Results 101 to 110 of about 5,698,498 (295)

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

Advanced Science, EarlyView.
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao   +5 more
wiley   +1 more source

Sessionbased graph convolutional recurrent neural networks recommendation model

四川大学学报. 自然科学版, 2021
The session based recommendation is a subtask of recommendation system, which addresses the recommendation problem about anonymous users. Although the existing methods with the graph neural network for recommendation have achieved good results, which are
CAO Wan-Ping, ZHOU Gang, CHEN Li, CUI Lan-Lan   +3 more
doaj  

Graph Neural Network, ChebNet, Graph Convolutional Network, and Graph Autoencoder: Tutorial and Survey

This is a tutorial paper on graph neural networks including ChebNet, graph convolutional network, graph attention network, and graph autoencoder. It starts with Laplacian of graph, graph Fourier transform, and graph convolution. Then, it is explained how
Benyamin Ghojogh, Ali Ghodsi
core   +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

3D convolutional neural network for schizophrenia detection using as EEG-based functional brain network [PDF]

Schizophrenia (ScZ) is a chronic mental disorder affecting the function of the brain, which causes emotional, social, and cognitive problems.
Wen, Peng, Shen, Mingkan, Li, Yan, Song, Bo   +3 more
core   +1 more source

Construction of Sabatier Volcanoes for CO2 Hydrogenation to C1‐2 Oxygenates Using Data‐Efficient Machine Learning

Advanced Science, EarlyView.
A new data‐efficient framework combining DFT calculations, a neural network model, and automated graph analysis of catalytic reaction networks is proposed and applied to CO2 hydrogenation on transition metal nanoparticles. The analysis shows how efficient C2 oxygenate production requires a balance between CHx formation, C–C coupling, protonation, and ...
Mikhail V. Polynski, Sergey M. Kozlov
wiley   +1 more source

Dual‐Module Near‐Infrared Fluorophores Discovery System via Knowledge Transfer

Advanced Science, EarlyView.
This study presents a dual‐module deep learning system for the design of near‐infrared (NIR) fluorophores. A large molecular library is generated and analyzed, leading to the suggestions of promising candidates. The effectiveness of the system is further validated through the synthesis, characterization, and in vivo imaging, demonstrating its potential
Yixin Zhu, Xia Ling, Xianhe Zhang, Chuanjiang Jian, Leilei Shi, Wentao Song, Xiaonan Wang, Bin Liu   +7 more
wiley   +1 more source

Barron Space for Graph Convolutional Neural Networks

Journal of Fourier Analysis and Applications
Graph convolutional neural network (GCNN) operates on graph domain and it has achieved a superior performance to accomplish a wide range of tasks. In this paper, we introduce a Barron space of functions on a compact domain of graph signals. We prove that the proposed Barron space is a reproducing kernel Banach space, it can be decomposed into the union
Seok-Young Chung, Qiyu Sun
openaire   +2 more sources

Enhancing Super‐Resolution Spatial Transcriptomics Data by Transfer Learning

Advanced Science, EarlyView.
SpotZoomer employs a transfer‐learning‐based strategy to enhance the resolution of Visium data by leveraging available high‐resolution priors. The resulting super‐resolved maps enable sharper delineation of cell boundaries and more precise inference of cell–cell communication patterns that would otherwise remain obscured at native resolution.
Xiaoyu Li, Lihua Zhang, Wenwen Min
wiley   +1 more source

Learning meshless parameterization with graph convolutional neural networks

, 2023
International audienceThis paper proposes a deep learning approach for parameterizing an unorganized or scattered point cloud in R 3 with graph convolutional neural networks.
Giannelli, Carlotta, Imperatore, Sofia, Scholz, Felix, Mantzaflaris, Angelos   +3 more
core   +1 more source