Effectively predicting molecular interactions has the potential to accelerate
molecular dynamics by multiple orders of magnitude and thus revolutionize
chemical simulations. Graph neural networks (GNNs) have recently shown great
successes for this task, overtaking classical methods based on fixed molecular
kernels. However, they still appear very limited from a theoretical
perspective, since regular GNNs cannot distinguish certain types of graphs. In
this work we close this gap between theory and practice. We show that GNNs with
directed edge embeddings and two-hop message passing are indeed universal
approximators for predictions that are invariant to translation, and
equivariant to permutation and rotation. We then leverage these insights and
multiple structural improvements to propose the geometric message passing
neural network (GemNet). We demonstrate the benefits of the proposed changes in
multiple ablation studies. GemNet outperforms previous models on the COLL,
MD17, and OC20 datasets by 34%, 41%, and 20%, respectively, and performs
especially well on the most challenging molecules. Our implementation is
available online.

本研究介绍了一种新型的 Geometric Message Passing Neural Network（GemNet）结构，采用了定向边嵌入和双跳消息传递，展现出图神经网络在预测分子动力学方面的强大效果和广阔前景。