Molecular Modeling of Tertiary Amines to
Forecast their CO2 Absorption Properties
Melaine Kuenemann and Denis Fourches
Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh,
Identifying novel amines to absorb and recycle CO 2 is of critical importance for industry. This
process known as amine scrubbing suffers from the lack of available amines capable of low heat of reaction, fast absorption rate, and high capacity for CO 2 . 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, to forecast the CO2 absorption properties of tertiary amines, we developed a series of quantitative structure-property relationships (QSPR) solely based on computed chemical descriptors, three machine learning techniques, and a predictive modeling workflow. After collecting and curating more than 40 amine structures, we have built a modeling set of 25 amines with their absorption properties. In this presentation, we discuss the prediction performances of the QSPR models generated from that modeling set and how to use these models to screen and design novel amines. We believe the series of models presented in this study represent innovative tools for screening and prioritizing novel amines to be synthesized and tested experimentally for their CO 2 absorption properties.