Results 141 to 150 of about 474 (170)

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

open access: yesAdvanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj   +8 more
wiley   +1 more source

When Poor Exciton Dissociation Limits Photocurrents in Organic Solar Cells: Why Low Offset Non‐Fullerene Acceptor Blends Can't Be Efficient

open access: yesAdvanced Materials, EarlyView.
The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

open access: yesAdvanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena   +3 more
wiley   +1 more source

Rethinking Charge Transport and Recombination in Donor‐Diluted Organic Solar Cells

open access: yesAdvanced Materials, EarlyView.
Organic solar cells with 1–45% PM6 content in Y12 were studied to link structure and charge dynamics to performance. The conductivity follows a 3D percolation model without a sharp threshold. Donor dilution preserves the photogeneration yield, but limits the fill factor due to transport resistance losses.
Chen Wang   +14 more
wiley   +1 more source

Contextualizing ancient texts with generative neural networks. [PDF]

open access: yesNature
Assael Y   +13 more
europepmc   +1 more source

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