Abstract
In this study, a total of 160 experimental data points of Henry's law constant of CO2 in 32 imidazole ionic liquids (ILs) were collected, with the temperatures range from 283 to 350 K. Herein intuitive and explanatory descriptors related to Henry's law constant (HLC) were suggested from the 2D structural features of the ILs according to experimental experience and laws. Temperature was used as another variable due to its significant effect on Henry's law constant. Three machine learning methods were used to construct models to fast predict the HLC based on suggested descriptors. Multi-layer Perceptrowas mainly used to build the model and compared with the results of Random forest and Multiple Linear Regression after investigating the outliers and variable selection. In addition, if only one data point was left at a similar temperature and the reduced dataset was also used to build models in the same procedure, the results were not as good as those of the full dataset but still satisfactory.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China [No. 21576071; 21776061]; the Foundation of International Science and Technology Cooperation of Henan Province [No. 162102410012]; the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry [No. 20091001] and the program for Science & Technology Innovation Team in Universities of Henan Province [No.19IRTSTHN029]
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Wu, T., Li, WL., Chen, MY. et al. Prediction of Henry's law constants of CO2 in imidazole ionic liquids using machine learning methods based on empirical descriptors. Chem. Pap. 75, 1619–1628 (2021). https://doi.org/10.1007/s11696-020-01415-8
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DOI: https://doi.org/10.1007/s11696-020-01415-8