Results 271 to 280 of about 109,103 (311)

Terahertz graphene-based tunable capacitance metamaterials with ultra-high amplitude modulation depth. [PDF]

Light Sci Appl
Guo ZJ, Wu GB.
europepmc   +1 more source

Named Entity Recognition Models for Machine Learning Interatomic Potentials: A User‐Centric Approach to Knowledge Extraction from Scientific Literature

Advanced Intelligent Discovery, EarlyView.
Named entity recognition pipeline for knowledge extraction from scientific literature. Machine learning interatomic potential (MLIP) is an emerging technique that has helped achieve molecular dynamics simulations with unprecedented balance between efficiency and accuracy. Recently, the body of MLIP literature has been growing rapidly, which propels the
Bowen Zheng, Grace X. Gu
wiley   +1 more source

Efficient phase shift in metamaterial spoof surface plasmon polaritons waveguides. [PDF]

Sci Rep
Mazdouri B, Mirzavand R.
europepmc   +1 more source

Piezoelectric Origami Metamaterials for Enhanced Handwriting Recognition and Trajectory Tracking

Advanced Intelligent Discovery, EarlyView.
This study introduces origami metamaterials inspired by Kresling piezoelectric generators to enhance biometric authentication and handwriting trajectory recognition. Overcoming sensor limitations in conventional devices, the design enables multichannel data acquisition with fewer sensors, utilizing machine learning to accurately identify content ...
Yinzhi Jin, Ting Tan, Zhimiao Yan
wiley   +1 more source

Electromagnetically Coupled Resonant Face-to-Face Double-Layer Metamaterial for Highly Sensitive THz Impedance Spectroscopy. [PDF]

Adv Sci (Weinh)
Sengupta R, Khand H, Sarusi G.
europepmc   +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

Bioinspired polypropylene-based functionally graded materials and metamaterials modeling the mistletoe-host interface. [PDF]

Beilstein J Nanotechnol
Rojas González LM, Ghavidelnia N, Eberl C, Mylo MD.   +3 more
europepmc   +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

Soft multistable magnetic-responsive metamaterials. [PDF]

Sci Adv
Greenwood TE, Elder B, Hasan MN, Anklam J, Lee S, Teng J, Wang P, Kong YL.   +7 more
europepmc   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source