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
D. Nakano
Seiji Takeda

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.
View on arXivComments on this paper