Computational science articles within Nature Communications

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  • Article
    | Open Access

    Oscillating neural networks promise ultralow power consumption and rapid computation for tackling complex optimization problems. Here, the authors demonstrate VO2 oscillators to solve NP-complete problems with projected power consumption of 13 µW/oscillator.

    • Olivier Maher
    • , Manuel Jiménez
    •  & Siegfried Karg

  • Article
    | Open Access

    Detection of radiation is important for environmental health and safety. Here the authors demonstrate a method for radiation detection and mapping in 2D using minimum number of detectors and inter-pixel padding to increase the contrast between pixels.

    • Ryotaro Okabe
    • , Shangjie Xue
    •  & Mingda Li

  • Article
    | Open Access

    For reservoir computing, improving prediction accuracy while maintaining low computing complexity remains a challenge. Inspired by the Granger causality, Li et al. design a data-driven and model-free framework by integrating the inference process and the inferred results on high-order structures.

    • Xin Li
    • , Qunxi Zhu
    •  & Wei Lin

  • Article
    | Open Access

    In modern football games, data-driven analysis serves as a key driver in determining tactics. Wang, Veličković, Hennes et al. develop a geometric deep learning algorithm, named TacticAI, to solve high-dimensional learning tasks over corner kicks and suggest tactics favoured over existing ones 90% of the time.

    • Zhe Wang
    • , Petar Veličković
    •  & Karl Tuyls

  • Article
    | Open Access

    Understanding machine learning models’ ability to extrapolate from training data to unseen data - known as generalisation - has recently undergone a paradigm shift, while a similar understanding for their quantum counterparts is still missing. Here, the authors show that uniform generalization bounds pessimistically estimate the performance of quantum machine learning models.

    • Elies Gil-Fuster
    • , Jens Eisert
    •  & Carlos Bravo-Prieto

  • Article
    | Open Access

    Forecasting the future behaviors based on observed data remains a challenging task especially for large nonlinear systems. The authors propose a data-driven approach combining manifold learning and delay embeddings for prediction of dynamics for all components in high-dimensional systems.

    • Tao Wu
    • , Xiangyun Gao
    •  & Jürgen Kurths

  • Article
    | Open Access

    The SARS-CoV-2 Alpha variant of concern emerged in the UK in late 2020 but spread internationally before it was detected. Here, the authors reconstruct the dynamics of dissemination of this variant out of the UK by combining extent of genomic sequencing, travel volume, and local epidemic dynamics in a Bayesian model.

    • Benjamin Faucher
    • , Chiara E. Sabbatini
    •  & Chiara Poletto

  • Article
    | Open Access

    Discovery of 2D materials with useful electronic properties is challenging. Here, the authors use DFT to design a stable semiconducting 2D carbon allotrope for optoelectronic applications that has light charge carriers and unusual secondary bandgap.

    • Zhenzhe Zhang
    • , Hanh D. M. Pham
    •  & Rustam Z. Khaliullin

  • Article
    | Open Access

    Data drift is the systematic change in the underlying distribution of input features in prediction models, and can cause deterioration in model performance. Here, the authors highlight the importance of detecting data drift in clinical settings and evaluate methods for detecting drift in medical image data.

    • Ali Kore
    • , Elyar Abbasi Bavil
    •  & Mohamed Abdalla

  • Article
    | Open Access

    The effects of data leakage on predictive models in neuroimaging studies are not well understood. Here, the authors show that data leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have more minor effects.

    • Matthew Rosenblatt
    • , Link Tejavibulya
    •  & Dustin Scheinost

  • Article
    | Open Access

    Predicting the evolution of dynamical systems remains challenging, requiring high computational effort or effective reduction of the system into a low-dimensional space. Here, the authors present a data-driven approach for predicting the evolution of systems exhibiting spatiotemporal dynamics in response to external input signals.

    • Francesco Regazzoni
    • , Stefano Pagani
    •  & Alfio Quarteroni

  • Article
    | Open Access

    Link prediction in temporal networks is relevant for many real-world systems, however, current approaches are usually characterized by high computational costs. The authors propose a temporal link prediction framework based on the sequential stacking of static network features, for improved computational speed, appropriate for temporal networks with completely unobserved or partially observed target layers.

    • Xie He
    • , Amir Ghasemian
    •  & Peter J. Mucha

  • Article
    | Open Access

    Detecting hydrogen gas in humid air is an unresolved challenge of significant importance for the safe implementation of hydrogen (energy) technologies. Here, authors demonstrate how the use of neural networks enables the sensing of hydrogen in highly humid air with a detection limit of 100 ppm.

    • David Tomeček
    • , Henrik Klein Moberg
    •  & Christoph Langhammer

  • Article
    | Open Access

    Analysis of capacitive behavior of electrode materials used in batteries and pseudocapacitors is challenging. Here, authors report an electrochemical signal analysis method available as an online tool to classify the charge storage behavior of a material as battery-like or a pseudocapacitor-like.

    • Siraprapha Deebansok
    • , Jie Deng
    •  & Olivier Fontaine

  • Article
    | Open Access

    Encoding and downsampling images is key for visual prostheses. Here, the authors show that an actor-model framework using the inherent computation of the retinal network yields better performance in downsampling images compared to learning-free methods.

    • Franklin Leong
    • , Babak Rahmani
    •  & Diego Ghezzi

  • Article
    | Open Access

    Network structures can be examined at different scales, and subnetworks in the form of motifs can provide insights into global network properties. The authors propose an approach to decompose a network into a set of latent motifs, which can be used for network comparison, network denoising, and edge inference.

    • Hanbaek Lyu
    • , Yacoub H. Kureh
    •  & Mason A. Porter

  • Article
    | Open Access

    Near-eye displays are pivotal for building augmented and virtual reality platforms, but hurdles remain in achieving comfort and realistic visual experiences. Here, authors demonstrate compact 3D holographic glasses with focus cues by combining merits of waveguide displays and holographic displays.

    • Changwon Jang
    • , Kiseung Bang
    •  & Douglas Lanman

  • Article
    | Open Access

    Neural wavefunctions have become a highly accurate approach to solve the Schrödinger equation. Here, the authors propose an approach to optimize for a generalized wavefunction across compounds, which can help developing a foundation wavefunction model.

    • Michael Scherbela
    • , Leon Gerard
    •  & Philipp Grohs

  • Article
    | Open Access

    Existing feature visualisation methods are not well-suited for regression tasks. Here, authors introduce a method to learn the manifold topology related to deep neural network output and target labels and provide insightful visualisations of the high-dimensional features while preserving the local geometry.

    • Md Tauhidul Islam
    • , Zixia Zhou
    •  & Lei Xing

  • Article
    | Open Access

    Scintillators are widely used for radiation detection and require proper calibration in such applications. Here the authors discuss a Bayesian inference and machine learning method in combination with the Compton-edge probing that can describe the non-proportional scintillation response of inorganic scintillators.

    • David Breitenmoser
    • , Francesco Cerutti
    •  & Sabine Mayer

  • Article
    | Open Access

    Acute GVHD severity grading is based on target organ assessments. Here, the authors show that data-driven grading can identify 12 distinct grades with specific aGVHD phenotypes, which are associated with clinical outcomes, and that their method outperformed conventional gradings.

    • Evren Bayraktar
    • , Theresa Graf
    •  & Amin T. Turki

  • Article
    | Open Access

    Novel indicators of infectious disease prevalence could improve real-time surveillance and support healthcare planning. Here, the authors show that sales data for non-prescription medications from a UK high street retailer can improve the accuracy of models forecasting mortality from respiratory infections.

    • Elizabeth Dolan
    • , James Goulding
    •  & Laila J. Tata

  • Article
    | Open Access

    Optoelectronic neural networks are a promising avenue in AI computing for parallelization, power efficiency, and speed. Here, the authors present a dual-neuron optical-artificial learning approach for training large-scale diffractive neural networks, achieving VGG-level performance on ImageNet in simulation with a network that is 10 times larger than existing ones.

    • Xiaoyun Yuan
    • , Yong Wang
    •  & Lu Fang

  • Article
    | Open Access

    Over their careers, medicinal chemists develop a gut feeling for what is a promising molecule. Here, the authors use machine learning models to learn this intuition and show that it can be successfully applied in several drug discovery scenarios.

    • Oh-Hyeon Choung
    • , Riccardo Vianello
    •  & José Jiménez-Luna

  • Article
    | Open Access

    Rapid adoption of zero-emission vehicles with a concurrent transition to clean electricity is essential to achieve U.S. transportation decarbonization goals. Managing travel demand can ease this transition by reducing the need for clean electricity supply. @cghoehne, @nrel, #NRELMobility

    • Christopher Hoehne
    • , Matteo Muratori
    •  & Ookie Ma

  • Article
    | Open Access

    Critical transitions and qualitative changes of dynamics in cardiac, ecological, and economical systems, can be characterized by discrete-time bifurcations. The authors propose a deep learning framework that provides early warning signals for critical transitions in discrete-time experimental data.

    • Thomas M. Bury
    • , Daniel Dylewsky
    •  & Gil Bub

  • Article
    | Open Access

    High computational cost severely limit the applications of biophysically detailed multi-compartment models. Here, the authors present DeepDendrite, a GPU-optimized tool that drastically accelerates detailed neuron simulations for neuroscience and AI, enabling exploration of intricate neuronal processes and dendritic learning mechanisms in these fields.

    • Yichen Zhang
    • , Gan He
    •  & Tiejun Huang

  • Article
    | Open Access

    Transfer learning can be applied in computer vision and natural language processing to utilize knowledge from a source task to improve performance on a target task. The authors propose a framework for transfer learning with kernel methods for improved image classification and virtual drug screening.

    • Adityanarayanan Radhakrishnan
    • , Max Ruiz Luyten
    •  & Caroline Uhler

  • Article
    | Open Access

    Fano varieties are mathematical shapes that are basic units in geometry, they are challenging to classify in high dimensions. The authors introduce a machine learning approach that picks out geometric structure from complex mathematical data where rigorous analytical methods are lacking.

    • Tom Coates
    • , Alexander M. Kasprzyk
    •  & Sara Veneziale

  • Article
    | Open Access

    Modern microscopes can image a sample with sub-Angstrom and sub-picosecond resolutions, but this often requires analysis of tremendously large datasets. Here, the authors demonstrate that an autonomous experiment can yield over a 70% reduction in dataset size while still producing high-fidelity images of the sample.

    • Saugat Kandel
    • , Tao Zhou
    •  & Mathew J. Cherukara

  • Article
    | Open Access

    Many expression deconvolution approaches have been developed to estimate % RNA contributions of diverse cell types to mixed RNA measurements. Here, the authors have developed a complementary approach called scProjection to recover cell type-specific expression profiles from mixed RNA measurements.

    • Nelson Johansen
    • , Hongru Hu
    •  & Gerald Quon

  • Article
    | Open Access

    Authors utilize a number of models (mathematical, in vitro and in vivo infection) to analyse pre-clinical and Phase I clinical trial data, in regard to potential risk of resistance associated with a Plasmodium falciparum inhibitor, cabamiquine.

    • Eva Stadler
    • , Mohamed Maiga
    •  & Thomas Spangenberg

  • Article
    | Open Access

    Here, Mei and Chen propose an in-memory mechanical computing architecture with simplified and reduced data exchange, where computing occurs within mechanical memory units, to facilitate the design of intelligent mechanical systems.

    • Tie Mei
    •  & Chang Qing Chen

  • 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

    Synchronization of e-wearables can be challenging due to device performance variations. Here, the authors develop a general neural network-based solution that analyses and correct disparities between multiple virtual clocks and demonstrate it for a Bluetooth synchronized motion capture system at high frequency.

    • Karthikeyan Kalyanasundaram Balasubramanian
    • , Andrea Merello
    •  & Marco Crepaldi

  • 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

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