论文标题
域信息图神经网络：量子化学案例研究
Domain-informed graph neural networks: a quantum chemistry case study
论文作者
论文摘要
我们探索了不同的策略，将先前的领域知识整合到深神经网络（DNN）的设计中。我们专注于图形神经网络（GNN），其用例是估计表示为图的化学系统（分子和晶体）的势能。我们将域知识的两个要素集成到GNN的设计中，以限制和正规化其学习，以提高准确性和泛化。首先，关于原子之间存在不同类型的关系（化学键）的知识用于调节GNN中的节点的相互作用。其次，使用简单的多任务范式将一些物理数量相关性的知识用于将学习的特征限制为更高的物理相关性。我们通过将它们应用于两个依赖不同机制来传播节点和更新节点状态的信息的架构来证明我们的知识集成的一般适用性。
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.
