Machine learning boosted eutectic solvent design for CO2 capture with experimental validation

Abstract

Although eutectic solvents (ESs) have garnered significant attention as promising solvents for carbon dioxide (CO2) capture, systematic studies on discovering novel ESs linking machine learning (ML) and experimental validation are scarce. For the reliable prediction of CO2‐in‐ES solubility, ensemble ML modeling based on random forest and extreme gradient boosting with inputs of COSMO‐RS derived molecular descriptors is rigorously performed, for which an extensive experimental CO2‐in‐ES solubility database of 2438 data points in 162 ESs involving 106 ES systems are collected. With the best‐performing model obtained, the CO2 solubilities of 4735 novel combinations of ES components are first predicted for estimating their potential in CO2 capture. The top‐ranked candidate combinations are subsequently evaluated by examining the environmental health and safety properties of individual components and assessing the potential operating window based on solid–liquid equilibrium (SLE) prediction. Three most promising ES systems are finally retained, which are thoroughly studied by SLE and CO2 absorption experiments.