Results 31 to 40 of about 198 (143)

Ultrafast Dynamics of Spin Current and Electron Temperature in Spintronic Terahertz Emitters

Advanced Science, EarlyView.
This work quantifies optical‐pump‐induced THz transmission variations to reveal electron and lattice temperature dynamics and presents time‐resolved THz spectral maps for sub‐picosecond analysis. A 63 ± 8 fs time lag between THz emission and laser excitation is identified, serving as an intrinsic parameter limiting the upper frequency of spintronic THz
Yifan Wang, Zuanming Jin, Miao Cai, Zhiqiang Lan, Zhenjie Ge, Zheng Feng, Wei Tan, Alexei V. Balakin, Alexander P. Shkurinov, Yan Peng, Yiming Zhu   +10 more
wiley   +1 more source

Bias‐Engineered Synthetic Antiferromagnets Hosting Sub‐20 nm Zero‐Field Skyrmions at Room Temperature

Advanced Science, EarlyView.
A fully compensated synthetic antiferromagnet (SAF) multilayer exhibits a uniform state at zero field, without skyrmions. We use a SAF bias system to provide RKKY‐mediated exchange bias to the SAF multilayer, promoting zero‐field skyrmion stabilization and polarity control.
Emily Darwin, Riccardo Tomasello, Reshma Peremadathil Pradeep, Mario Carpentieri, Giovanni Finocchio, Hans J. Hug   +5 more
wiley   +1 more source

Strain Tuning the Occupation of Candidate Topological Weyl States in W‐Doped MoTe2

Advanced Science, EarlyView.
The present study investigates strain‐induced modifications of the electronic structure in the Weyl semimetal Td${\rm T}_d$‐Mo0.91W0.09Te2${\mathrm{Mo}}_{0.91}{\mathrm{W}}_{0.09}{\mathrm{Te}}_{2}$ using hard X‐ray angle‐resolved photoemission spectroscopy.
Amon Lanz, Olena Tkach, Yaryna Lytvynenko, Florian Diekmann, Harshit Agarwal, Sofia Michaela Souliou, Mehdi Frachet, Michael Merz, Sergii V. Chernov, Andrei Gloskovskii, Volkmar Koller, Dmytro Kutnyakhov, Moritz Hoesch, Christoph Schlueter, Philipp Rüssmann, Yuriy Mokrousov, Matthieu Le Tacon, Kai Rossnagel, Jure Demsar, Gerd Schönhense, Hans‐Joachim Elmers, Olena Fedchenko   +21 more
wiley   +1 more source

Atomically Modulating Competing Exchange Interactions in Centrosymmetric Skyrmion Hosts GdRu2X2 (X = Si and Ge)

Advanced Electronic Materials, EarlyView.
Our work bridges the gap between skyrmion discovery and material design by demonstrating how atomic‐scale control of exchange interactions enables tunable skyrmion phase transitions in centrosymmetric magnetic metals. ABSTRACT Magnetic skyrmions are topologically protected spin states that hold promise for shaping the future of electronics.
Dasuni N. Rathnaweera, Xudong Huai, K. Ramesh Kumar, Michał J. Winiarski, Tomasz Klimczuk, Allana G. Iwanicki, Satya K. Kushwaha, Martin Mourigal, Tyrel M. McQueen, Thao T. Tran   +9 more
wiley   +1 more source

Nonlinear Transverse Transport in a Ferromagnetic Polar Metal

Advanced Electronic Materials, EarlyView.
This work reports the observation of a nonlinear transverse response in ferromagnetic polar SrRuO3(111) thin films. The nonlinear signal exhibits a sharp enhancement across the magnetic phase transition. Through detailed scaling and theoretical analysis, the authors attribute this behavior to a sign reversal of the Berry curvature triple, establishing ...
Xuyang Sha, Xuejin Zhang, Hao Liu, Jin Cao, Ruohan Chen, Jinfeng Zhai, Dingfu Shao, Shiwei Wu, Zhen Wang, Cong Xiao, Shengyuan A. Yang, Pan He, Hangwen Guo, Jian Shen   +13 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

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

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