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

Submission Track: Papers

Submission Category: AI-Guided Design

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

TL;DR: Autoencoder which combines GNN and molecular hypergraph grammar with an application to material discovery

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.

Submission Number: 16