Tag: Graph Neural Networks

A new study shows that deep learning models can accurately predict the mechanical strength of biodegradable magnesium alloys while clearly ...

Researchers at Central South University have developed a cluster-based domain adaptation method that aligns only semantically similar portions of source ...

A new multimodal AI framework called MEGH-DDI combines chemical language models with graph neural networks to predict drug–drug interactions with ...

Researchers at Liaoning Normal University have developed MCC-ED, a multi-module AI framework that combines contextual interaction, closed-loop graph fusion, and ...

Researchers in Zhengzhou have built a graph neural network that encodes the measured noise of superconducting quantum processors to correct ...

A new deep learning architecture called Hessian Interatomic Potentials predicts the full molecular Hessian directly, delivering one to two orders ...

A systematic study finds that large language models consistently fail to learn spatial coordinate information in materials property prediction, with ...

A hybrid systematic review and experimental benchmark of five deep learning architectures reveals which neural networks can realistically detect and ...

A rigorous benchmark of six machine learning families across eight Ahmedabad monitoring stations finds that a hybrid GCN-LSTM model only ...

Researchers combined a graph autoencoder, an attention-equipped graph convolutional network, and a slime-mould-inspired optimizer to locate hidden faults in smart ...