Statistical physics, thermodynamics and nonlinear dynamics articles within Nature Communications

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  • Article
    | Open Access

    Whether stick-slip instabilities can give rise to avalanches of slip lengths is an outstanding issue in solid friction. Here, the authors demonstrated that there indeed exists a critical regime in which stick-slip friction can be described by a common set of statistical laws of avalanche dynamics.

    • Caishan Yan
    • , Hsuan-Yi Chen
    •  & Penger Tong

  • Article
    | Open Access

    What is the physical limit on entropy production in a suspension of active microswimmers? In answer to this question, the authors derive a general theorem that provides an exact lower bound on the total, external and internal dissipation by a microswimmer and apply it to optimize swimmer shapes.

    • Abdallah Daddi-Moussa-Ider
    • , Ramin Golestanian
    •  & Andrej Vilfan

  • Article
    | Open Access

    Many-body localized systems are believed to reach a stationary state without thermalizing. By using analytical and numerical calculations, the authors construct simple initial states for a typical MBL model, which neither equilibrate nor thermalize, similar to non-ergodic behavior in many-body scarred systems.

    • Henrik Wilming
    • , Tobias J. Osborne
    •  & Christoph Karrasch

  • Article
    | Open Access

    Mechanical properties of materials are governed by dislocations, yet it remains a challenge to resolve their evolution on the atomic scale. Svetlizky et al. use colloidal crystals to investigate, in three dimensions, how dislocations enable plastic relaxation and the formation of networks.

    • Ilya Svetlizky
    • , Seongsoo Kim
    •  & Frans Spaepen

  • Article
    | Open Access

    In nonlinear tracking control, relevant to robotic applications, the knowledge on the system model may be not available and there is current need in model-free approaches to track a desired trajectory, regular or chaotic. The authors introduce here a framework that employs machine learning to control a two-arm robotic manipulator using only partially observed states.

    • Zheng-Meng Zhai
    • , Mohammadamin Moradi
    •  & Ying-Cheng Lai

  • Article
    | Open Access

    Artificial spin ice systems have been used to simulate a variety of phenomena including phase transitions. Here, the authors expand the scope of applications to encompass non-ergodic dynamics, by reporting real-space imaging of ergodicity transitions in a vortex-frustrated artificial spin ice.

    • Michael Saccone
    • , Francesco Caravelli
    •  & Alan Farhan

  • Article
    | Open Access

    Personal communication networks through mobile phones and online platforms can be characterized by patterns of tie strengths. The authors propose a model to explain driving mechanisms of emerging tie strength heterogeneity in social networks, observing similarity of patterns across various datasets.

    • Gerardo Iñiguez
    • , Sara Heydari
    •  & Jari Saramäki

  • Article
    | Open Access

    Conservation laws are crucial for analyzing and modeling nonlinear dynamical systems; however, identification of conserved quantities is often quite challenging. The authors propose here a geometric approach to discovering conservation laws directly from trajectory data that does not require an explicit dynamical model of the system or detailed time information.

    • Peter Y. Lu
    • , Rumen Dangovski
    •  & Marin Soljačić

  • Article
    | Open Access

    Understanding glass transition would rely on the knowledge of the structural ordering upon slow cooling in the absence of crystallization or phase separation. The authors identify exotic compositional order, not accompanied by any thermodynamic signature, directly impacts the structural relaxation dynamics.

    • Hua Tong
    •  & Hajime Tanaka

  • Article
    | Open Access

    It is generally accepted that non-equilibrium conditions would have been necessary for the formation of primitive metabolic structures, but the focus has mostly been on externally imposed non-equilibrium conditions. Here, the authors show that catalytically active particles like enzymes participating in a metabolic cycle can spontaneously self-organize into dynamically structured condensates composed of active mixtures, by employing non-reciprocal interactions.

    • Vincent Ouazan-Reboul
    • , Jaime Agudo-Canalejo
    •  & Ramin Golestanian

  • Article
    | Open Access

    Rare quantum tunneling two-level systems are known to govern the glass physics at low temperatures, but it remains challenging to detect them in simulations. Ciarella et al. show a machine learning approach to efficiently identify the structural defects, allowing to predict the quantum splitting.

    • Simone Ciarella
    • , Dmytro Khomenko
    •  & Francesco Zamponi

  • Article
    | Open Access

    The mechanical forces exerted by active fluids may provide an effective way of transporting microscopic objects, but the details remain elusive. Using space modulated activity, Pellicciotta et al. generate active pressure gradients capable of transporting passive particles in controlled directions.

    • Nicola Pellicciotta
    • , Matteo Paoluzzi
    •  & Roberto Di Leonardo

  • Article
    | Open Access

    Recent experiments on the dynamical charge response of strange metals reveal unusual features such as momentum-independent continuum of excitations and unconventional plasmon decay. Here the authors present a phenomenological theory based on the analogy to classical fluids near a jamming-like transition.

    • Stephen J. Thornton
    • , Danilo B. Liarte
    •  & Debanjan Chowdhury

  • Article
    | Open Access

    Many-body localization is an important example of non-ergodic behaviour, however the conditions for its existence and stability are not fully established. Kloss et al establish theoretically and numerically the absence of many-body localization in a broad class of spin models respecting certain symmetries.

    • Benedikt Kloss
    • , Jad C. Halimeh
    •  & Yevgeny Bar Lev

  • Article
    | Open Access

    The Sherrington-Kirkpatrick model is a paradigmatic model in the field of complex disordered systems such as spin glasses and neural networks. Here the authors study the stochastic thermodynamics of an asymmetric version of the model by using a path integral method and provide exact solutions for the entropy production.

    • Miguel Aguilera
    • , Masanao Igarashi
    •  & Hideaki Shimazaki

  • Article
    | Open Access

    Increase of friction between two solid surfaces in stationary contact over time, known as frictional aging, has been widely observed. Farain and Bonn show that, regardless of surface roughness or degree of compression, the normalized stress relaxation of microcontacts is the same as that of bulk material.

    • Kasra Farain
    •  & Daniel Bonn

  • Article
    | Open Access

    Achieving shape assembly behaviour in robot swarms with adaptability and efficiency is challenging. Here, Sun et. al. propose a strategy based on an adapted mean-shift algorithm, thus realizing complex shape assembly tasks such as shape regeneration, cargo transportation, and environment exploration.

    • Guibin Sun
    • , Rui Zhou
    •  & Shiyu Zhao

  • Article
    | Open Access

    Out-of-time-ordered correlators of local operators can quantify information scrambling in quantum many-body systems, but they are not easily accessible in experiments. Here the authors show that their global versions can be used for the same purpose and has been measured in nuclear magnetic resonance experiments.

    • Tianci Zhou
    •  & Brian Swingle

  • Article
    | Open Access

    Topological transport in thermal diffusion is governed by physical principles that are distinct from those encountered in solid-state or photonic topological systems. Here, the authors demonstrate an experimental strategy for engineering topological thermal phases with bulk, edge and corner modes.

    • Guoqiang Xu
    • , Xue Zhou
    •  & Cheng-Wei Qiu

  • Article
    | Open Access

    Understanding of diffusive and spreading processes in networks remains challenging when dynamics of the network is complex. The authors propose a quantity to reflect the potential of a network node to diffuse information, that may serve to develop interventions for improved network efficiency.

    • Tiago A. Schieber
    • , Laura C. Carpi
    •  & Martín G. Ravetti

  • Article
    | Open Access

    Increasing the speed of magnetization switching is an obvious pathway to improve spintronic device performance. However, very fast magnetization switching is accompanied by instabilities. Here, Gidding et al study these instabilities using optical pumping, and show that instability generated spin-waves can achieve a high enough amplitude to drive switching of the magnetization, with a distinctive coherent pattern.

    • M. Gidding
    • , T. Janssen
    •  & A. Kirilyuk

  • Article
    | Open Access

    Supercritical fluids have local density inhomogeneities caused by molecular clusters. Authors show that the molecular interactions of supercritical fluids, associated with localized clusters, obey complex network dynamics that can be represented by a hidden-variable network model.

    • Filip Simeski
    •  & Matthias Ihme

  • Article
    | Open Access

    State-of-the-art machine learning models in drug discovery fail to reliably predict the binding properties of poorly annotated proteins and small molecules. Here, the authors present AI-Bind, a machine learning pipeline to improve generalizability and interpretability of binding predictions.

    • Ayan Chatterjee
    • , Robin Walters
    •  & Giulia Menichetti

  • Article
    | Open Access

    Authors model programmable photonic circuits targeting universal unitaries and verify that a type of unit rotation operator has a heavy-tailed distribution. They suggest hardware pruning for random unitary and present design strategies for high fidelity and energy efficiency in large-scale quantum computations and photonic deep learning accelerators.

    • Sunkyu Yu
    •  & Namkyoo Park

  • Article
    | Open Access

    Visualization of large complex networks is challenging with limitations for the network size and depicting specific structures. The authors propose a Graph Neural Network based algorithm with improved speed and the quality of graph layouts, which allows for fast and informative visualization of large networks.

    • Csaba Both
    • , Nima Dehmamy
    •  & Albert-László Barabási

  • Article
    | Open Access

    Efficient spatial targeting of interventions could reduce the spread of infections in transportation hubs. Here, the authors assess the optimal locations to target in Heathrow airport using disease transmission models informed by a contact network based on anonymised location data from 200,000 individuals.

    • Mattia Mazzoli
    • , Riccardo Gallotti
    •  & José J. Ramasco

  • Article
    | Open Access

    Triadic interactions are higher-order interactions relevant to many real complex systems. The authors develop a percolation theory for networks with triadic interactions and identify basic mechanisms for observing dynamical changes of the giant component such as the ones occurring in neuronal and climate networks.

    • Hanlin Sun
    • , Filippo Radicchi
    •  & Ginestra Bianconi

  • Article
    | Open Access

    Active field theories are powerful tools to explain phenomena such as motility-induced phase separation. The authors report an active analogue to the quantum mechanics tunneling effect, showing similarity to the Schrödinger equation, by introducing an extended model applicable to active particles with inertia.

    • Michael te Vrugt
    • , Tobias Frohoff-Hülsmann
    •  & Raphael Wittkowski

  • Article
    | Open Access

    Living things rely on extremely sensitive molecular circuits. Here, authors uncover a universal structural limit on kinetic scheme sensitivity, with implications for gene regulation & the functions of condensates.

    • Jeremy A. Owen
    •  & Jordan M. Horowitz

  • Article
    | Open Access

    A kagome lattice spin-ice system is created with the superconducting qubits of a quantum annealer, and shown to exhibit a field-induced kinetic crossover between spin-liquid phases. Specifically, kinetics within both the Ice-I phase and the unconventional field-induced Ice-II phase are  presented.

    • Alejandro Lopez-Bezanilla
    • , Jack Raymond
    •  & Andrew D. King

  • Article
    | Open Access

    Learning analytical models from noisy data remains challenging and depends essentially on the noise level. The authors analyze the transition of the model-learning problem from a low-noise phase to a phase where noise is too high for the underlying model to be learned by any method, and estimate upper bounds for the transition noise.

    • Oscar Fajardo-Fontiveros
    • , Ignasi Reichardt
    •  & Roger Guimerà

  • Article
    | Open Access

    Populations of swarming coupled oscillators with inhomogeneous natural frequencies and chirality are relevant for active matter systems and micro-robotics. The authors model and analyze a variety of their self-organized behaviors that mimic natural and artificial micro-scale collective systems.

    • Steven Ceron
    • , Kevin O’Keeffe
    •  & Kirstin Petersen

  • Article
    | Open Access

    In the quest to understand how deep neural networks work, identification of slow and fast variables is a desirable step. Inspired by tools from theoretical physics, the authors propose a simplified description of finite deep neural networks based on two matrix variables per layer and provide analytic predictions for feature learning effects.

    • Inbar Seroussi
    • , Gadi Naveh
    •  & Zohar Ringel

  • Article
    | Open Access

    Defect lines shaped as conic sections are common in smectic liquid crystals, where they manifest equidistance of molecular layers curled in space. Here authors present hyperbolas and parabolas as domain walls in ferroelectric nematics, which are shaped so to avoid being electrically charged.

    • Priyanka Kumari
    • , Bijaya Basnet
    •  & Oleg D. Lavrentovich

  • Article
    | Open Access

    Finding the ground states of spin glasses relevant for disordered magnets and many other physical systems is computationally challenging. The authors propose here a deep reinforcement learning framework for calculating the ground states, which can be trained on small-scale spin glass instances and then applied to arbitrarily large ones.

    • Changjun Fan
    • , Mutian Shen
    •  & Yang-Yu Liu

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