Results 171 to 180 of about 11,198,121 (288)

Preface to the theme issue 'physics-informed machine learning and its structural integrity applications'. [PDF]

Philos Trans A Math Phys Eng Sci, 2023
Zhu SP, De Jesus AMP, Berto F, Michopoulos JG, Iacoviello F, Wang Q.   +5 more
europepmc   +1 more source

Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models

Advanced Materials, EarlyView.
Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng, Min Hong, Zhiquan Zeng, Chuanyu Liu, Qian Wang, Melissa Jane Meadowcroft, Vlad Badilita, Carlos Miguel Costa, Senentxu Lanceros‐Méndez, Ying Li, Chenyao Ma, Weibo Gong, Surendra Kumar Martha, Aloysius Soon, Piao Ma, Di Zhang, Teguh Ariyanto, Sudaryanto, Hiroshi Kakinuma, Jie Zhao, Jiayu Peng, Pengfei Ou, Jun Lu, Shin‐ichi Orimo, Hao Li   +24 more
wiley   +1 more source

On-demand growth of semiconductor heterostructures guided by physics-informed machine learning. [PDF]

Sci Adv
Shen C, Li Y, Zhan W, Pan S, Lin F, Xin K, Cong H, Xu C, Cheng X, Liu R, Ni Z, Jin C, Xu B, Chen S, Wei Z, Xue C, Wang Z, Zhao C.   +17 more
europepmc   +1 more source

Advances in Bio‐Integrated Edge Computing Systems for Smart and Connected Health

Advanced Materials, EarlyView.
This review explores advances in bio‐integrated edge computing for healthcare, focusing on co‐designing computing, sensing, and energy strategies to overcome integration and power challenges for optimized, decentralized operation. ABSTRACT Recent advances in bio‐integrated healthcare technologies have enabled continuous, high‐resolution monitoring and ...
Gyuwon Kim, Seungwon Yoo, Tae Woog Kang, Yunuo Huang, Woon‐Hong Yeo   +4 more
wiley   +1 more source

A Simple Descriptor for Solid‐State Ionic Conductors Across Chemistries

Advanced Materials, EarlyView.
Researchers develop a simple and transferable descriptor, distance contraction, for solid‐state ion migration across Li‐, Na‐, K‐, and Mg‐ion conductors and diverse anion chemistries. By capturing the maximum local contraction experienced by the migrating ion, the descriptor enables rapid materials screening and unifies diverse ion‐transport mechanisms.
Ramanuja Srinivasan Saravanan, Charles E. Schwarz, Felix Adams, Kiyan Amirian, Yifei Mo   +4 more
wiley   +1 more source

Integrated Physics-Informed Machine Learning Framework for Structural Damage Detection, Localization, and Severity Classification. [PDF]

Sensors (Basel)
Wang Z, Jahanshahi MR.
europepmc   +1 more source

Lagrangian large eddy simulations via physics-informed machine learning. [PDF]

Proc Natl Acad Sci U S A, 2023
Tian Y, Woodward M, Stepanov M, Fryer C, Hyett C, Livescu D, Chertkov M.   +6 more
europepmc   +1 more source

Biomass DNA: An Emerging Building Block for Advanced Materials

Advanced Materials, EarlyView.
Biomass DNA is rapidly emerging from its traditional role as a genetic blueprint to serve as a versatile abundant, highly programmable polymeric building block. This review highlights the transformation of macroscopic bulk biomass DNA into next generation of functional materials, spanning from dynamic hydrogels, sustainable bioplastics, and high ...
Yuwei Hu, Itamar Willner, Li Niu
wiley   +1 more source

Physics-Informed Machine Learning for Electromagnetic and Multiphysics Modeling

Accurate and efficient modeling of complex electromagnetic and multiphysics systems remains computationally demanding due to high-dimensional parameter spaces, complex boundaries, multi-scale phenomena, and numerical stability requirements.
Qi, Shutong
core  

Physics-Informed Machine Learning Correction of Variational Quantum Eigensolver Energies for Molecular Ground States. [PDF]

ACS Omega
Sree KCS, Shankar V.
europepmc   +1 more source