Research articles

Filter By:

  • Finding stable radical compounds for redox flow batteries is a challenging molecular design task. Sowndarya et al. combine an AlphaZero-based framework with a surrogate objective function trained on quantum chemistry simulations to generate suitable radical candidates that are stable. The approach promises to contribute to the development of low-cost, reliable energy storage technologies.

    • Shree Sowndarya S. V.
    • Jeffrey N. Law
    • Peter C. St. John
    Article Open Access
  • Targeted drug delivery is an exciting application of nanorobotics, but directing particles in the blood stream to the right location and in sufficient number is challenging. Gu and colleagues have developed a microtubule scaffold with embedded micromagnets that allows cargo, such as drug particles, to be transported in microvascular networks with precision and speed.

    • Hongri Gu
    • Emre Hanedan
    • Bradley J. Nelson
  • So-called noisy intermediate-scale quantum devices will be capable of a range of quantum simulation tasks, provided that the effects of noise can be sufficiently reduced. A neural error mitigation approach is developed that uses neural networks to improve the estimates of ground states and ground-state observables of molecules and quantum systems obtained using quantum simulations on near-term devices.

    • Elizabeth R. Bennewitz
    • Florian Hopfmueller
    • Pooya Ronagh
  • Using the natural dynamics of a legged robot for locomotion is challenging and can be computationally complex. A newly designed quadruped robot called Morti uses a central pattern generator inside two feedback loops as an adaptive method so that it efficiently uses the passive elasticity of its legs and can learn to walk within 1 h.

    • Felix Ruppert
    • Alexander Badri-Spröwitz
    Article Open Access
  • Artificial DNA circuits that can perform neural network-like computations have been developed, but scaling up these circuits to recognize a large number of patterns is a challenging task. Xiong, Zhu and colleagues experimentally demonstrate a convolutional neural network algorithm using a synthetic DNA-based regulatory circuit in vitro and develop a freeze–thaw approach to reduce the computation time from hours to minutes, paving the way towards more powerful biomolecular classifiers.

    • Xiewei Xiong
    • Tong Zhu
    • Hao Pei
  • An end-to-end machine learning approach that can learn which mechanisms determine cell fate and competition from a large time-lapse microscopy dataset is developed. The approach makes use of a probabilistic autoencoder to learn an interpretable representation of the organization of cells, and provides cell fate predictions that can be tested in drug screening experiments.

    • Christopher J. Soelistyo
    • Giulia Vallardi
    • Alan R. Lowe
  • Deep learning methods can provide useful predictions for drug design, but their hyperparameters need to be carefully tweaked to give good performance on a specific problem or dataset. Li et al. present here a method that finds appropriate architectures and hyperparameters for a wide range of drug design tasks and can achieve good performance without human intervention.

    • Yuquan Li
    • Chang-Yu Hsieh
    • Xiaojun Yao
  • Exoskeletons can assist movement in upper limb impairments to recover mobility and independence, but rigid or heavy exoskeletons can be impractical. Georgarakis and colleagues have developed a soft, tendon-driven device that assists shoulder movements and counteracts gravity to reduce muscle fatigue.

    • Anna-Maria Georgarakis
    • Michele Xiloyannis
    • Robert Riener
  • Robots usually learn to use tools from direct experience or from observing the use of a tool. While knowledge can be transferred between similar tools, novel and creative use of tools is challenging. Tee and colleagues present a method where skill transfer does not come from experience of using other tools but from using the robot’s own limbs.

    • Keng Peng Tee
    • Samuel Cheong
    • Gowrishankar Ganesh
  • While reinforcement learning can be a powerful tool for complex design tasks such as molecular design, training can be slow when problems are either too hard or too easy, as little is learned in these cases. Jeff Guo and colleagues provide a curriculum learning extension to the REINVENT de novo molecular design framework that provides problems of increasing difficulty over epochs such that the training process is more efficient.

    • Jeff Guo
    • Vendy Fialková
    • Atanas Patronov
  • B-cell receptors (BCRs) and their impact on B cells play a vital role in our immune system; however, the manner in which B cells are activated by BCRs are still poorly understood. Ze Zhang and colleagues present a graph-based method that connects BCR and single B-cell RNA sequencing data and identifies notable coupling between BCR and B-cell expression in COVID-19.

    • Ze Zhang
    • Woo Yong Chang
    • Tao Wang
  • Tactile sensing is needed for robots to physically interact with humans in daily living and in the workplace. A scientific challenge in robotics is how to simultaneously detect contact location and intensity. The authors describe a large-area sensing skin for robotic system applications, specifically for human–machine interactions.

    • Luca Massari
    • Giulia Fransvea
    • Calogero Maria Oddo
    Article Open Access
  • Respiratory complications after a COVID infection are a growing concern, but follow-up chest CT scans of COVID-19 survivors hardly present any recognizable lesions. A deep learning-based method was developed that calculates a scan-specific optimal window and removes irrelevant tissues such as airways and blood vessels from images with segmentation models, so that subvisual abnormalities in lung scans become visible.

    • Longxi Zhou
    • Xianglin Meng
    • Xin Gao
    Article Open Access
  • Deep learning could be less energy intensive when implemented on spike-based neuromorphic chips. An approach inspired by a characteristic feature of biological neurons, the presence of slowly changing internal currents, is developed to emulate long short-term memory units in a sparse spiking regime for neuromorphic implementation.

    • Arjun Rao
    • Philipp Plank
    • Wolfgang Maass
  • Swarms of microrobots could eventually be used to deliver drugs to specific targets in the body, but the coordination of these swarms in complex environments is challenging. Yang and colleagues present a real-time autonomous distribution planning method based on deep learning that controls both the shape and position of the swarm, as well as the imaging system used for swarm navigation to cover longer distances.

    • Lidong Yang
    • Jialin Jiang
    • Li Zhang
  • With the availability of a vast and growing number of digital publications, machine reading and other knowledge mining tools, computational methods can be applied at scale to extract insights from the scientific literature. Belikov et al. develop a Bayesian method to mine the biomedical literature that identifies robust scientific findings which could improve the planning of further experiments and scientific investigation.

    • Alexander V. Belikov
    • Andrey Rzhetsky
    • James Evans
  • The invariant causal prediction (ICP) framework tries to determine the causal variables given an outcome variable, but considerable effort is needed to adapt existing ICP methods to the clinical domain. The authors propose an automated causal inference method that could potentially address the challenges of applying the ICP framework to complex clinical datasets.

    • Ji Q. Wu
    • Nanda Horeweg
    • Viktor H. Koelzer
    Article Open Access