Machine learning prediction of CO<sub>2</sub> Henry’s law constant in ionic liquid assisted by graph neural network features

<sec><p indent="0mm">Due to the ongoing climate change, there has been extensive attention drawn to carbon capture and storage technologies, with the focus on applying ionic liquids as absorbents. Ionic liquids are widely regarded as promising in capturing CO₂, because of their unique chemical and physical properties, such as low vapor pressure, high chemical stability, and tunability. Notably, the binary mixtures of ionic liquids often outperform single-component ionic liquids in CO₂ absorption, which is attributed to the synergistic interaction between different ionic liquids. As a critical thermodynamic parameter, the Henry’s law constant (HLC) can be used to describe the solubility of a solute in a solvent under low concentration limits. Regarding CO₂ capture, the magnitude of the HLC is closely associated with the capacity of ionic liquids to absorb CO₂. When HLC decreases, solubility is improved, which means the solvent, particularly in the presence of ionic liquids, can capture a greater amount of CO₂ under a given pressure. Given a significant correlation between this enhanced absorption capacity and the efficiency of CO₂ capture, it is crucial to optimize the performance of ionic liquids in carbon capture. </sec><sec> Usually, it is a lengthy, labor-intensive, and costly process to determine HLC in the conventional ways, whether through experimental techniques or theoretical calculations. Experimental approaches require meticulous procedures and precise measurements, while theoretical calculations necessitate the consumption of extensive computational resources and time. Both methods are resource-intensive, limiting their scalability when applied to a wide range of materials. However, the advancement of machine learning technology makes it possible to predict these constants in an efficient and cost-effective way by using computational models. Trained on relevant data, machine learning models enable the rapid and accurate predictions of HLC, which significantly reduces the time cost and expense incurred by experimental or theoretical determinations. In addition to accelerating the process of identifying promising ionic liquids for CO₂ capture, this shift toward data-driven approaches also enhances the overall efficiency and practicality of carbon capture research and development. </sec><sec> In this study, an innovative framework is proposed to predict the HLC of CO₂ in ionic liquids by combining graph neural networks (GNNs) with machine learning (ML) techniques. Specifically, there are four types of GNNs used to generate molecular descriptors, namely Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Isomorphism Networks (GIN), and Graph Sample and Aggregate Networks (GraphSAGE). These descriptors are integrated with various machine learning algorithms to systematically compare the performance of these combinations in HLC prediction, including K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression Trees (GBRT), and XGBoost (eXtreme Gradient Boosting). According to the research results, the combination of GNNs with tree-based algorithms, such as GBRT, XGBoost, and RF, leads to a significantly better performance relative to the combinations involving KNN and SVR. Among them, the GNN+GBRT model performs best on the training set, while the GNN+XGBoost model performs best in accuracy on the test set. Additionally, the molecular descriptors created by using the GCN model produce a slightly better overall performance than other GNN architectures, which evidences the high capacity of GCNs in processing complex molecular structure data. Therefore, they are applicable to build high-precision predictive models for CO₂ Henry’s law constant. </sec><sec> To sum up, the application of this GNN+ML framework in predicting CO₂ Henry’s law constant plays a vital role in optimizing CO₂ capture technologies. By improving the accuracy and efficiency of predictions, this approach contributes to the advancement of environmental technologies and sustainable engineering. </sec>