Results 111 to 120 of about 1,903,201 (339)

A review on the applications of graph neural networks in materials science at the atomic scale

Materials Genome Engineering Advances
In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from ...
Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong   +4 more
doaj   +1 more source

Graph Neural Network for Traffic Forecasting: A Survey [PDF]

Expert systems with applications, 2021
Weiwei Jiang, Jiayun Luo
semanticscholar   +1 more source

Functional Materials for Environmental Energy Harvesting in Smart Agriculture via Triboelectric Nanogenerators

Advanced Functional Materials, EarlyView.
This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva, Giulio Fatti, Emanuel Carlos, Guilherme Ferreira, Sumita Goswami, Suman Nandy, Luiz H. C. Mattoso, Elvira Fortunato, Caio G. Otoni, Rodrigo Martins   +9 more
wiley   +1 more source

Graph neural network structural limitation for thermal simulation and architecture optimization through rating system

Advanced Modeling and Simulation in Engineering Sciences
Graph neural networks are well suited for physics based simulation. Among other features, graphs can accurately represent thermal effects, with energy conservation operating on the nodes (vertices) and heat flow coursing through edges.
Pierre Hembert, Chady Ghnatios, Julien Cotton, Francisco Chinesta   +3 more
doaj   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

GNEA: A Graph Neural Network with ELM Aggregator for Brain Network Classification

Complexity, 2020
Brain networks provide essential insights into the diagnosis of functional brain disorders, such as Alzheimer’s disease (AD). Many machine learning methods have been applied to learn from brain images or networks in Euclidean space.
Xin Bi, Zhixun Liu, Yao He, Xiangguo Zhao, Yongjiao Sun, Hao Liu   +5 more
doaj   +1 more source

Gated Graph Recurrent Neural Networks

IEEE Transactions on Signal Processing, 2020
Graph processes exhibit a temporal structure determined by the sequence index and and a spatial structure determined by the graph support. To learn from graph processes, an information processing architecture must then be able to exploit both underlying structures.
Luana Ruiz, Fernando Gama, Alejandro Ribeiro   +2 more
openaire   +2 more sources

A 3D Biofabricated Disease Model Mimicking the Brain Extracellular Matrix Suitable to Characterize Intrinsic Neuronal Network Alterations in the Presence of a Breast Tumor Disseminated to the Brain

Advanced Functional Materials, EarlyView.
A 3D disease model is developed using customized hyaluronic‐acid‐based hydrogels supplemented with extracellular matrix (ECM) proteins resembling brain ECM properties. Neurons, astrocytes, and tumor cells are used to mimic the native brain surrounding.
Esra Türker, Mateo S. Andrade Mier, Jessica Faber, Mike Friedrich, Zan Lamberger, Jeannette Weigelt, Christian Stigloher, Nicoletta Murenu, Natascha Schaefer, Jörg Tessmar, Gregor Lang, Silvia Budday, Katrin G. Heinze, Antje Appelt‐Menzel, Pamela L. Strissel, Reiner Strick, Carmen Villmann   +16 more
wiley   +1 more source

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

, 2016
This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary ...
Bresson, Xavier, Defferrard, Michaël, Seo, Youngjoo, Vandergheynst, Pierre   +3 more
core  

Ice Lithography: Recent Progress Opens a New Frontier of Opportunities

Advanced Functional Materials, EarlyView.
This review focuses on recent advancements in ice lithography, including breakthroughs in compatible precursors and substrates, processes and applications, hardware, and digital methods. Moreover, it offers a roadmap to uncover innovation opportunities for ice lithography in fields such as biological, nanoengineering and microsystems, biophysics and ...
Bingdong Chang, Gwendoline A. E. Anand, Rubaiyet Haque, Dylan A. Chiaro, Mona Sharma, Andrew R. Williams, Frank Niklaus, Daryl W. Yee, Gavin M. King, Anpan Han   +9 more
wiley   +1 more source