AI and machine learning

Featured articles

• 

  A knowledge-guided pre-training framework for improving molecular representation learning

  Accurate property prediction relies on effective molecular representation. Here, the authors introduce KPGT, a knowledge-guided self-supervised framework that improves molecular representation, leading to superior predictions of molecular properties and advancing AI-driven drug discovery.

  • Han Li
  • Ruotian Zhang
  • Jianyang Zeng

  ArticleOpen Access21 Nov 2023 Nature Communications
• 

  Differentially private knowledge transfer for federated learning

  To ensure the privacy of processed data, federated learning approaches involve local differential privacy techniques which however require communicating a large amount of data that needs protection. The authors propose here a framework that uses selected small data to transfer knowledge in federated learning with privacy guarantees.

  • Tao Qi
  • Fangzhao Wu
  • Xing Xie

  ArticleOpen Access24 Jun 2023 Nature Communications
• 

  On the visual analytic intelligence of neural networks

  Visual oddity tasks delve into the visual analytic intelligence of humans, which remained challenging for artificial neural networks. The authors propose here a model with biologically inspired neural dynamics and synthetic saccadic eye movements with improved efficiency and accuracy in solving the visual oddity tasks.

  • Stanisław Woźniak
  • Hlynur Jónsson
  • Evangelos Eleftheriou

  ArticleOpen Access25 Sep 2023 Nature Communications
• 

  Flight trajectory prediction enabled by time-frequency wavelet transform

  Accurate flight trajectory prediction can be a challenging task in air traffic control, especially for maneuver operations. Here, authors develop a time-frequency analysis based on an encoder-decoder neural architecture to estimate wavelet components and model global flight trends and local motion details.

  • Zheng Zhang
  • Dongyue Guo
  • Yi Lin

  ArticleOpen Access29 Aug 2023 Nature Communications
• 

  Continuous estimation of power system inertia using convolutional neural networks

  The increase of intermittent energy sources and renewable energy penetration generally results in reduced overall inertia, making power systems susceptible to disturbances. Here, authors develop an AI-based method to estimate inertia in real-time and test its performance on a heterogeneous power network.

  • Daniele Linaro
  • Federico Bizzarri
  • Angelo M. Brambilla

  ArticleOpen Access24 Jul 2023 Nature Communications
• 

  Data fusion and multivariate analysis for food authenticity analysis

  Using two different mass spectrometric platforms, authors demonstrate how metabolomic data fusion and multivariate analysis can be used to accurately identify the geographic origin and production method of salmon.

  • Yunhe Hong
  • Nicholas Birse
  • Christopher T. Elliott

  ArticleOpen Access8 Jun 2023 Nature Communications
• 

  Learning how network structure shapes decision-making for bio-inspired computing

  Better understanding of a trade-off between the speed and accuracy of decision-making is relevant for mapping biological intelligence to machines. The authors introduce a brain-inspired learning algorithm to uncover dependencies in individual fMRI networks with features of neural activity and predict inter-individual differences in decision-making.

  • Michael Schirner
  • Gustavo Deco
  • Petra Ritter

  ArticleOpen Access23 May 2023 Nature Communications
• 

  Combining data and theory for derivable scientific discovery with AI-Descartes

  Automatic extraction of consistent governing laws from data is a challenging problem. The authors propose a method that takes as input experimental data and background theory and combines symbolic regression with logical reasoning to obtain scientifically meaningful symbolic formulas.

  • Cristina Cornelio
  • Sanjeeb Dash
  • Lior Horesh

  ArticleOpen Access12 Apr 2023 Nature Communications
• 

  Meta-learning biologically plausible plasticity rules with random feedback pathways

  The biological plausibility of backpropagation and its relationship with synaptic plasticity remain open questions. The authors propose a meta-learning approach to discover interpretable plasticity rules to train neural networks under biological constraints. The meta-learned rules boost the learning efficiency via bio-inspired synaptic plasticity.

  • Navid Shervani-Tabar
  • Robert Rosenbaum

  ArticleOpen Access31 Mar 2023 Nature Communications
• 

  Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy

  Artificial Intelligence has achieved success in a variety of single-player or competitive two-player games with no communication between players. Here, the authors propose an approach where Artificial Intelligence agents have ability to negotiate and form agreements, playing the board game Diplomacy.

  • János Kramár
  • Tom Eccles
  • Yoram Bachrach

  ArticleOpen Access6 Dec 2022 Nature Communications
• 

  Transfer learning enhanced water-enabled electricity generation in highly oriented graphene oxide nanochannels

  Large-scale nanochannel integration and the multi-parameter coupling restrictive influence on electric generation are big challenges for effective energy harvesting from spontaneous water flow within artificial nanochannels. Here, authors apply transfer learning to overcome these and design optimized water-enabled generators.

  • Ce Yang
  • Haiyan Wang
  • Liangti Qu

  ArticleOpen Access10 Nov 2022 Nature Communications
• 

  Mapping global dynamics of benchmark creation and saturation in artificial intelligence

  Recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, the authors introduce methodologies for creating condensed maps of the global dynamics of benchmark.

  • Simon Ott
  • Adriano Barbosa-Silva
  • Matthias Samwald

  ArticleOpen Access10 Nov 2022 Nature Communications
• 

  Adversarial attacks and adversarial robustness in computational pathology

  Artificial Intelligence can support diagnostic workflows in oncology, but they are vulnerable to adversarial attacks. Here, the authors show that convolutional neural networks are highly susceptible to white- and black-box adversarial attacks in clinically relevant classification tasks.

  • Narmin Ghaffari Laleh
  • Daniel Truhn
  • Jakob Nikolas Kather

  ArticleOpen Access29 Sep 2022 Nature Communications
• 

  Generalisable 3D printing error detection and correction via multi-head neural networks

  3D printing is prone to errors and continuous monitoring and real-time correction during processing remains a significant challenge limiting its applied potential. Here, authors train a neural network to detect and correct diverse errors in real time across many geometries, materials and even printing setups.

  • Douglas A. J. Brion
  • Sebastian W. Pattinson

  ArticleOpen Access15 Aug 2022 Nature Communications
• 

  Addressing fairness in artificial intelligence for medical imaging

  A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field of medical imaging, where AI systems are beginning to be increasingly adopted, is no exception. Here we discuss the meaning of fairness in this area and comment on the potential sources of biases, as well as the strategies available to mitigate them. Finally, we analyze the current state of the field, identifying strengths and highlighting areas of vacancy, challenges and opportunities that lie ahead.

  • María Agustina Ricci Lara
  • Rodrigo Echeveste
  • Enzo Ferrante

  CommentOpen Access6 Aug 2022 Nature Communications
• 

  Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements

  Existing neural network potentials are generally designed for narrow target materials. Here the authors develop a neural network potential which is able to handle any combination of 45 elements and show its applicability in multiple domains.

  • So Takamoto
  • Chikashi Shinagawa
  • Takeshi Ibuka

  ArticleOpen Access30 May 2022 Nature Communications
• 

  Decoding lip language using triboelectric sensors with deep learning

  Lip-language decoding systems are a promising technology to help people lacking a voice live a convenient life with barrier-free communication. Here, authors propose a concept of such system integrating self-powered triboelectric sensors and a well-trained dilated RNN model based on prototype learning.

  • Yijia Lu
  • Han Tian
  • Zhong Lin Wang

  ArticleOpen Access17 Mar 2022 Nature Communications
• 

  Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations

  Deep learning has an increasing impact to assist research. Here, authors show that a dynamical neural network, trained on a minimal amount of data, can predict the behaviour of spintronic devices with high accuracy and an extremely efficient simulation time.

  • Xing Chen
  • Flavio Abreu Araujo
  • Damien Querlioz

  ArticleOpen Access23 Feb 2022 Nature Communications
• 

  Uncovering interpretable potential confounders in electronic medical records

  Randomized clinical trials are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding factors. Here, the authors develop a framework based on natural language processing to uncover interpretable potential confounders from text.

  • Jiaming Zeng
  • Michael F. Gensheimer
  • Ross D. Shachter

  ArticleOpen Access23 Feb 2022 Nature Communications
• 

  Active label cleaning for improved dataset quality under resource constraints

  High quality labels are important for model performance, evaluation and selection in medical imaging. As manual labelling is time-consuming and costly, the authors explore and benchmark various resource-effective methods for improving dataset quality.

  • Mélanie Bernhardt
  • Daniel C. Castro
  • Ozan Oktay

  ArticleOpen Access4 Mar 2022 Nature Communications
• 

  AI Pontryagin or how artificial neural networks learn to control dynamical systems

  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

  ArticleOpen Access17 Jan 2022 Nature Communications
• 

  Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings

  Electrons and phonons give rise to important properties of materials. The machine learning framework Mat2Spec vastly accelerates their computational characterization, enabling discovery of materials for thermoelectrics and solar energy technologies.

  • Shufeng Kong
  • Francesco Ricci
  • John M. Gregoire

  ArticleOpen Access17 Feb 2022 Nature Communications
• 

  Artificial-intelligence-driven discovery of catalyst genes with application to CO₂ activation on semiconductor oxides

  Here the authors demonstrate an artificial-intelligence based approach to identify catalytic materials features that correlate with mechanisms that trigger, facilitate, or hinder CO2 catalytic reactions.

  • Aliaksei Mazheika
  • Yang-Gang Wang
  • Matthias Scheffler

  ArticleOpen Access20 Jan 2022 Nature Communications
• 

  Artificial intelligence-enhanced quantum chemical method with broad applicability

  Artificial intelligence is combined with quantum mechanics to break the limitations of traditional methods and create a new general-purpose method for computational chemistry simulations with high accuracy, speed and transferability.

  • Peikun Zheng
  • Roman Zubatyuk
  • Pavlo O. Dral

  ArticleOpen Access2 Dec 2021 Nature Communications
• 

  Face detection in untrained deep neural networks

  Face-selective neurons are observed in the primate visual pathway and are considered as the basis of face detection in the brain. Here, using a hierarchical deep neural network model of the ventral visual stream, the authors suggest that face selectivity arises in the complete absence of training.

  • Seungdae Baek
  • Min Song
  • Se-Bum Paik

  ArticleOpen Access16 Dec 2021 Nature Communications
• 

  A Deep Gravity model for mobility flows generation

  The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. Here, the authors use deep neural networks to discover non-linear relationships between geographical variables and mobility flows.

  • Filippo Simini
  • Gianni Barlacchi
  • Luca Pappalardo

  ArticleOpen Access12 Nov 2021 Nature Communications
• 

  3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients

  Current spread hampers the efficacy of neuromodulation, while existing animal, in vitro and in silico models have failed to give patient-centric insights. Here the authors employ 3D printing and machine learning to advance clinical predictions of current spread for cochlear implant patients.

  • Iek Man Lei
  • Chen Jiang
  • Yan Yan Shery Huang

  ArticleOpen Access29 Oct 2021 Nature Communications
• 

  Neural heterogeneity promotes robust learning

  The authors show that heterogeneity in spiking neural networks improves accuracy and robustness of prediction for complex information processing tasks, results in optimal parameter distribution similar to experimental data and is metabolically efficient for learning tasks at varying timescales.

  • Nicolas Perez-Nieves
  • Vincent C. H. Leung
  • Dan F. M. Goodman

  ArticleOpen Access4 Oct 2021 Nature Communications
• 

  Embodied intelligence via learning and evolution

  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

  ArticleOpen Access6 Oct 2021 Nature Communications
• 

  Next generation reservoir computing

  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

  ArticleOpen Access21 Sep 2021 Nature Communications
• 

  Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging

  In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates. Here, the authors propose a continual learning approach to deal with such domain shifts occurring at unknown time points.

  • Matthias Perkonigg
  • Johannes Hofmanninger
  • Georg Langs

  ArticleOpen Access28 Sep 2021 Nature Communications
• 

  Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry

  Machine learning has the potential to significantly speed-up the discovery of new materials in synthetic materials chemistry. Here the authors combine unsupervised machine learning and crystal structure prediction to predict a novel quaternary lithium solid electrolyte that is then synthesized.

  • Andrij Vasylenko
  • Jacinthe Gamon
  • Matthew J. Rosseinsky

  ArticleOpen Access21 Sep 2021 Nature Communications
• 

  Understanding the onset of hot streaks across artistic, cultural, and scientific careers

  Despite their ubiquitous nature across a wide range of creative domains, it remains unclear if there is any regularity underlying the beginning of successful periods in a career. Here, the authors develop computational methods to trace the career outputs of artists, film directors, and scientists and explore how they move in their creative space along their career trajectory.

  • Lu Liu
  • Nima Dehmamy
  • Dashun Wang

  ArticleOpen Access13 Sep 2021 Nature Communications
• 

  Machine learning dismantling and early-warning signals of disintegration in complex systems

  Network dismantling allows to find minimum set of units attacking which leads to system’s break down. Grassia et al. propose a deep-learning framework for dismantling of large networks which can be used to quantify the vulnerability of networks and detect early-warning signals of their collapse.

  • Marco Grassia
  • Manlio De Domenico
  • Giuseppe Mangioni

  ArticleOpen Access31 Aug 2021 Nature Communications
• 

  Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

  Development of deep neural networks benefits from new approaches and perspectives. Stelzer et al. propose to fold a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops which is also of relevance for new hardware implementations and applications.

  • Florian Stelzer
  • André Röhm
  • Serhiy Yanchuk

  ArticleOpen Access27 Aug 2021 Nature Communications
• 

  Avalanches and edge-of-chaos learning in neuromorphic nanowire networks

  Neuromorphic nanowire networks are found to exhibit neural-like dynamics, including phase transitions and avalanche criticality. Hochstetter and Kuncic et al. show that the dynamical state at the edge-of-chaos is optimal for learning and favours computationally complex information processing tasks.

  • Joel Hochstetter
  • Ruomin Zhu
  • Zdenka Kuncic

  ArticleOpen Access29 Jun 2021 Nature Communications
• 

  Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization

  Unmasking the decision making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, the authors demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts.

  • Tianyu Han
  • Sven Nebelung
  • Daniel Truhn

  ArticleOpen Access14 Jul 2021 Nature Communications
• 

  Machine learning based energy-free structure predictions of molecules, transition states, and solids

  Accurate computational prediction of atomistic structure with traditional methods is challenging. The authors report a kernel-based machine learning model capable of reconstructing 3D atomic coordinates from predicted interatomic distances across a variety of system classes.

  • Dominik Lemm
  • Guido Falk von Rudorff
  • O. Anatole von Lilienfeld

  ArticleOpen Access22 Jul 2021 Nature Communications
• 

  A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions

  Deep learning algorithms trained on data streamed temporally from different clinical sites and from a multitude of physiological sensors are generally affected by a degradation in performance. To mitigate this, the authors propose a continual learning strategy that employs a replay buffer.

  • Dani Kiyasseh
  • Tingting Zhu
  • David Clifton

  ArticleOpen Access9 Jul 2021 Nature Communications
• 

  Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression

  Reinbold et al. propose a physics-informed data-driven approach that successfully discovers a dynamical model using high-dimensional, noisy and incomplete experimental data describing a weakly turbulent fluid flow. This approach is relevant to other non-equilibrium spatially-extended systems.

  • Patrick A. K. Reinbold
  • Logan M. Kageorge
  • Roman O. Grigoriev

  ArticleOpen Access28 May 2021 Nature Communications
• 

  Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks

  Canatar et al. propose a predictive theory of generalization in kernel regression applicable to real data. This theory explains various generalization phenomena observed in wide neural networks, which admit a kernel limit and generalize well despite being overparameterized.

  • Abdulkadir Canatar
  • Blake Bordelon
  • Cengiz Pehlevan

  ArticleOpen Access18 May 2021 Nature Communications
• 

  A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis

  Predictive computational approaches are fundamental to accelerating solid-state inorganic synthesis. This work demonstrates a computational tractable approach constructed from available thermochemistry data and based on a graph-based network model for predicting solid-state inorganic reaction pathways.

  • Matthew J. McDermott
  • Shyam S. Dwaraknath
  • Kristin A. Persson

  ArticleOpen Access25 May 2021 Nature Communications
• 

  Synaptic metaplasticity in binarized neural networks

  Deep neural networks usually rapidly forget the previously learned tasks while training new ones. Laborieux et al. propose a method for training binarized neural networks inspired by neuronal metaplasticity that allows to avoid catastrophic forgetting and is relevant for neuromorphic applications.

  • Axel Laborieux
  • Maxence Ernoult
  • Damien Querlioz

  ArticleOpen Access5 May 2021 Nature Communications
• 

  Bias free multiobjective active learning for materials design and discovery

  Identifying optimal materials in multiobjective optimization problems represents a challenge for new materials design approaches. Here the authors develop an active-learning algorithm to optimize the Pareto-optimal solutions successfully applied to the in silico polymer design for a dispersant-based application.

  • Kevin Maik Jablonka
  • Giriprasad Melpatti Jothiappan
  • Brian Yoo

  ArticleOpen Access19 Apr 2021 Nature Communications
• 

  Qualitative similarities and differences in visual object representations between brains and deep networks

  Deep neural networks are widely considered as good models for biological vision. Here, we describe several qualitative similarities and differences in object representations between brains and deep networks that elucidate when deep networks can be considered good models for biological vision and how they can be improved.

  • Georgin Jacob
  • R. T. Pramod
  • S. P. Arun

  ArticleOpen Access25 Mar 2021 Nature Communications
• 

  Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron tomography

  Precise determination of surface atomic structure of metallic nanoparticles is key to unlock their surface/interface properties. Here the authors introduce a neural network-assisted atomic electron tomography approach that provides a three-dimensional reconstruction of metallic nanoparticles at individual atom level.

  • Juhyeok Lee
  • Chaehwa Jeong
  • Yongsoo Yang

  ArticleOpen Access30 Mar 2021 Nature Communications
• 

  Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning

  Intolerance to variation is a strong indicator of disease relevance for coding regions of the human genome. Here, the authors present JARVIS, a deep learning method integrating intolerance to variation in non-coding regions and sequence-specific annotations to infer non-coding variant pathogenicity.

  • Dimitrios Vitsios
  • Ryan S. Dhindsa
  • Slavé Petrovski

  ArticleOpen Access8 Mar 2021 Nature Communications
• 

  Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning

  Predicting a priori local defects in amorphous materials remains a grand challenge. Here authors combine a rotationally non-invariant structure representation with deep-learning to predict the propensity for shear transformations of amorphous solids for different loading orientations, only given the static structure.

  • Zhao Fan
  • Evan Ma

  ArticleOpen Access8 Mar 2021 Nature Communications
• 

  Learning dominant physical processes with data-driven balance models

  The dynamics of complex physical systems can be determined by the balance of a few dominant processes. Callaham et al. propose a machine learning approach for the identification of dominant regimes from experimental or numerical data with examples from turbulence, optics, neuroscience, and combustion.

  • Jared L. Callaham
  • James V. Koch
  • Steven L. Brunton

  ArticleOpen Access15 Feb 2021 Nature Communications
• 

  Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment

  Tests for autonomous vehicles are usually made in the naturalistic driving environment where safety-critical scenarios are rare. Feng et al. propose a testing approach combining naturalistic and adversarial environment which allows to accelerate testing process and detect dangerous driving events.

  • Shuo Feng
  • Xintao Yan
  • Henry X. Liu

  ArticleOpen Access2 Feb 2021 Nature Communications
• 

  Improving the accuracy of medical diagnosis with causal machine learning

  In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them, while existing diagnostic algorithms are purely associative. Here, the authors reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic algorithms.

  • Jonathan G. Richens
  • Ciarán M. Lee
  • Saurabh Johri

  ArticleOpen Access11 Aug 2020 Nature Communications