Room temperature ionic liquids viscosity prediction from deep-learning models

Abstract

Ionic liquids (ILs) are a new group of novel solvents with great potential in design-synthesis. They are promising electrolyte candidates in energy storage applications, especially in rechargeable batteries. However, in practice, their usage remains limited due to the unfavorable high-viscosity (η) property at ambient conditions. To optimize the design synthesis of ILs, a systematic fundamental study of their structure-property relationship is deemed necessary. In this study, we employed a deep-learning (DL) model to predict the room-temperature viscosity of a wide range of ILs that consist of various cationic and anionic families. Based on this DL model, accurate prediction of IL viscosity can be realized, reaching an R2 score of 0.99 with a root mean square error of ~45 mPa·s. To further help identify low- and high-η ILs, a low/high-η binary classification model with an overall accuracy of 93% for test prediction is obtained based on the DL model. From the important structure-property relationship analysis governed by the top-rank molecular descriptors of this model, a list of very low-η ILs (i.e., η < 30 mPa·s) that could be potentially useful in battery electrolytes is identified. Based on the finding of the DL model, it suggests that in order to achieve low-η, grafting IL cations into smaller sizes (e.g., smaller head rings) and short alkyl chains and reducing ionization potentials/energies will help. Meanwhile, for the same cations, further reducing anions in sizes, chain lengths, and hydrogen bonds might be useful to further reduce the viscosity. Thus, with a fine selection and molecular grafting of anionic and cationic species in ILs, we believe fine-tuning IL viscosities can be achieved through the proper design synthesis of functional groups in ILs.