Developing Machine Learning Models for Predicting Multiple Physical Properties of Ionic Liquids through a Combined Constitution-Structure-Interaction Descriptor

Ionic liquids (ILs) have garnered significant research interest due to their wide-range applications in separation, catalysis, and synthesis. However, the combination of an extensive quantity of diverse anions and cations makes it a grand challenge for traditional methods (theoretical calculations and experiments) to analyze the properties of unexplored ILs. The emerging machine learning (ML) technique can uncover complex relationships within large data sets. However, the prediction accuracy of ML models is highly dependent on the selection of universal descriptors that are both physically meaningful and broadly applicable. In this work, we designed a composite descriptor incorporating the constitution, structure, and interaction. Then we utilized this descriptor in combination with five conventional ML models to predict six important properties of ILs including density, surface tension, heat capacity, viscosity, electrical conductivity, and thermal conductivity. Detailed comparison results show that the gradient boosting regression tree (GBRT) model with respect to other ML models exhibits superior accuracy and generalizability in predicting all six properties of ILs. The results of feature importance and Shapley additive explanations (SHAP) values further reveal that the structure descriptor in our proposed composite descriptor plays a key role in predicting the properties of ILs. Our study demonstrates that the integration of a constitution-structure-interaction descriptor with an ML model yields remarkable accuracy in predicting properties of ILs, and we anticipate that this can serve as a powerful tool for predicting properties of other materials beyond ILs.