ibm-research/materials.mhg-ged
Feature Extraction โข Updated โข 74 โข 4
A new autoencoder combining graph neural networks and Molecular Hypergraph Grammar is introduced for material property prediction, showing promise in diverse tasks.
Property prediction plays an important role in material discovery. As an initial step to eventually develop a foundation model for material science, we introduce a new autoencoder called the MHG-GNN, which combines graph neural network (GNN) with Molecular Hypergraph Grammar (MHG). Results on a variety of property prediction tasks with diverse materials show that MHG-GNN is promising.
Get this paper in your agent:
hf papers read 2309.16374 curl -LsSf https://hf.co/cli/install.sh | bash No dataset linking this paper
No Collection including this paper