A review of ionic liquids and deep eutectic solvents design for CO2 capture with machine learning

Abstract

Ionic liquids (ILs) and deep eutectic solvents (DESs) are regarded as the next generation solvents for carbon capture which consist of cations and anions. Thousands of combinations of cations and anions can lead to varied properties of ILs/DESs, which makes it difficult to screen such ILs/DESs for CO2 in experiments. Computer-aided molecular design (CAMD) saves time and cost by reversing the search for the structure of ILs that are suitable for carbon capture. Compared with other thermodynamic models, machine learning (ML) models have the advantages of efficiency and accuracy in CAMD; hence, the number of studies on the application of ML models in the field of CAMD is growing each year. In this paper, a concise review of the application of ML to ILs/DESs-based CO2 capture technology is provided. The development process of ML models in (1) the prediction of the properties of ILs/DESs using their structure; and (2) the prediction of the carbon capture effect using process parameters is discussed. Perspectives on future research directions are proposed and key challenges are identified for screening suitable ILs/DESs using the capture effectiveness of a specific carbon capture process as an evaluation criterion.