Results 171 to 180 of about 7,571,754 (304)

Optimal Control Drives Ultrafast and Energy‐Efficient Magnetization Switching in Van der Waals Magnets

Advanced Materials, EarlyView.
ABSTRACT The accelerating expansion of data‐centric technologies is sharply increasing the energy burden of information storage, placing unprecedented pressure on the efficiency of magnetic switching. Conventional field‐driven reversal, once the foundation of magnetic memory, has become impractical in modern architectures due to its high energy cost ...
Mohammad H. Badarneh, PeiYu Cai, Elton J. G. Santos   +2 more
wiley   +1 more source

Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces. [PDF]

Proc Conf Empir Methods Nat Lang Process, 2018
Rios A, Kavuluru R.
europepmc   +1 more source

Leaftronics: Bio‐Fractal Scaffolds From Leaf Venation for Low‐Waste Electronics

Advanced Materials, EarlyView.
“Leaftronics” transforms naturally evolved leaf venation into quasi‐fractal scaffolds for sustainable electronics. Polymer‐infiltrated leaf skeletons can be used to fabricate ultra‐smooth, reflow‐ and thin‐film‐compatible decomposable substrates, while making the same lignocellulose networks conducting results in flexible transparent electrodes.
Rakesh Rajendran Nair, Hrisheekesh Thachoth Chandran, Karl Leo, Hans Kleemann   +3 more
wiley   +1 more source

Author Correction: Multi-label Learning for Predicting the Activities of Antimicrobial Peptides. [PDF]

Sci Rep, 2018
Wang P, Ge R, Liu L, Xiao X, Li Y, Cai Y.   +5 more
europepmc   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced 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, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Research on plug-and-play correlation enhancement modules in deep multi-label learning. [PDF]

Sci Rep
Zhang J, Yuan C, Li X.
europepmc   +1 more source

Multi-label Learning for Predicting the Activities of Antimicrobial Peptides. [PDF]

Sci Rep, 2017
Wang P, Ge R, Liu L, Xiao X, Li Y, Cai Y.   +5 more
europepmc   +1 more source

Path‐Decoupled Cation‐Eutaxy III–V van der Waals Memristive Semiconductors for Mitigating the Neuromorphic Accuracy‐Energy Trade‐off

Advanced Materials, EarlyView.
Path‐decoupled III–V van der Waals memtransistors spatially separate ionic and electronic transport to overcome the conventional trade‐off between accuracy and energy in neuromorphic hardware. Mobile K+ ions in the vdW gaps set a wide conductance window, Gmax/Gmin, while gate‐tunable hole conduction lowers programming energy, enabling reliable ...
Jihong Bae, Ji Hoon Han, Taeyoung Kim, Jongbum Won, Seokmin Park, Gyu Won Kim, Sungsoon Kim, Hong Choi, Taehoon Kim, Hong‐Sub Lee, Jae‐Hyun Lee, Young Jin Choi, Jinwoo Cheon, Wooyoung Shim   +13 more
wiley   +1 more source

MOSTPLAS: a self-correction multi-label learning model for plasmid host range prediction. [PDF]

Bioinformatics
Zou W, Ji Y, Guan J, Sun Y.
europepmc   +1 more source

Measuring the Hall Effect in Hysteretic Materials

Advanced Materials, EarlyView.
The authors highlight common pitfalls in measuring the Hall effect: in hysteretic magnets, improper data processing can create signals that look exotic but are not real. This Perspective explains the origin of these artifacts and presents practical measurement strategies that help researchers identify reliable Hall responses in complex magnetic ...
Jaime M. Moya, Anthony Voyemant, Sudipta Chatterjee, Scott B. Lee, Grigorii Skorupskii, Connor J. Pollak, Leslie M. Schoop   +6 more
wiley   +1 more source