Results 181 to 190 of about 5,175,662 (301)

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

Near-100% site utilization of single atoms for efficient electrocatalysis. [PDF]

Nat Commun
Wei X, An M, Tang X, Wei J, Yan P, Qiu Y, Jiang D, Asakura Y, Song X, Asahi T, Zhu C, Yamauchi Y.   +11 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

Self-contracting, battery-free triboelectric wound healing strip with strong wet adhesion. [PDF]

Nat Commun
Meng X, Xiao X, Jeon S, Cho DS, Zhang K, Kwon YH, Mo H, Park Y, Park BJ, Kim D, Pang F, Kim S, Choi BO, Dai K, Kim SW.   +14 more
europepmc   +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

Micro-Texturized and Ultra-Soft Dry Electrode for Motion-Artifact Tolerant and Long-Term Stable Wearable Electrophysiological Monitoring. [PDF]

Small
Kim SM, Jang HJ, Kim KH, Hwang YJ, Lee YB, Jeong J, Jung S, Jeong E, Han DW, Sung ES, Seo MH.   +10 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

Multi-Scale Numerical Simulation of Fatigue Crack Propagation Mechanisms in the Heat-Affected Zone of AH36 Steel Welds. [PDF]

Materials (Basel)
Shen C, Fu Y, Zhao W, Yang J.
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Edge polaritons at metal-insulator boundaries in a phase separated correlated oxide. [PDF]

Nat Commun
Luo W, Bercher A, Dominguez C, Del Valle J, Teyssier J, Taboada-Gutiérrez J, Kuzmenko AB.   +6 more
europepmc   +1 more source