Environmental Chemistry Letters

, Volume 17, Issue 3, pp 1397–1404 | Cite as

Prediction of CO2 absorption by physical solvents using a chemoinformatics-based machine learning model

  • Hao Li
  • Dan Yan
  • Zhien ZhangEmail author
  • Eric Lichtfouse
Original Paper


The rising atmospheric CO2 level is partly responsible for global warming. Despite numerous warnings from scientists during the past years, nations are reacting too slowly, and thus, we will probably reach a situation needing rapid and effective techniques to reduce atmospheric CO2. Therefore, advanced engineering methods are particularly important to decrease the greenhouse effect, for instance, by capturing CO2 using solvents. Experimental testing of many solvents under different conditions is necessary but time-consuming. Alternatively, modeling CO2 capture by solvents using a nonlinear fitting machine learning is a rapid way to select potential solvents, prior to experimentation. Previous predictive machine learning models were mainly designed for blended solutions in water using the solution concentration as the main input of the model, which was not able to predict CO2 solubility in different types of physical solvents. To address this issue, here, we developed a new descriptor-based chemoinformatics model for predicting CO2 solubility in physical solvents in the form of mole fraction. The input factors include organic structural and bond information, thermodynamic properties, and experimental conditions. We studied the solvents from 823 data, including methanol (165 data), ethanol (138), n-propanol (98), n-butanol (64), n-pentanol (59), ethylene glycol (52), propylene glycol (54), acetone (51), 2-butanone (49), ethylene glycol monomethyl ether (46 data), and ethylene glycol monoethyl ether (47), using artificial neural networks as the machine learning model. Results show that our descriptor-based model predicts the CO2 absorption in physical solvents with generally higher accuracy and low root-mean-squared errors. Our findings show that using a set of simple but effective chemoinformatics-based descriptors, intrinsic relationships between the general properties of physical solvents and their CO2 solubility can be precisely fitted with machine learning.


Chemoinformatics Greenhouse gas CO2 Absorption Solubility Physical solvent Chemical descriptors Prediction Machine learning Artificial neural network (ANN) 


Supplementary material

10311_2019_874_MOESM1_ESM.docx (120 kb)
Supplementary material 1 (DOCX 121 kb)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Chemistry and the Institute for Computational and Engineering SciencesThe University of Texas at AustinAustinUSA
  2. 2.Shenzhen Environmental Science and Technology Engineering LaboratoryTsinghua-Berkeley Shenzhen Institute, Tsinghua UniversityShenzhenChina
  3. 3.William G. Lowrie Department of Chemical and Biomolecular EngineeringThe Ohio State UniversityColumbusUSA
  4. 4.CEREGE, Aix-Marseille UnivColl de France, CNRS, INRA, IRDAix-en-ProvenceFrance

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