Results 241 to 250 of about 4,082,283 (306)

Hyperbolic Kernel Graph Neural Networks for Neurocognitive Decline Analysis From Multimodal Brain Imaging. [PDF]

IEEE Trans Pattern Anal Mach Intell
Yang M, Sun Y, Wang Q, Bozoki A, Kohi M, Liu M.   +5 more
europepmc   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

HeartSimSage: Attention-Enhanced Graph Neural Networks for Accelerating Cardiac Mechanics Modeling. [PDF]

J Comput Phys
Shi L, Chen Y, Vedula V.
europepmc   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Explainable artificial intelligence reveals divergent learning in pharmacophore-based hierarchical pooling graph neural networks. [PDF]

Sci Rep
Urrutia MJT, Mastropietro A, Bajorath J.
europepmc   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Multimodal connectivity-based cortical segmentation with graph neural networks. [PDF]

Front Neurosci
Łabiak A, Kazi A, Pellegrini C, Frau-Pascual A, Aganj I.   +4 more
europepmc   +1 more source

Accumulated local effects and graph neural networks for link prediction. [PDF]

Sci Rep
Kaczyńska P, Sienkiewicz J, Ślęzak D.
europepmc   +1 more source

Broadening Hard‐Magnet Discovery Beyond Symmetry Constraints via Unified Effective Anisotropy

Advanced Science, EarlyView.
A unified effective‐anisotropy descriptor (Keff) extends hard‐magnet screening across all seven crystal systems, beyond the uniaxial restriction of conventional searches. Machine‐learning screening of 9320 known ferromagnets and diffusion‐model generation together yield 38 rare‐earth‐free or ‐lean candidates with DFT‐validated magnetic hardness (κ > 1),
Hojae Kim, Hyeondeok Shin, Kyungju Nam, Seunghyo Noh, Donghwi Kim, Yousung Jung   +5 more
wiley   +1 more source

A V2X communication resource allocation method based on graph neural networks and deep reinforcement learning. [PDF]

Sci Rep
Yu W, Yang X, Yu S.
europepmc   +1 more source