Computational science articles within Nature Communications

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  • Article
    | Open Access

    Resource sharing over peer-to-peer technological networks is emerging as economically important, yet little is known about how people choose to share in this context. Here, the authors introduce a new game to model sharing, and test how players form sharing strategies depending on technological constraints.

    • Hirokazu Shirado
    • , George Iosifidis
    •  & Nicholas A. Christakis

  • Article
    | Open Access

    Designing mechanical metamaterials is challenging because of the large number of non-periodic constituent elements. Here, the authors develop an approach to design arbitrarily shaped metamaterials that is more computationally efficient by six orders of magnitude compared to other approaches.

    • Lucas A. Shaw
    • , Frederick Sun
    •  & Jonathan B. Hopkins

  • Article
    | Open Access

    Predicting plastic deformation in crystals remains challenging owing to the nonlinear nature of stochastic avalanches involved, which resemble the critical phenomena. Salmenjoki et al. use machine learning to predict plastic deformation and show that it works better for those under large strains.

    • Henri Salmenjoki
    • , Mikko J. Alava
    •  & Lasse Laurson

  • Article
    | Open Access

    Diversity is believed to raise effectiveness and performance but it contains many aspects. Here the authors studied the relationship between research impact and five classes of diversity and found that ethnic diversity had the strongest correlation with scientific impact.

    • Bedoor K. AlShebli
    • , Talal Rahwan
    •  & Wei Lee Woon

  • Article
    | Open Access

    It is now possible to predict what a chemical smells like based on its chemical structure, however to date, this has only been done for a small number of odor descriptors. Here, using natural-language semantic representations, the authors demonstrate prediction of a much wider range of descriptors.

    • E. Darío Gutiérrez
    • , Amit Dhurandhar
    •  & Guillermo A. Cecchi

  • Article
    | Open Access

    Continuous-time computation paradigm could represent a viable alternative to the standard digital one when dealing with certain classes of problems. Here, the authors propose a generalised version of a continuous-time solver and simulate its performances in solving MaxSAT and two-colour Ramsey problems.

    • Botond Molnár
    • , Ferenc Molnár
    •  & Mária Ercsey-Ravasz

  • Article
    | Open Access

    Accurate and actionable biomarkers that integrate diverse molecular, functional and clinical information hold great promise in precision medicine. Here, the authors develop SIMMS, a method for pathway-based cross-disease biomarker discovery.

    • Syed Haider
    • , Cindy Q. Yao
    •  & Paul C. Boutros

  • Article
    | Open Access

    Complex networks can be a useful tool to investigate problems in social science. Here the authors use game theory to establish a network model and then use a machine learning approach to characterize the role of nodes within a social network.

    • Yuan Yuan
    • , Ahmad Alabdulkareem
    •  & Alex ‘Sandy’ Pentland

  • Article
    | Open Access

    Solid-state nuclear magnetic resonance combined with quantum chemical shift predictions is limited by high computational cost. Here, the authors use machine learning based on local atomic environments to predict experimental chemical shifts in molecular solids with accuracy similar to density functional theory.

    • Federico M. Paruzzo
    • , Albert Hofstetter
    •  & Lyndon Emsley

  • Article
    | Open Access

    AI is used increasingly in medical diagnostics. Here, the authors present a deep learning model that masters medical knowledge, demonstrated by it having passed the written test of the 2017 National Medical Licensing Examination in China, and can provide help with clinical diagnosis based on electronic health care records.

    • Ji Wu
    • , Xien Liu
    •  & Ping Lv

  • Article
    | Open Access

    Dynamics in cold atomic ensembles involve complex many-body interactions that are hard to treat analytically. Here, the authors use machine learning to optimise the cooling and trapping of neutral atoms, showing an improvement in the resulting resonant optical depth compared to more traditional solutions.

    • A. D. Tranter
    • , H. J. Slatyer
    •  & G. T. Campbell

  • Article
    | Open Access

    With the rapid development of quantum computers, quantum machine learning approaches are emerging as powerful tools to perform electronic structure calculations. Here, the authors develop a quantum machine learning algorithm, which demonstrates significant improvements in solving quantum many-body problems.

    • Rongxin Xia
    •  & Sabre Kais

  • Article
    | Open Access

    Understanding the occurrence of sudden changes in plasma parameters is important for the operation of magnetically confined fusion devices. Here the authors use simulation to shed light on the formation of abrupt large-amplitude events and the associated redistribution of energetic ions in a tokamak.

    • Andreas Bierwage
    • , Kouji Shinohara
    •  & Masatoshi Yagi

  • Article
    | Open Access

    Despite advances in ENSO modeling, super El Niño events remain largely unpredictable. Hameed et al. postulate that ENSO-IOD interaction is crucial for super El Niño development and identify a self-limiting factor that constrains ENSO dynamics from generating these extreme events on their own.

    • Saji N. Hameed
    • , Dachao Jin
    •  & Vishnu Thilakan

  • Article
    | Open Access

    Memristive technology is a promising avenue towards realizing efficient non-von Neumann neuromorphic hardware. Boybat et al. proposes a multi-memristive synaptic architecture with a counter-based global arbitration scheme to address challenges associated with the non-ideal memristive device behavior.

    • Irem Boybat
    • , Manuel Le Gallo
    •  & Evangelos Eleftheriou

  • Article
    | Open Access

    How structure and function coevolve in developing brains is little understood. Here, the authors study a coupled model of network development and memory, and find that due to the feedback networks with some initial memory capacity evolve into heterogeneous structures with high memory performance.

    • Ana P. Millán
    • , J. J. Torres
    •  & J Marro

  • Article
    | Open Access

    Previous work decoding linguistic meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode meanings of semantically diverse new sentences with topics not encountered during training.

    • Francisco Pereira
    • , Bin Lou
    •  & Evelina Fedorenko

  • Article
    | Open Access

    Simplified neuron models, such as generalized leaky integrate-and-fire (GLIF) models, are extensively used in network modeling. Here the authors systematically generate and compare GLIF models of varying complexity for their ability to classify cell types in the Allen Cell Types Database and faithfully reproduce spike trains.

    • Corinne Teeter
    • , Ramakrishnan Iyer
    •  & Stefan Mihalas

  • Article
    | Open Access

    Different experimental and computational approaches can be used to study RNA structures. Here, the authors present a computational method for data-directed reconstruction of complex RNA structure landscapes, which predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data.

    • Hua Li
    •  & Sharon Aviran

  • Article
    | Open Access

    Certain physical problems such as the rupture of a thin sheet can be difficult to solve as computations breakdown at the point of rupture. Here the authors propose a regularization approach to overcome this breakdown which could help dealing with mathematical models that have finite time singularities.

    • Panayotis G. Kevrekidis
    • , Constantinos I. Siettos
    •  & Yannis G. Kevrekidis

  • Article
    | Open Access

    New computing paradigms, such as in-memory computing, are expected to overcome the limitations of conventional computing approaches. Sebastian et al. report a large-scale demonstration of computational phase change memory (PCM) by performing high-level computational primitives using one million PCM devices.

    • Abu Sebastian
    • , Tomas Tuma
    •  & Evangelos Eleftheriou

  • Article
    | Open Access

    While automated reaction systems typically work for the synthesis of pre-defined molecules, automated systems to discover reactivity are more challenging. Here the authors report an autonomous organic reaction search engine that allows discovery of the most reactive pathways in a multi-reagent, multistep reaction system.

    • Vincenza Dragone
    • , Victor Sans
    •  & Leroy Cronin

  • Article
    | Open Access

    The huge amount of data generated in fields like neuroscience or finance calls for effective strategies that mine data to reveal underlying dynamics. Here Brunton et al.develop a data-driven technique to analyze chaotic systems and predict their dynamics in terms of a forced linear model.

    • Steven L. Brunton
    • , Bingni W. Brunton
    •  & J. Nathan Kutz

  • Article
    | Open Access

    It is not clear how circadian biochemical cascades are encoded into neural electrical signals. Here, using a combination of electrophysiology and modelling approaches in mice, the authors show activation of glycogen synthase kinase 3 modulates neural activity in the suprachiasmatic nuclei via regulation of the persistent sodium current, INaP.

    • Jodi R. Paul
    • , Daniel DeWoskin
    •  & Karen L. Gamble

  • Article
    | Open Access

    The K-satisfability problem is a combinatorial discrete optimization problem, which for K=3 is NP-complete, and whose random formulation is of interest for understanding computational complexity. Here, the authors introduce the backtracking survey propagation algorithm for studying it for K=3 and K=4.

    • Raffaele Marino
    • , Giorgio Parisi
    •  & Federico Ricci-Tersenghi

  • Article
    | Open Access

    Overload failures propagate through hidden functional dependencies across networked systems. Here, the authors study the spatio-temporal propagation behaviour of cascading overload failures, and find that they spread radially from their origin with an approximately constant velocity.

    • Jichang Zhao
    • , Daqing Li
    •  & Shlomo Havlin

  • Article
    | Open Access

    Obtaining activation entropies and enthalpies of a reaction is important for distinguishing between alternative reaction mechanisms. Here the authors use computational methods to accurately obtain the enthalpic/entropic components of the activation free energy for hydrolytic deamination reactions.

    • Masoud Kazemi
    •  & Johan Åqvist

  • Article |

    A challenging problem is to identify the most central agents in interconnected multilayer networks. Here, De Domenico et al. present a mathematical framework to calculate centrality in such networks—versatility—and rank nodes accordingly.

    • Manlio De Domenico
    • , Albert Solé-Ribalta
    •  & Alex Arenas

  • Article
    | Open Access

    Machine learning systems use algorithms that can interpret data to make improved decisions. Hermans et al. develop a physical scheme for a computing system based on recurrent neural networks that physically implements the error backpropagation algorithm, thus performing its own training process.

    • Michiel Hermans
    • , Michaël Burm
    •  & Peter Bienstman

  • Article |

    The study of pattern formation in reaction–diffusion systems has been mainly restricted to symmetric (undirected) networks. Here, Asllani et al.identify a different pattern formation mechanism in a larger class of networks incorporating the possibility of unequal weights for transport along edges.

    • Malbor Asllani
    • , Joseph D. Challenger
    •  & Duccio Fanelli

  • Article |

    Many networks exhibit patterns of synchronized clusters, but the conditions under which this occurs are poorly understood. Pecora et al. develop an analytical approach based on computational group theory to predict the emergence and disappearance of synchrony among local clusters in complex networks.

    • Louis M. Pecora
    • , Francesco Sorrentino
    •  & Rajarshi Roy

  • Article
    | Open Access

    Reservoir computing uses computational techniques related to neural networks to perform certain computing tasks. Here, the authors implement a passive optical reservoir computing scheme integrated on a silicon chip, operating at speeds up to 12.5 Gbit s−1.

    • Kristof Vandoorne
    • , Pauline Mechet
    •  & Peter Bienstman

  • Article |

    Practical quantum computers will require protocols to carry out computation on encrypted data, just like their classical counterparts. Here, the authors present such a protocol that allows an untrusted server to implement universal quantum gates on encrypted qubits without learning about the inputs.

    • K. A. G. Fisher
    • , A. Broadbent
    •  & K. J. Resch

  • Article
    | Open Access

    Most new device concepts for random-access memory are based on inorganic spin filters, which need a permanent magnet to operate. Here, the authors exploit the chiral-induced spin selectivity effect in an organic spin filter to construct a novel type of memory device, which works without a permanent magnet.

    • Oren Ben Dor
    • , Shira Yochelis
    •  & Yossi Paltiel

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