MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural
  Network
Akihiro Kishimoto Hiroshi Kajino Masataka Hirose Junta Fuchiwaki Indra Priyadarsini Lisa Hamada Hajime Shinohara Daiju Nakano Seiji Takeda
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.