We explore different strategies to integrate prior domain knowledge into the design of a deep neural network (DNN). We focus on graph neural networks (GNN), with 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 (chemical bonds) between atoms is used to modulate the interaction of nodes in the GNN. 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 paradigm. We demonstrate the general applicability of our knowledge integrations by applying them to two architectures that rely on different mechanisms to propagate information between nodes and to update node states.
翻译:我们探索了将先前的域知识纳入深神经网络设计的不同战略。我们侧重于图形神经网络(GNN),用图表来估计化学系统(分子和晶体)的潜在能量。我们将域知识的两个要素纳入了GNN的设计,以限制和规范其学习,实现更高的准确性和概括性。首先,关于原子之间存在不同类型关系(化学联系)的知识被用来调节GNN的节点的互动。第二,利用一些物理数量的相关知识,用简单的多任务模式来限制所学到的物理关联性。我们通过将这些知识应用到依靠不同机制传播节点间信息和更新节点状态的两个结构中来表明我们知识整合的普遍适用性。