Results 141 to 150 of about 29,034 (231)

Exceptional Antimodes in Multi‐Drive Cavity Magnonics

open access: yesAdvanced 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   +4 more
wiley   +1 more source

Topologically enhanced exciton transport. [PDF]

open access: yesNat Commun
Thompson JJP   +3 more
europepmc   +1 more source

Topological Materials and Related Applications

open access: yesAdvanced Electronic Materials, EarlyView.
This review covers topological materials—including topological insulators, quantum valley Hall and quantum spin Hall insulators, and topological Weyl and Dirac semimetals—as well as their most recent advancements in fields such as spintronics, electronics, photonics, thermoelectrics, and catalysis.
Carlo Grazianetti   +9 more
wiley   +1 more source

Large Anomalous Nernst Effect in Disordered CoPt Thin Films for Planar Thermoelectric Sensing

open access: yesAdvanced Electronic Materials, EarlyView.
Disordered CoPt thin films exhibit a large anomalous Nernst effect with strong dependence on Pt concentration. The transverse thermoelectric response reaches 3.22 µV K−1 near equiatomic composition and an ANE sensitivity of 0.3 µm A−1. The results reveal that composition‐controlled microstructure in sputtered CoPt alloys provides an effective route for
Mojtaba Mohammadi   +2 more
wiley   +1 more source

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

open access: yesAdvanced 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   +4 more
wiley   +1 more source

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

open access: yesAdvanced 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   +19 more
wiley   +1 more source

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