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Found 2 papers, 2 papers with code
Crystal Graph Neural Networks for Data Mining in Materials Science
This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset based on a theoretical materials database.
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OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials Science
We introduce a large-scale dataset of quantum-mechanically calculated properties of crystalline materials for graph representation learning that contains approximately 900k entries (OQM9HK).
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