论文标题
迈向可解释的消息传递网络，以预测金属有机框架中的二氧化碳吸附
Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks
论文作者
论文摘要
金属有机框架（MOF）是纳米多孔材料，可用于从化石燃料发电厂的排气中捕获二氧化碳以减轻气候变化。在这项工作中，我们设计和训练通过神经网络（MPNN）的消息预测MOF中的模拟CO $ _2 $吸附。为了提供有关MOF的哪些子结构对于预测很重要的见解，我们将软注意机制引入读取函数，以量化节点表示对图表表示的贡献。我们研究了稀疏注意的不同机制，以确保仅确定最相关的子结构。
Metal-organic framework (MOFs) are nanoporous materials that could be used to capture carbon dioxide from the exhaust gas of fossil fuel power plants to mitigate climate change. In this work, we design and train a message passing neural network (MPNN) to predict simulated CO$_2$ adsorption in MOFs. Towards providing insights into what substructures of the MOFs are important for the prediction, we introduce a soft attention mechanism into the readout function that quantifies the contributions of the node representations towards the graph representations. We investigate different mechanisms for sparse attention to ensure only the most relevant substructures are identified.
