Poster
in
Workshop: AI for Accelerated Materials Design (AI4Mat-2023)
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network
Akihiro Kishimoto · Hiroshi Kajino · Hirose Masataka · Junta Fuchiwaki · Indra Priyadarsini · Lisa Hamada · Hajime Shinohara · Daiju Nakano · Seiji Takeda
Keywords: [ property prediction ] [ Graph neural network ] [ material discovery ] [ molecular hypergraph grammar ] [ Autoencoder ]
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.