Neural message passing on molecular graphs is one of the most promising
methods for predicting formation energy and other properties of molecules and
materials. In this work we extend the neural message passing model with an edge
update network which allows the information exchanged between atoms to depend
on the hidden state of the receiving atom. We benchmark the proposed model on
three publicly available datasets (QM9, The Materials Project and OQMD) and
show that the proposed model yields superior prediction of formation energies
and other properties on all three datasets in comparison with the best
published results. Furthermore we investigate different methods for
constructing the graph used to represent crystalline structures and we find
that using a graph based on K-nearest neighbors achieves better prediction
accuracy than using maximum distance cutoff or the Voronoi tessellation graph.

本文研究在分子图中应用神经信息传递以提高物质形成能预测及其他性质。通过引入边更新网络和各个原子的隐藏状态，该方法在 QM9、材料计划和 OQMD 三个公开数据集上表现出明显的优势。此外，通过比较使用 K 近邻、最大距离截断或泰森多边形图法生成晶体结构图，结果表明使用 K 近邻生成图可以获得更好的预测准确性。