# Machine-Learning Model Prediction of Ionic Liquids Melting Points
## 1 Introduction  
With various valuable physicochemical properties (e.g., hydrophobic/hydrophilic, ionic conductivity, thermal stability, etc. ), ionic liquids (ILs) have attracted a lot of attention within the scientific community in various applications, such as energy storage, CO\({}_{2}\) capture, catalysis, lubricant additives, pharmaceuticals, and foods and bioproducts [1, 2, 3, 4, 5, 6, 7, 8]. The main advantage of these novel compounds is that ILs are molten salts which consists of cations and anions with a wide variation of possible combination. Thus, the novel properties of the ILs are therefore determined by the unique molecular structures and interactions between ions. Based on the purpose of applications, the composition of ILs and their properties can be selected and fine-tuned from a large diversity of candidates of inorganic or organic cations and anions [9, 10, 11, 8]. ILs are usually known as \"magic solvents\" due to their high flexibilities in designed synthesis and wide tunable molecular structures and properties. ILs present great potential for energy storage applications, e.g., lithium-ion batteries, lithium-oxygen (Li-O\({}_{2}\)), lithium-sulfur (Li-S) batteries and redox flow batteries (RFBs) [1, 12, 13, 14, 15] as promising supporting electrolyte materials. The solvent viscosity and melting point are critical factors to determine the device's efficiency. However, the practical application of many ionic liquids remains restricted which is attributed to the unfavorable transport property due to the high melting points [1, 16, 17, 15]. To tune the melting point of ILs, a fundamental understanding at the molecular level and a systematic in-depth study of structural properties is needed. For any given chemical compound, the melting point is a determinant character in solid-to-liquid transition, dictated by several important factors, e.g., molecular structures, the configurations of atoms, ions and molecules in a crystalline structures(e.g., symmetry, crystal packing, molecular conformation flexibility), and the important interplay of various interactions (e.g., intra-molecular bonds, electrostatics, van der Waals, hydrogen bonding) among the molecular constituents. Thus, an accurate prediction of the melting point of ILs is highly anticipated and is challenging as well. According to the study of [PERSON] et al. [18], there exist approximately \(10^{18}\) combinations of ions that could lead to useful ionic liquids. However, the wide diversity of IL compounds with various complex structures and physicochemical properties makes a systematic in-depth study on IL compounds extremely challenging. Therefore, there is a need to pursue predictive computational tools to aid the experimental design and synthesis of ILs with desirable properties, such as practically low melting points (\(T_{m}\)). To explore and predict melting points of a wide range of ILs, a comprehensive study adopting a rigorous thermodynamic approach that provides an accurate quantitative prediction with high computational cost [19; 20; 21; 22] in all systems using atomistic or molecular simulation, might not be feasible in practices. To overcome this challenge, a systematic high-throughput screening and in-depth systematic analysis on a vast amount of reported ILs dataset with a specific focus on their physicochemical properties is an important baseline study. An affordable solution, in terms of the present state-of-the-art methodology, is to develop predictive modeling to estimate and predict some important physicochemical properties, such as melting point, viscosity, ionic conductivity of the ILs and their mixtures by utilizing advanced statistical learning methods over sufficiently large datasets available in studies using quantitative structure-property relationships, QSPR; or quantitative structure-activity relationships, QSAR [23; 24].