Cheminformatics Modeling of Amine Solutions for Assessing their CO₂ Absorption Properties

As stricter regulations on CO₂ emissions are adopted worldwide, identifying efficient chemical processes to capture and recycle CO₂ is of critical importance for industry. The most common process known as amine scrubbing suffers from the lack of available amine solutions capable of capturing CO₂ efficiently. Tertiary amines characterized by low heats of reaction are considered good candidates but their absorption properties can significantly differ from one analogue to another despite high structural similarity. Herein, after collecting and curating experimental data from the literature, we have built a modeling set of 41 amine structures with their absorption properties. Then we analyzed their chemical composition using molecular descriptors and non-supervised clustering. Furthermore, we developed a series of quantitative structure-property relationships (QSPR) to assess amines' CO₂ absorption properties from their structural characteristics. These models afforded reasonable prediction performances (e. g., Q²_LOO =0.63 for CO₂ absorption amount) even though they are solely based on 2D chemical descriptors and individual machine learning techniques (random forest and neural network). Overall, we believe the chemical analysis and the series of QSPR models presented in this proof-of-concept study represent new knowledge and innovative tools that could be very useful for screening and prioritizing hypothetical amines to be synthesized and tested experimentally for their CO₂ absorption properties.