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.
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