@inproceedings{
kishimoto2023mhggnn,
title={{MHG}-{GNN}: Combination of Molecular Hypergraph Grammar with Graph Neural Network},
author={Akihiro Kishimoto and Hiroshi Kajino and Hirose Masataka and Junta Fuchiwaki and Indra Priyadarsini and Lisa Hamada and Hajime Shinohara and Daiju Nakano and Seiji Takeda},
booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
year={2023},
url={https://openreview.net/forum?id=IC7b3EQ7wB}
}
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.
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