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 S ⋅ Lisa Hamada ⋅ Hajime Shinohara ⋅ Daiju Nakano ⋅ Seiji Takeda

Keywords: Autoencoder molecular hypergraph grammar material discovery Graph neural network property prediction autoencoder graph neural network

Project Page [ OpenReview]

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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