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Found 3 papers, 2 papers with code
CrysMMNet: Multimodal Representation for Crystal Property Prediction
In this work, we leverage textual descriptions of materials to model global structural information into graph structure and learn a more robust and enriched representation of crystalline materials.
CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials
To leverage these untapped data, this paper presents CrysGNN, a new pre-trained GNN framework for crystalline materials, which captures both node and graph level structural information of crystal graphs using a huge amount of unlabelled material data.
Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs
Then we propose to use graph convolution on the line graph of a hypergraph.
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