mlsanityData to 13 Oct 2026

MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network

arXiv 2309.16374 ↗First posted 28 Sep 2023cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 30 of its 45 references that are in the map. First posted on arXiv 28 Sep 2023.

Builds on

30 of 45 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2025A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Toolscited by 7
  2. 2024Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINTcited by 2
  3. 2023Improving Molecular Properties Prediction Through Latent Space Fusioncited by 1
  4. 2026MSAlign: Aligning Molecule and Mass Spectra representations for Metabolite Identificationcited by 1