论文标题
$ \ textrm {co} _2 $在Nano-Pores中的预测与图神经网络的吸附
Prediction of $\textrm{CO}_2$ Adsorption in Nano-Pores with Graph Neural Networks
论文作者
论文摘要
我们研究了基于图的卷积神经网络方法，用于预测和排名晶体金属有机框架（MOF）吸附剂的气体吸附性能，以应用于$ \ textrm {co} _2 $的燃烧后捕获中。我们的模型仅基于包含吸附物材料候选物的原子描述的标准结构输入文件。我们构建了新颖的方法论扩展，以匹配具有数百个功能以更高的计算成本构建的古典机器学习模型的预测准确性。我们的方法可以更广泛地应用于以工业规模优化气体捕获过程。
We investigate the graph-based convolutional neural network approach for predicting and ranking gas adsorption properties of crystalline Metal-Organic Framework (MOF) adsorbents for application in post-combustion capture of $\textrm{CO}_2$. Our model is based solely on standard structural input files containing atomistic descriptions of the adsorbent material candidates. We construct novel methodological extensions to match the prediction accuracy of classical machine learning models that were built with hundreds of features at much higher computational cost. Our approach can be more broadly applied to optimize gas capture processes at industrial scale.
