CGCNN
PulseAugur coverage of CGCNN — every cluster mentioning CGCNN across labs, papers, and developer communities, ranked by signal.
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Graph Neural Networks Enhance Material Property Prediction for High-Entropy Oxides
Researchers have explored the use of graph neural networks (GNNs) for predicting the properties of high-entropy perovskite oxides (HEPOs), a complex class of materials. The study investigated ordered-to-disordered trans…
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Machine learning framework accelerates discovery of new photocatalysts
Researchers have developed MatCreatioNN, a machine learning framework designed to accelerate the discovery of photocatalysts for environmental applications. This system combines reinforcement learning for generating met…
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New GNN models advance crystal property prediction and molecular simulations
Researchers have developed new graph neural network (GNN) models for predicting crystal properties. One approach, CPGN, uses a multi-scale GNN to jointly learn atomic, bond, and coordination-polyhedron representations, …