Featured
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Article
| Open Access
Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy
Improving signal to noise ratio of Raman spectra is vital for the application. Here, authors show a noise learning method that learns the noise feature of a spectrometer. This improves the signal to noise ratio and makes deep learning to be instrument dependent instead of sample dependent.
- Hao He
- , Maofeng Cao
- & Bin Ren
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Article
| Open Access
Efficient optimization with higher-order Ising machines
Combinatorial optimization problems can be solved on parallel hardware called Ising machines. Most studies have focused on the use of second-order Ising machines. Compared to second-order Ising machines, the authors show that higher-order Ising machines realized with coupled-oscillator networks can be more resource-efficient and provide superior solutions for constraint satisfaction problems.
- Connor Bybee
- , Denis Kleyko
- & Friedrich T. Sommer
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Article
| Open Access
Brownian reservoir computing realized using geometrically confined skyrmion dynamics
Magnetic skyrmions, due to their strongly nonlinearity and multiscale dynamics, are promising for implementing reservoir computing. Here, the authors experimentally demonstrate skyrmion-based spatially multiplexed reservoir computing able to perform Boolean Logic operations, using thermal and current driven dynamics of spin structures.
- Klaus Raab
- , Maarten A. Brems
- & Mathias Kläui
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Article
| Open Access
Simulating a chemically fueled molecular motor with nonequilibrium molecular dynamics
Molecular motors move in response to an imbalance between concentrations of fuel and waste molecules. Here, the authors simulate such non-equilibrium conditions to characterize a model motor’s performance and mechanism of operation.
- Alex Albaugh
- & Todd R. Gingrich
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Article
| Open Access
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
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Article
| Open Access
An in-memory computing architecture based on two-dimensional semiconductors for multiply-accumulate operations
In standard computing architectures, memory and logic circuits are separated, a feature that slows matrix operations vital to deep learning algorithms. Here, the authors present an alternate in-memory architecture and demonstrate a feasible approach for analog matrix multiplication.
- Yin Wang
- , Hongwei Tang
- & Wenzhong Bao
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Article
| Open Access
Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
Designing efficient neuromorphic systems for complex temporal tasks remains a challenge. Zhong et al. develop a parallel memristor-based reservoir computing system capable of tuning critical parameters, achieving classification accuracy of 99.6% in spoken-digit recognition and time-series prediction error of 0.046 in the Hénon map.
- Yanan Zhong
- , Jianshi Tang
- & Huaqiang Wu
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Article
| Open Access
Emergence of winner-takes-all connectivity paths in random nanowire networks
Nanowire networks with memristive properties are promising for neuromorphic applications. Here, the authors observe the formation of a preferred conduction pathway which uses the lowest possible energy to get through the network and could be exploited for the design of optimal brain-inspired devices.
- Hugh G. Manning
- , Fabio Niosi
- & John J. Boland
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Article
| Open Access
Insight into induced charges at metal surfaces and biointerfaces using a polarizable Lennard–Jones potential
Molecular dynamics models for predicting the behavior of metallic nanostructures typically do not take into account polarization effects in metals. Here, the authors introduce a polarizable Lennard–Jones potential that provides quantitative insight into the role of induced charges at metal surfaces and related complex material interfaces.
- Isidro Lorenzo Geada
- , Hadi Ramezani-Dakhel
- & Hendrik Heinz
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Article
| Open Access
Immobilization of single argon atoms in nano-cages of two-dimensional zeolite model systems
While noble gases can be trapped in 3D porous structures, immobilizing them on 2D surfaces represents a formidable challenge. Here, the authors cage individual argon atoms in 2D model zeolite frameworks at room temperature, providing exciting opportunities for the fundamental study of isolated noble gas atoms using surface science methods.
- Jian-Qiang Zhong
- , Mengen Wang
- & J. Anibal Boscoboinik
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Article
| Open Access
Lattice-free prediction of three-dimensional structure of programmed DNA assemblies
DNA may be used to fabricate functional nanostructures with various possible geometries, but first being able to predict these structures is a challenging task. Here, the authors use coarse-grained modelling to predict the shape of artificial DNA nanostructures in solution.
- Keyao Pan
- , Do-Nyun Kim
- & Mark Bathe
Highly parallel and ultra-low-power probabilistic reasoning with programmable gaussian-like memory transistors