# Machine-Learning Model Prediction of Ionic Liquids Melting Points
## 5 Conclusions  
Ionic liquids (ILs) have great potential for application in energy storage and conversion devices, and they have been identified as promising electrolyte candidates in various batteries systems. However, the practical application of many ionic liquids remains limited due to the unfavorable melting points that constrain the operating temperatures and exhibit unfavorable transport property in batteries. With this as our motivation, we carried out a baseline study to investigate the trend of melting points (\(T_{m}\)) of a wide variety of ILs, with the aim to search for insights that will lead us to fine tune the \(T_{m}\) of ILs using high-throughput screenings of large a ILs dataset and machine-learning model. Based on the dataset (1253 ILs) obtained from an established ILs database, i.e., ILThermo (v2.0) [31; 32], we managed to construct a predictive model to predict the melting points (\(T_{m}\)) of ILs with a reasonably high accuracy, achieving an \(R^{2}\) score of 0.90 with an RMSE of \(\sim\)32 K by utilizing a set of important quantitative structure-property relationship (QSPR) molecular features or descriptors based on the deep-learning (DL) model (Section 3.2). Despite a wide variation in the distribution of melting points and a wide variety of anion-cation combination in the ILs dataset (Section 3.1), we found the melting points \(T_{m}\) of various ILs can be determined based on a limited set of molecular descriptors. These molecular descriptors consist of 137 descriptors that highlight several important molecular features that have significant influence in determining the melting points of ILs, e.g., the presence of electronegative constituents, geometrical structures, branching and shapes, hydrogen-bonding ability, polarizabilities, etc. (Section 3.3). Based on the DL model, the important interplay dictated by the structural properties of the cations and anions of ILs that determine the distribution of \(T_{m}\) can be seen (Section 3.3). For example, the high \(T_{m}\) of ILs tends to favor small values of shape indices which measure the relative cyclicity of ILs molecules that could relate to cyclic compounds commonly found in ILs cationic counterparts (e.g., imidazolium, pyridinium, phosphonium, etc.). We elucidated the effects of anionic counterparts by incorporating appropriate anionic species with a more localized negative charge, and stronger hydrogen bonding in intermolecular interaction, which can lead to an increasing melting point for ILs (Section 3.3). Thus, with a fine selection of anion-cation combination in ILs, we believe that the design and engineering of functional groups is the key to fine tune the melting points, and further studies in the development of predictive models that are able to accurately predict other physicochemical properties (e.g., viscosity, hydrophilicity/hydrophobicity, conductivity) relevant to battery application will be conducted. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12052408/s1](https://www.mdpi.com/article/10.3390/app12052408/s1)]([https://www.mdpi.com/article/10.3390/app12052408/s1](https://www.mdpi.com/article/10.3390/app12052408/s1)), Table S1: The performance metrics of DL-model that applied to different testing sets which based on 137 molecular features/descriptors. N is the sample size of the dataset. For RMSE, the unit is in Kelvin (K); Figure S1: DL model predicted melting point (i.e., \(T_{m}\) (_Prediction_)/K) values (in blue color) of the entire ILs dataset versus the experimental measured melting point (i.e., \(T_{m}\) (_Exp_)/K) (in red color) obtained from the literatures (i.e., ILThermo database); Figure S2: A correlation matrix comparing the melting point (\(T_{m}\)) with top 20 important molecular descriptors based on the Pearson correlation method.