| Summary: | Ionic liquids (ILs) have great potential for application in energy storage and conversion devices. They have been identified as promising electrolytes candidates in various battery systems. However, the practical application of many ionic liquids remains limited due to the unfavorable melting points (<i>T<sub>m</sub></i>) which constrain the operating temperatures of the batteries and exhibit unfavorable transport property. To fine tune the <i>T<sub>m</sub></i> of ILs, a systematic study and accurate prediction of <i>T<sub>m</sub></i> of ILs is highly desirable. However, the <i>T<sub>m</sub></i> of an IL can change considerably depending on the molecular structures of the anion and cation and their combination. Thus, a fine control in <i>T<sub>m</sub></i> of ILs can be challenging. In this study, we employed a deep-learning model to predict the <i>T<sub>m</sub></i> of various ILs that consist of different cation and anion classes. Based on this model, a prediction of the melting point of ILs can be made with a reasonably high accuracy, achieving an <i>R</i><sup>2</sup> score of 0.90 with RMSE of ~32 K, and the <i>T<sub>m</sub></i> of ILs are mostly dictated by some important molecular descriptors, which can be used as a set of useful design rules to fine tune the <i>T<sub>m</sub></i> of ILs.
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