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.

翻译：性质预测在材料发现中扮演着重要角色。作为最终开发材料科学基础模型的初步步骤，我们提出了一种名为MHG-GNN的新型自编码器，该模型将图神经网络（GNN）与分子超图文法（MHG）相结合。在多种材料的不同性质预测任务上的实验结果表明，MHG-GNN具有广阔的应用前景。