines free-energy minimization from statistical physics with gradient-based optimization techniques in machine learning and utilizes parallel computation, outperforming state-of-the-art algorithms and showcasing the synergy of merging statistical physics with machine learning.

David Zwicker discusses the delicate balance between entropy and enthalpy that determines whether fluids comprising many components mix or phase separate.

False vacuum decay is a process of fundamental importance in quantum field theory. Here, a 5,564-qubit quantum annealer is used to simulate the dynamics of false vacuum decay and observe the formation of bubbles of true vacuum. This approach could provide insight into the role of phase transitions in the early Universe.

A deep learning algorithm is presented to classify single-particle tracking trajectories into theoretical models of anomalous diffusion and detect if the trajectory is related to a model not originally found within the training dataset.

Amid growing urbanization, the 15-minute city model seeks to transform city living by ensuring that essential services are just a short walk away from city inhabitants. New research now quantitatively measures this urban ideal, revealing significant disparities in access across cities globally.