Focus 26 January 2021

AI and machine learning

Computational advances have enabled the deployment of increasingly complex models, which are applied now to a broad-ranging set of fields. This editorial showcase aims at providing a snapshot of the current tools and challenges that are currently holding the promise to change lives in several ways. Herein, we also highlight research on the underlying pursuit of developing the concept of Artificial Intelligence.

Featured articles

Predicting and improving complex beer flavor through machine learning

Perception and appreciation of food flavour depends on many factors, posing a challenge for effective prediction. Here, the authors combine extensive chemical and sensory analyses of 250 commercial Belgian beers to train machine learning models that enable flavour and consumer appreciation prediction.
- Michiel Schreurs
- Supinya Piampongsant
- Kevin J. Verstrepen
ArticleOpen Access26 Mar 2024 Nature Communications
Local prediction-learning in high-dimensional spaces enables neural networks to plan

The task of planning a sequence of actions, and dynamically adjusting the plan in dependence of unforeseen circumstances, remains challenging for artificial intelligence frameworks. The authors introduce a learning approach inspired by cognitive functions, that demonstrates high flexibility and generalization capability in planning tasks, suitable for on-chip learning.
- Christoph Stöckl
- Yukun Yang
- Wolfgang Maass
ArticleOpen Access15 Mar 2024 Nature Communications
Emerging opportunities and challenges for the future of reservoir computing

Reservoir Computing has shown advantageous performance in signal processing and learning tasks due to compact design and ability for fast training. Here, the authors discuss the parallel progress of mathematical theory, algorithm design and experimental realizations of Reservoir Computers, and identify emerging opportunities as well as existing challenges for their large-scale industrial adoption.
- Min Yan
- Can Huang
- Jie Sun
PerspectiveOpen Access6 Mar 2024 Nature Communications
Predicting multiple observations in complex systems through low-dimensional embeddings

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
ArticleOpen Access12 Mar 2024 Nature Communications
Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

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
ArticleOpen Access28 Feb 2024 Nature Communications
Hybrid AI-enhanced lightning flash prediction in the medium-range forecast horizon

In this work, authors propose a synergistic approach combining state-of-the-art deterministic forecasting model with artificial intelligence for predicting lightning occurrences. The strategy shows efficient predictive capabilities at medium-range forecast horizons.
- Mattia Cavaiola
- Federico Cassola
- Andrea Mazzino
ArticleOpen Access8 Feb 2024 Nature Communications
Segment anything in medical images

Segmentation is an important fundamental task in medical image analysis. Here the authors show a deep learning model for efficient and accurate segmentation across a wide range of medical image modalities and anatomies.
- Jun Ma
- Yuting He
- Bo Wang
ArticleOpen Access22 Jan 2024 Nature Communications
Connectome-based reservoir computing with the conn2res toolbox

Brain connectivity patterns shape computational capacity of biological neural networks, however mapping empirically measured connectivity to artificial networks remains challenging. The authors present a toolbox for implementing biological neural networks as artificial reservoir networks. The toolbox allows for a variety of empirical/measured connectomes and is equipped with various dynamical systems, and cognitive tasks.
- Laura E. Suárez
- Agoston Mihalik
- Bratislav Misic
ArticleOpen Access22 Jan 2024 Nature Communications
Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing

Utilising geometric information and reducing computational costs are key challenges in the molecular modelling field. Here, authors propose ViSNet, which efficiently extracts geometric features, accurately predicts molecular properties, and drives simulations with interpretability.
- Yusong Wang
- Tong Wang
- Tie-Yan Liu
ArticleOpen Access5 Jan 2024 Nature Communications
Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics

Brain-inspired spiking neural networks have shown their capability for effective learning, however current models may not consider realistic heterogeneities present in the brain. The authors propose a neuron model with temporal dendritic heterogeneity for improved neuromorphic computing applications.
- Hanle Zheng
- Zhong Zheng
- Lei Deng
ArticleOpen Access4 Jan 2024 Nature Communications
Learning few-shot imitation as cultural transmission

The modelling of human-like behaviours is one of the challenges in the field of Artificial Intelligence. Inspired by experimental studies of cultural evolution, the authors propose a reinforcement learning approach to generate agents capable of real-time third-person imitation.
- Avishkar Bhoopchand
- Bethanie Brownfield
- Lei M. Zhang
ArticleOpen Access28 Nov 2023 Nature Communications
Augmenting interpretable models with large language models during training

Prediction and interpretation tasks may be challenging in high-stakes applications, such as medical decision-making, or systems with compute-limited hardware. The authors introduce an augmented framework for leveraging the knowledge learned by Large Language Models to build interpretable models which are both accurate and efficient.
- Chandan Singh
- Armin Askari
- Jianfeng Gao
ArticleOpen Access30 Nov 2023 Nature Communications
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

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