Abstract：
We explore different strategies to integrate prior domain knowledge into the design of graph neural networks (GNN). Our study is supported by a use-case of estimating the potential energy of chemical systems (molecules and crystals) represented as graphs. We integrate two elements of domain knowledge into the design of the GNN to constrain and regularise its learning, towards higher accuracy and generalisation. First, knowledge on the existence of different types of relations/graph edges (e.g. chemical bonds in our case study) between nodes of the graph is used to modulate their interactions. We formulate and compare two strategies, namely specialised message production and specialised update of internal states. Second, knowledge of the relevance of some physical quantities is used to constrain the learnt features towards a higher physical relevance using a simple multi-task learning (MTL) paradigm. We explore the potential of MTL to better capture the underlying mechanisms behind the studied phenomenon. We demonstrate the general applicability of our two knowledge integrations by applying them to three architectures that rely on different mechanisms to propagate information between nodes and to update node states. Our implementations are made publicly available. To support these experiments, we release three new datasets of out-of-equilibrium molecules and crystals of various complexities.
摘要：
我们探索了将先验领域知识集成到图神经网络（GNN）设计中的不同策略。我们的研究得到了估计以图形表示的化学系统（分子和晶体）的势能的用例的支持。我们将领域知识的两个元素集成到GNN的设计中，以约束和规范其学习，朝着更高的准确性和泛化方向发展。首先,关于图的节点之间存在不同类型的关系/图边（例如我们的案例研究中的化学键）的知识用于调节它们的相互作用。我们制定和比较两种策略，即专门的消息制作和专门的内部状态更新。第二,使用简单的多任务学习（MTL）范式，将一些物理量的相关性知识用于将学习的特征约束为更高的物理相关性。我们探索了MTL更好地捕获所研究现象背后的潜在机制的潜力。通过将两种知识集成应用于三种架构，我们证明了两种知识集成的普遍适用性，这三种架构依赖于不同的机制在节点之间传播信息并更新节点状态。我们的实现是公开可用的。为了支持这些实验，我们发布了三个不平衡分子和各种复杂晶体的新数据集。
