Interpretable Prediction of CO2 Solubility of Ionic Liquids Based on Graph Convolution Neural Network
In order to build an accurate prediction model of CO2 solubility of ionic liquids,considering the problems existing in traditional models,such as complex descriptor calculation,high cost,difficult related structure and properties,and insufficient structural feature extraction,a prediction model APGCN-XGBoost combining graph convolution neural network and XGBoost with attention mechanism was proposed.The analysis results of CO2 solubility data of 9 897 groups of ionic liquids show that the prediction performance of the proposed APGCN-XGBoost model is better than that of the traditional molecular fingerprint model and graph convo-lutional neural network model.In addition,the APGCN-XGBoost model learned the characteristic information and molecular non-local information of each atom and structure in ionic liquids,which can be used not only to predict the properties,but also to explore the rela-tionship between the chemical structures and properties,that is,to screen out the structural information of ionic liquids that is important for solubility prediction through the interpretation of the model,thus realizing the computer-aided design and screening of ideal ionic liquids in the process of CO2 capture.
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