MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network

Akihiro Kishimoto, Hiroshi Kajino, Masataka Hirose, Junta Fuchiwaki, Indra Priyadarsini +4 more
9/28/2023
cs.LG

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Abstract

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.

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Cite this paper

@article{kishimoto2023mhggnn,
  title  = {MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network},
  author = {Akihiro Kishimoto and Hiroshi Kajino and Masataka Hirose and Junta Fuchiwaki and Indra Priyadarsini and Lisa Hamada and Hajime Shinohara and Daiju Nakano and Seiji Takeda},
  year   = {2023},
  eprint = {2309.16374},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.16374v1}
}

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