title: Cheminformatics Modeling of Amine Solutions for Assessing their CO2Absorption Properties.
abstract: As stricter regulations on CO2emissions are adopted worldwide, identifying efficient chemical processes to capture and recycle CO2is of critical importance for industry. The most common process known as amine scrubbing suffers from the lack of available amine solutions capable of capturing CO2efficiently. 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' CO2absorption properties from their structural characteristics. These models afforded reasonable prediction performances (e. g., Q2LOO=0.63 for CO2absorption 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 CO2absorption properties.
authors.affiliation: Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, USA.
authors.forename: Melaine A
authors.initials: MA
authors.lastname: Kuenemann
authors.affiliation: Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, USA.
authors.forename: Denis
authors.initials: D
authors.lastname: Fourches
mesh_terms: D000465:Algorithms; D000588:Amines / Q000737:chemistry*; D002245:Carbon Dioxide / Q000032:analysis / Q000737:chemistry*; D016000:Cluster Analysis; D016208:Databases, Factual; D000069550:Machine Learning; D008956:Models, Chemical; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D012996:Solutions
chemical_list: D000588:Amines; D012996:Solutions; D002245:Carbon Dioxide
keywords: CO2 recycling; QSPR; amine scrubbing; molecular modeling