Novel Strategy of Machine Learning for Predicting Henry’s Law Constants of CO2 in Ionic Liquids

The Henry's law constant (HLC) of CO2 is an important parameter to characterize its absorption by solvents. However, the HLC data is incomplete in the NIST ionic liquid (IL) Thermo database. In this work, molecular dynamics (MD) simulations were used to accurately calculate the HLC of CO2 in ILs. Then, machine learning (ML) and artificial neural networks were combined to learn from limited data to rapidly expand the database. Three rapid HLC prediction models were established by cross-validation and grid search based on rigorous data sets of IL's structure, temperature, and HLC. The determination coefficient, mean absolute error, and mean square error of the optimal multilayer perceptron model were 0.9817, 0.3023, and 0.2104, respectively. Compared with the reported models, the prediction model established in this work has better versatility and higher prediction accuracy. The HLC matrix of CO2 in 306 ILs was completed, which proves the great potential and significance of the MD–ML method in the expansion of green solvent database. Finally, the structure–activity relationship of CO2 absorption by the binary IL mixtures was studied.