Computational science articles within Nature Communications

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  • Article
    | Open Access

    Series of machine learning models, relevant for tasks in biology, medicine, and finance, usually involve complex feature attribution techniques. The authors introduce a tractable method to compute local feature attributions for a series of machine learning models inspired by connections to the Shapley value.

    • Hugh Chen
    • , Scott M. Lundberg
    •  & Su-In Lee

  • Article
    | Open Access

    Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Here the authors apply some of the latest advances in natural language processing, generative Transformers, to train ProtGPT2, a language model that explores unseen regions of the protein space while designing proteins with nature-like properties.

    • Noelia Ferruz
    • , Steffen Schmidt
    •  & Birte Höcker

  • Article
    | Open Access

    Designing energy-efficient computing solution for the implementation of AI algorithms in edge devices remains a challenge. Yang et al. proposes a decentralized brain-inspired computing method enabling multiple edge devices to collaboratively train a global model without a fixed central coordinator.

    • Helin Yang
    • , Kwok-Yan Lam
    •  & H. Vincent Poor

  • Article
    | Open Access

    As mass quarantines, absences due to sickness, or other shocks thin out patient-physician networks, the system might be pushed to a tipping point where it loses its ability to deliver care. Here, the authors propose a data-driven framework to quantify regional resilience to such shocks via an agent-based model.

    • Michaela Kaleta
    • , Jana Lasser
    •  & Peter Klimek

  • Article
    | Open Access

    A critical task in spatial transcriptomics analysis is to understand inherently spatial relationships among cells. Here, the authors present a deep learning framework to integrate spatial and transcriptional information, spatially extending pseudotime and revealing spatiotemporal organization of cells.

    • Honglei Ren
    • , Benjamin L. Walker
    •  & Qing Nie

  • Article
    | Open Access

    Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians.

    • Sergey P. Primakov
    • , Abdalla Ibrahim
    •  & Philippe Lambin

  • Article
    | Open Access

    Machine learning tools allow to extract dynamical systems from data, however this problem remains challenging for networks and systems of interacting agents. The authors introduce an approach to learn a predictive model for the dynamics of coupled agents in the form of partial differential equations using emergent spatial embeddings.

    • Felix P. Kemeth
    • , Tom Bertalan
    •  & Ioannis G. Kevrekidis

  • Article
    | Open Access

    Scattering of electrons from defects and boundaries in mesoscopic samples is encoded in quantum interference patterns of magneto-conductance, but these patterns are difficult to interpret. Here the authors use machine learning to reconstruct electron wavefunction intensities and sample geometry from magneto-conductance data.

    • Shunsuke Daimon
    • , Kakeru Tsunekawa
    •  & Eiji Saitoh

  • Article
    | Open Access

    Understanding the transport of the particles and fuel in the fusion plasma is fundamentally important. Here the authors report a cross-link interaction between electron- and ion-scale turbulences in plasma in terms of trapped electron mode and electron temperature gradient modes and their implication to fusion plasma.

    • Shinya Maeyama
    • , Tomo-Hiko Watanabe
    •  & Akihiro Ishizawa

  • Article
    | Open Access

    Artificial intelligence approaches inspired by human cognitive function have usually single learned ability. The authors propose a multimodal foundation model that demonstrates the cross-domain learning and adaptation for broad range of downstream cognitive tasks.

    • Nanyi Fei
    • , Zhiwu Lu
    •  & Ji-Rong Wen

  • Article
    | Open Access

    The atomic structure of heterogeneous catalysts is usually a blackbox. Here the authors demonstrate large-scale machine learning atomic simulations help to resolve the catalyst structure and reaction mechanism of encapsulated PtSnOx clusters in zeolite that feature a mortise-and-tenon joinery structure and the superior activity towards propane dehydrogenation.

    • Sicong Ma
    •  & Zhi-Pan Liu

  • Article
    | Open Access

    How regional anatomy shapes function is not well understood. Here, the authors evaluate the performance of 40 communication models in predicting functional connectivity, and find regional heterogeneity in terms of fit and optimal model, and that regional coupling varies over the human lifespan.

    • Farnaz Zamani Esfahlani
    • , Joshua Faskowitz
    •  & Richard F. Betzel

  • Article
    | Open Access

    Rydberg atoms are sensitive to microwave signals and hence can be used to detect them. Here the authors demonstrate a Rydberg receiver enhanced by deep learning, Rydberg atoms acting as antennae, to receive, extract, and decode the multi-frequency microwave signal effectively.

    • Zong-Kai Liu
    • , Li-Hua Zhang
    •  & Bao-Sen Shi

  • Article
    | Open Access

    Tasks involving continual learning and adaptation to real-time scenarios remain challenging for artificial neural networks in contrast to real brain. The authors propose here a brain-inspired optimizer based on mechanisms of synaptic integration and strength regulation for improved performance of both artificial and spiking neural networks.

    • Giorgia Dellaferrera
    • , Stanisław Woźniak
    •  & Evangelos Eleftheriou

  • Article
    | Open Access

    Reservoir computing has demonstrated high-level performance, however efficient hardware implementations demand an architecture with minimum system complexity. The authors propose a rotating neuron-based architecture for physically implementing all-analog resource efficient reservoir computing system.

    • Xiangpeng Liang
    • , Yanan Zhong
    •  & Huaqiang Wu

  • Article
    | Open Access

    Simulations of turbulent flows are relevant for aerodynamic and weather modeling, however challenging to capture flow dynamics in the near wall region. To solve this problem, the authors propose a multi-agent reinforcement learning approach to discover wall models for large-eddy simulations.

    • H. Jane Bae
    •  & Petros Koumoutsakos

  • Article
    | Open Access

    The authors use an agent-based model to investigate the potential of reactive vaccination strategies for COVID-19 outbreak mitigation. They find that distributing vaccines in schools and workplaces where cases are detected is more impactful than non-reactive strategies in a wide range of epidemic scenarios.

    • Benjamin Faucher
    • , Rania Assab
    •  & Chiara Poletto

  • Perspective
    | Open Access

    A grand challenge in robotics is realising intelligent agents capable of autonomous interaction with the environment. In this Perspective, the authors discuss the potential, challenges and future direction of research aimed at demonstrating embodied intelligent robotics via neuromorphic technology.

    • Chiara Bartolozzi
    • , Giacomo Indiveri
    •  & Elisa Donati

  • Article
    | Open Access

    Large amounts of interaction data are collected by messaging apps, mobile phone carriers, and social media. Creţu et al. propose a behavioral profiling attack model and show that the stability of people’s interaction networks over time can allow individuals to be re-identified in interaction datasets.

    • Ana-Maria Creţu
    • , Federico Monti
    •  & Yves-Alexandre de Montjoye

  • Article
    | Open Access

    Topology optimization, relevant for materials design and engineering, requires solving of challenging high-dimensional problems. The authors introduce a self-directed online learning approach, as embedding of deep learning in optimization methods, that accelerates the training and optimization processes.

    • Changyu Deng
    • , Yizhou Wang
    •  & Wei Lu

  • Article
    | Open Access

    Optimal control of complex dynamical systems can be challenging due to cost constraints and analytical intractability. The authors propose a machine-learning-based control framework able to learn control signals and force complex high-dimensional dynamical systems towards a desired target state.

    • Lucas Böttcher
    • , Nino Antulov-Fantulin
    •  & Thomas Asikis

  • Article
    | Open Access

    Navigation and trajectory planning in environments with background flow, relevant for robotics, are challenging provided information only on local surrounding. The authors propose a reinforcement learning approach for time-efficient navigation of a swimmer through unsteady two-dimensional flows.

    • Peter Gunnarson
    • , Ioannis Mandralis
    •  & John O. Dabiri

  • Article
    | Open Access

    The state of Victoria, Australia experienced a substantial second wave of COVID-19 but brought it under control with strict non-pharmaceutical interventions. Here, the authors model the second wave in Victoria to estimate the impacts of the different interventions.

    • James M. Trauer
    • , Michael J. Lydeamore
    •  & Romain Ragonnet

  • Article
    | Open Access

    Recovery of underlying governing laws or equations describing the evolution of complex systems from data can be challenging if dataset is damaged or incomplete. The authors propose a learning approach which allows to discover governing partial differential equations from scarce and noisy data.

    • Zhao Chen
    • , Yang Liu
    •  & Hao Sun

  • Article
    | Open Access

    The authors propose a new framework, deep evolutionary reinforcement learning, evolves agents with diverse morphologies to learn hard locomotion and manipulation tasks in complex environments, and reveals insights into relations between environmental physics, embodied intelligence, and the evolution of rapid learning.

    • Agrim Gupta
    • , Silvio Savarese
    •  & Li Fei-Fei

  • Article
    | Open Access

    Reservoir computers are artificial neural networks that can be trained on small data sets, but require large random matrices and numerous metaparameters. The authors propose an improved reservoir computer that overcomes these limitations and shows advantageous performance for complex forecasting tasks

    • Daniel J. Gauthier
    • , Erik Bollt
    •  & Wendson A. S. Barbosa

  • Article
    | Open Access

    Image-based simulation for obtaining physical quantities is limited by the uncertainty in the underlying image segmentation. Here, the authors introduce a workflow for efficiently quantifying segmentation uncertainty and creating uncertainty distributions of the resulting physics quantities.

    • Michael C. Krygier
    • , Tyler LaBonte
    •  & Scott A. Roberts

  • Article
    | Open Access

    Neural Networks are known to perform poorly outside of their training domain. Here the authors propose an inverse sampling strategy to train neural network potentials enabling to drive atomistic systems towards high-likelihood and high-uncertainty configurations without the need for molecular dynamics simulations.

    • Daniel Schwalbe-Koda
    • , Aik Rui Tan
    •  & Rafael Gómez-Bombarelli

  • Article
    | Open Access

    The analysis of networks and network processes can require low-dimensional representations, possible for specific structures only. The authors propose a geometric formalism which allows to unfold the mechanisms of dynamical processes propagation in various networks, relevant for control and community detection.

    • Adam Gosztolai
    •  & Alexis Arnaudon

  • Article
    | Open Access

    Quantitative methods to assess the quality of hPSC-derived organoids have not been developed. Here they present a prediction algorithm to assess the transcriptomic similarity between hPSC-derived organoids and the corresponding human target organs and perform validation on lung bud organoids, antral gastric organoids, and cardiomyocytes.

    • Mi-Ok Lee
    • , Su-gi Lee
    •  & Hyun-Soo Cho

  • Article
    | Open Access

    In-silico trials rely on virtual populations and interventions simulated using patient-specific models and may offer a solution to lower costs. Here, the authors present the flow diverter performance assessment in-silico trial, which models the treatment of intracranial aneurysms with a flow-diverting stent.

    • Ali Sarrami-Foroushani
    • , Toni Lassila
    •  & Alejandro F. Frangi

  • Article
    | Open Access

    Gravity waves are observed in Venus atmosphere, but their characteristics are not well-known. Here, the authors show spontaneous generation of gravity waves from the thermal tides in the Venus atmosphere as small-scale gravity waves are resolved in high-resolution general circulation model.

    • Norihiko Sugimoto
    • , Yukiko Fujisawa
    •  & Yoshi-Yuki Hayashi

  • Article
    | Open Access

    Generating new sensible molecular structures is a key problem in computer aided drug discovery. Here the authors propose a graph-based molecular generative model that outperforms previously proposed graph-based generative models of molecules and performs comparably to several SMILES-based models.

    • Omar Mahmood
    • , Elman Mansimov
    •  & Kyunghyun Cho

  • Article
    | Open Access

    In organic chemistry, synthetic routes for new molecules are often specified in terms of reacting molecules only. The current work reports an artificial intelligence model to predict the full sequence of experimental operations for an arbitrary chemical equation.

    • Alain C. Vaucher
    • , Philippe Schwaller
    •  & Teodoro Laino

  • Article
    | Open Access

    Transparent photodetectors based on graphene stacked vertically along the optical axis have shown promising potential for light field reconstruction. Here, the authors develop transparent photodetector arrays and implement a neural network for real-time 3D optical imaging and object tracking.

    • Dehui Zhang
    • , Zhen Xu
    •  & Theodore B. Norris

  • Article
    | Open Access

    The detection of the effects of spin symmetry in momentum distribution of an SU(N)-symmetric Fermi gas has remained challenging. Here, the authors use supervised machine learning to connect the spin multiplicity to thermodynamic quantities associated with different parts of the momentum distribution.

    • Entong Zhao
    • , Jeongwon Lee
    •  & Gyu-Boong Jo

  • Article
    | Open Access

    Machine learning algorithms offer new possibilities for automating reaction procedures. The present paper investigates automated reaction’s prediction with Molecular Transformer, the state-of-the-art model for reaction prediction, proposing a new debiased dataset for a realistic assessment of the model’s performance.

    • Dávid Péter Kovács
    • , William McCorkindale
    •  & Alpha A. Lee

  • Article
    | Open Access

    The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated for the aluminum case.

    • Justin S. Smith
    • , Benjamin Nebgen
    •  & Kipton Barros

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