Results 51 to 60 of about 140,252 (207)

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

Topological Materials and Related Applications

Advanced 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, Roberto Mantovan, Emanuele Longo, Harold J. W. Zandvliet, Pantelis Bampoulis, Yu Pan, Fan Li, Xia Wang, Payton Downey, Alessandro Molle   +9 more
wiley   +1 more source

Exploiting Temperature Effects for Robust Control and Reference Circuits Using Thin‐Film Contact‐Controlled Transistors

Advanced Electronic Materials, EarlyView.
Compact circuits based on contact‐controlled transistors are well‐suited to unsupervised thermal management, sensitive temperature measurement, or temperature‐stable current references. Demonstrated on flexible microcrystalline silicon and supported by simulation, the approach does not require supply voltage regulation, remains manufacturable across ...
Eva Bestelink, Jean‐Charles Fustec, Olivier de Sagazan, Rogier Fransen, George Bairaktatis, Emmanuel Jacques, Radu A. Sporea   +6 more
wiley   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Finite Boolean Algebras for Solid Geometry using Julia's Sparse Arrays

, 2019
The goal of this paper is to introduce a new method in computer-aided geometry of solid modeling. We put forth a novel algebraic technique to evaluate any variadic expression between polyhedral d-solids (d = 2, 3) with regularized operators of union ...
DiCarlo, Antonio, Onofri, Elia, Paoluzzi, Alberto, Scorzelli, Giorgio, Shapiro, Vadim   +4 more
core  

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 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