Results 111 to 120 of about 54,299 (266)

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

Optoelectronic Nanofluidic Neural Networks for Ionic Computing

open access: yesAdvanced Materials, EarlyView.
An ion‐based optoelectronic nanofluidic memristor enables neuromorphic computing in aqueous environments. With tunable ionic memory and multimodal synaptic plasticity, it realizes densely connected ionic neural networks capable of image classification, motion prediction, logic computation, and real‐time in‐sensor computing, advancing fully connected ...
Yaxin Huang   +10 more
wiley   +1 more source

Speech Emotion Recognition Using Convolution Neural Networks and Multi-Head Convolutional Transformer. [PDF]

open access: yesSensors (Basel), 2023
Ullah R   +9 more
europepmc   +1 more source

Electrically Coded Retinomorphic Spectrophotodetector

open access: yesAdvanced Materials, EarlyView.
Self‐powered retinomorphic pyro‐photodetector is demonstrated that avoids machine‐learning post‐processing and covers 365–940 nm. Electrostatic balancing of built‐in potential produces an electrical wavelength code, delivering <3 nm wavelength decoding accuracy with ∼46 µs response.
Mohit Kumar, Hyunmin Dang, Hyungtak Seo
wiley   +1 more source

Charge‐Encoded Sidechains Enable Deterministic Ion Ingress and Memory Retention in Organic Electrochemical Synaptic Transistors

open access: yesAdvanced Materials, EarlyView.
Organic electrochemical synaptic transistors based on sidechain‐engineered conjugated polyelectrolytes reveal that cationic sidechains enable efficient volumetric ion penetration and dense backbone doping, leading to enhanced transconductance and long‐term synaptic retention.
Haim Kwon   +6 more
wiley   +1 more source

Noise‐Tunable Memristor Enabling Programmable Probabilistic Neurons for Frequency‐Selective Time‐Series Signal Encoding

open access: yesAdvanced Materials, EarlyView.
Memristors offer tunable resistance and intrinsic instability, making them promising tunable noise sources. We propose a spiking‐rate‐programmable probabilistic neuron using a Ru/TaOx/Pt memristor, where resistance‐dependent noise enables frequency‐selective encoding.
Do Hoon Kim   +8 more
wiley   +1 more source

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