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AI Model Restores Lost Bridge Data to Improve Structural Safety

BiGRU, a deep learning model that enhances data recovery in structural health monitoring, ensuring the reliability of bridge ...

Machine learning workflow enables faster, more reliable organic crystal structure prediction

Prediction of crystal structures of organic molecules is a critical task in many industries, especially in pharmaceuticals ...
Frontiers

Advancing Computational Material Science and Mechanics through Integrated Deep Learning Models

The field of computational materials science has been profoundly transformed by integrating deep learning and other machine learning methodologies. These sophisticated data-driven approaches have ...

Molecules bend, properties change: How nanographene transforms through oxidation

Graphene and its molecular fragments, known as nanographenes, are key materials for next-generation organic electronics due ...
IEEE

Enhancing Graph Structure Learning via Motif-Driven Hypergraph Construction

Abstract: Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on ...
IEEE

Graph Contrastive Learning for Fusion of Graph Structure and Attribute Information

Abstract: Graph Contrastive Learning (GCL) plays a crucial role in multimedia applications due to its effectiveness in analyzing graph-structured data. Existing GCL methods focus on maximizing the ...