Estimation of solubility of acid gases in ionic liquids using different machine learning methods

Prediction and Interpretability of Melting Points of Ionic Liquids Using Graph Neural Networks

Ionic liquids (ILs) have wide and promising applications in fields such as chemical engineering, energy, and the environment. However, the melting points (MPs) of ILs are one of the most crucial properties affecting their applications. The MPs of ILs are affected by various factors, and tuning these in a laboratory is time-consuming and costly. Therefore, an accurate and efficient method is required to predict the desired MPs in the design of novel targeted ILs. In this study, three descriptor-based machine learning (DBML) models and eight graph neural network (GNN) models were proposed to predict the MPs of ILs. Fingerprints and molecular graphs were used to represent molecules for the DBML and GNNs, respectively. The GNN models demonstrated performance superior to that of the DBML models. Among all of the examined models, the graph convolutional model exhibited the best performance with high accuracy (root-mean-squared error = 37.06, mean absolute error = 28.79, and correlation coefficient = 0.76). Benefiting from molecular graph representation, we built a GNN-based interpretable model to reveal the atomistic contribution to the MPs of ILs using a data-driven procedure. According to our interpretable model, amino groups, S+, N+, and P+ would increase the MPs of ILs, while the negatively charged halogen atoms, S-, and N- would decrease the MPs of ILs. The results of this study provide new insight into the rapid screening and synthesis of targeted ILs with appropriate MPs.

Microwave Encounters Ionic Liquid: Synergistic Mechanism, Synthesis and Emerging Applications

Progress in microwave (MW) energy application technology has stimulated remarkable advances in manufacturing and high-quality applications of ionic liquids (ILs) that are generally used as novel media in chemical engineering. This Review focuses on an emerging technology via the combination of MW energy and the usage of ILs, termed microwave-assisted ionic liquid (MAIL) technology. In comparison to conventional routes that rely on heat transfer through media, the contactless and unique MW heating exploits the electromagnetic wave-ions interactions to deliver energy to IL molecules, accelerating the process of material synthesis, catalytic reactions, and so on. In addition to the inherent advantages of ILs, including outstanding solubility, and well-tuned thermophysical properties, MAIL technology has exhibited great potential in process intensification to meet the requirement of efficient, economic chemical production. Here we start with an introduction to principles of MW heating, highlighting fundamental mechanisms of MW induced process intensification based on ILs. Next, the synergies of MW energy and ILs employed in materials synthesis, as well as their merits, are documented. The emerging applications of MAIL technologies are summarized in the next sections, involving tumor therapy, organic catalysis, separations, and bioconversions. Finally, the current challenges and future opportunities of this emerging technology are discussed.

Hybrid Improved Grey Wolf Support Vector Regression Algorithm for Modeling Solubilities of APIs in Pure Ionic Liquids: σ-Profile Descriptors

The objective of this study was to model the solubility of active pharmaceutical ingredients (APIs) in different ionic liquids (ILs) based on the σ-moments of cations, anions, and APIs that were used as molecular descriptors calculated using the σ-profiles of three categories of descriptors based on conductor-like screening model for real solvents. The database of 83 API-ILs systems composed of 14 APIs, 12 cations, and 7 anions (25 ILs combinations) was collected as 850 data points at different temperature ranges. A hybrid Improved Grey Wolf Support vector regression, abbreviated as I-GWO-SVR(r), algorithm was selected as the learning method. Based on a comprehensive comparison with 11 different models, various statistical factors, and graphical analyses, including an external validation test, analysis of variance (ANOVA), and sensitivity analysis, the capability and validity of the proposed approach have been assessed and verified. The overall study confirmed that the proposed new model provided the best results in terms of predicting the solubility of APIs in ILs.