Results 61 to 70 of about 128 (127)

A General and Efficient Framework for the Rapid Design of Miniaturized, Wideband, and High‐Bit RIS

Advanced Electronic Materials, EarlyView.
A general and efficient framework is proposed for the rapid design of high‐performance reconfigurable intelligent surfaces (RISs). This framework integrates advanced antenna design techniques and incorporates various load types, quantities, and values to achieve the design of high‐performance RISs.
Jun Wei Zhang, Zheng Xing Wang, Wanwan Cao, Zhen Jie Qi, Li Jie Wu, Han Qing Yang, Qun Yan Zhou, Si Ran Wang, Hui Dong Li, Jun Yan Dai, Jiang Luo, Jun Wei Wu, Jia Nan Zhang, Zhen Zhang, Qiang Cheng   +14 more
wiley   +1 more source

Exceptional Antimodes in Multi‐Drive Cavity Magnonics

Advanced Electronic Materials, EarlyView.
Driven‐dissipative cavity‐magnonics provides a flexible platform for engineering non‐Hermitian physics such as exceptional points. Here, using a four‐port, three‐mode system with controllable microwave interference, antimodes and coherent perfect extinction (CPE) are realized, enabling active tuning to antimode exceptional points.
Mawgan A. Smith, Ryan D. McKenzie, Alban Joseph, Robert L. Stamps, Rair Macêdo   +4 more
wiley   +1 more source

Electric Field‐Induced Hole‐ and Electron‐Type Flat Bands in Twisted Double Bilayer Graphene

Advanced Electronic Materials, EarlyView.
The electronic structure of twisted double bilayer graphene is visualized using angle‐resolved photoemission spectroscopy with micrometer spatial resolution at twists of 3.1∘$^\circ$ and 6.0∘$^\circ$ as a function of gate voltage. Tunable hybridization effects and flat band formation occurs between valence and conduction band states due to a finite ...
Zhihao Jiang, Dongkyu Lee, Alfred J. H. Jones, Youngju Park, Paulina Majchrzak, Kimberly Hsieh, Chakradhar Sahoo, Thomas S. Nielsen, Kenji Watanabe, Takashi Taniguchi, Philip Hofmann, Yong P. Chen, Jeil Jung, Søren Ulstrup   +13 more
wiley   +1 more source

Amorphous Tungsten Driving Extraordinary Long‐Range Interlayer Exchange Coupling and Large Spin‐Orbit Torque

Advanced Electronic Materials, EarlyView.
The amorphous tungsten (W) is found to be a promising non‐magnetic interlayer simultaneously driving the long‐range interlayer exchange coupling and the large spin Hall effect in a synthetic antiferromagnet (SyAF). Thanks to these characteristics, efficient spin‐orbit torque switching is achieved in the SyAF.
Xueyao Hou, Keita Ito, Varun K. Kushwaha, Takumi Yamazaki, Junwei Gu, Zhenchao Wen, Keisuke Masuda, Jun Okabayashi, Shoya Sakamoto, Takeshi Seki   +9 more
wiley   +1 more source

Controllable Superconductivity in Suspended NbSe2

Advanced Electronic Materials, EarlyView.
Suspended NbSe2${\rm NbSe}_2$ provides two complementary routes for controlling superconducting transport. Gate‐driven deformation strains the suspended crystal and tunes its critical temperature and current. Weak thermal anchoring enhances thermal feedback, contributing to multistep hysteresis and negative differential resistance.
Ruihuan Fang, Cuiju Yu, Youqiang Huang, Tosson Elalaily, Yuvraj Chaudhry, Yaoqiang Zhou, Andres Castellanos‐Gomez, Sanshui Xiao, Jiwon Park, Hyunyong Choi, Fida Ali, Hanlin Fang, Jose L. Lado, Pertti Hakonen, Zhipei Sun   +14 more
wiley   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source