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.

Keywords

Machine learning / deep learning / ionic liquids / batteries / viscosity

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