A data‐driven optimization approach for the molecular design of <scp> CO ₂ </scp> capture ionic liquids mixtures

Abstract CO emissions into the atmosphere have become a global concern in recent years. The amount of CO generated in post‐combustion processes has attracted the attention of the international scientific community. Some processes use alkanolamines to absorb CO; however, their volatility increases the process cost. An alternative to this limitation is the use of ionic liquids (ILs) as solvents in the CO absorption process. An important characteristic of ILs is their extremely low vapour pressure, making them practically non‐volatile. In this work, molecular dynamics simulation (MDS) was employed to calculate the absorption capacity of an IL mixture, and a design of experiments approach guided by Bayesian optimization (BO) was used to find an optimal IL mixture that maximizes the amount of CO captured. The applied methodology helps to reduce the number of numerical experiments and, consequently, computation time. The IL mixture analyzed was [BMIM][NTF] and [EMIM][SCN] (1‐ethyl‐3‐methylimidazolium bis(trifluoromethylsulfonyl)imide and 1‐ethyl‐3‐methylimidazolium thiocyanate) due to their known effectiveness as absorbents. With only 12 simulations, the composition of the IL mixture that achieve maximum CO absorption was determined. The results obtained encourage further exploration of other IL mixtures that may absorb greater amounts of CO.